WO2024256941A1 - Systèmes et procédés de détection d'eau dans un mélange - Google Patents

Systèmes et procédés de détection d'eau dans un mélange Download PDF

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Publication number
WO2024256941A1
WO2024256941A1 PCT/IB2024/055619 IB2024055619W WO2024256941A1 WO 2024256941 A1 WO2024256941 A1 WO 2024256941A1 IB 2024055619 W IB2024055619 W IB 2024055619W WO 2024256941 A1 WO2024256941 A1 WO 2024256941A1
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WIPO (PCT)
Prior art keywords
water content
sensor
aperture
electrical parameter
signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2024/055619
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English (en)
Inventor
John A. MERCHANT
Darman M. ROCK
David M. Rudek
Knut Schumacher
Won Joon Choi
Aline Serrao DE FILIPPO
Samuel J. CARPENTER
Alissa P. WENNER
Thomas E. S. MUEHLE
Hans Emanuel GOLLNICK
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3M Innovative Properties Co
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3M Innovative Properties Co
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Application filed by 3M Innovative Properties Co filed Critical 3M Innovative Properties Co
Priority to KR1020257042727A priority Critical patent/KR20260018875A/ko
Priority to EP24733762.9A priority patent/EP4724799A1/fr
Priority to CN202480038937.XA priority patent/CN121285736A/zh
Publication of WO2024256941A1 publication Critical patent/WO2024256941A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/44—Resins; Plastics; Rubber; Leather
    • G01N33/442—Resins; Plastics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/26—Oils; Viscous liquids; Paints; Inks
    • G01N33/32—Paints; Inks

Definitions

  • a sensing system for a mixture that includes a sensing zone containing a mixture.
  • a sensor is inside the sensing zone that includes a printed circuit board (PCB), a transmitting electrode configured to generate an electrical field, and a receiving electrode.
  • the transmitting and receiving electrodes are configured to directly contact the mixture.
  • the sensor is configured to sense an electrical parameter value for the mixture.
  • the system also includes a signal analyzer that, based on the detected electrical parameter, determines a water content of the mixture.
  • FIGS. 1A-1B illustrate systems and devices that may benefit from embodiments herein.
  • Suitable functional materials usable in 3D electronics printing techniques may include conductive inks, dielectric inks, hybrid inks, or other functional inks.
  • conductive inks may be used to print conductive traces, electrodes, and interconnects.
  • Conductive inks generally include conductive materials, such as silver nanoparticles, graphene, or nanotubes, dispersed in a liquid medium.
  • Dielectric inks may be used to print electrically insulating structures.
  • Dielectric inks may include polymers or ceramics, dispersed in a liquid medium.
  • Hybrid inks may combine more than one functionality into a single ink formulation.
  • Property sensors as described herein may be used to sense properties of a fluid resulting from a mixing process.
  • ⁇ may also be used to sense properties of input fluids for a mixing process or for an industrial manufacturing process.
  • separate property sensors for respective input fluids are placed just in front of the mixer. Data from these property sensors measuring the input fluids can be processed along with data from a property sensor measuring the mixed fluid, e.g. in an integrated materials property monitoring system.
  • a property of each of the three fluids before mixing can be determined using three property sensors at the respective outlets of the three containers containing the three input fluids. This may help in quality control and reduce waste that might otherwise occur due to one of the input fluids being outside a specification for the property.
  • Property sensors herein may also be used to sense properties of fluids in bulk, or while fluid is flowing past, or through, a sensor. Sensors described herein may determine various properties of a fluid, like, for example, mixing ratio of a two-component adhesive or curing status of a curable composition or age of a material. Sensors herein may also detect water content in a mixture, and determine when and how water content varies. The number of properties which were varied previously to establish the set of calibration data representing calibration impedance responses measured previously at the different property values determines the number of properties that can later be determined by the property sensor.
  • one property of interest is a mixing ratio of two or more components of the fluid.
  • the fluid is a two-component adhesive, and a property of the fluid is a mixing ratio of the components.
  • a property of interest is a curing degree or a curing status.
  • the fluid is a curable composition, and a property of the fluid is the degree of curing of the composition.
  • Systems and methods herein may be particularly useful for determining whether or not, and how much, water is present in a mixture.
  • water contamination in monomers can cause a number of defects and performance issues for acrylate -based adhesives.
  • water content in a premix for an optically clear adhesive above a certain threshold can cause gelling defects. Gelling defects can render an optically clear adhesives useless, with defects that are hard to detect. Water contamination, therefore, is a large cost and waste driver in the production of optically-clear adhesives. Water also decreases the production efficiency of acrylate-based adhesives.
  • water present in the bulk mixture can change the material properties - e.g. cure degree and / or mechanical performance. Knowing a water content at a dispensing time allows for changes to be made during processing to make the use of the adhesive more consistent and predictable.
  • Water content is currently measured off a manufacturing line using Kari Fisher, IR or gas chromatography.
  • Systems and methods are desired that can measure water content in-line, in real-time or substantially real-time, so that defects occurrences are reduced and productivity increased.
  • real-time refers to data is processed within milliseconds so that it is available virtually immediately as feedback. While some delay due to processing are inevitable, “real-time” is intended to cover systems and methods where data can be collected or entered and a user can then interact with it without noticeable delay. E.g. a user may make a data entry into a system, and the data entry is then substantially immediately available for viewing or editing.
  • Systems herein measure an electrical property (e.g. impedance, conductivity, and / or dielectric constant) of a liquid in contact with, or moving through, a sensor. Electrical property values will change as the percent water content changes. A change in signal response as the percentage of water changes. The changes in signal response are also a function of an input voltage frequency. Temperature also impacts electrical property values and may also be measured in some embodiments herein. Mix ratio of a mixture also affects electrical property values.
  • an electrical property e.g. impedance, conductivity, and / or dielectric constant
  • curing is intended to broadly cover a changing of a material from a first state to a second state. For example, some liquids cure into solids. Some mixtures may experience crosslinking. Some mixtures may experience prepolymerization. Some mixtures may experience conversion. Detecting these and other similar state changes are expressly contemplated for embodiments herein.
  • Sensors herein may be used for inline operation or in a bypass loop. Sensors herein may have, or communicably couple to an interface with programmable logic controllers, processing circuitry, or other computing device. Measurement modes for sensors herein may rely on controlled bi-frequency permittivity and / or conductivity detection. Sensors herein may have electrodes that directly contact a mixture using a surface contact or a flow through architecture.
  • FIGS. 2-3 illustrate electrical parameter sensors in accordance with embodiments herein.
  • FIG. 2A illustrates an extended sensor in accordance with embodiments herein.
  • Sensor 200 is illustrated in FIG. 2A as having a length 230 of separation between an electrode portion 220 and edge connector 210.
  • Edge connector 210 should not come into contact with a mixture. Therefore, having a separation 230 in between edge connector 210 and electrode portion 220 increase the flexibility of use for a conductivity sensor - for example allowing sensor 200 to be used in a deeper container to ensure consistency throughout the depth of the container.
  • Sensor 200 can be dipped and used to stir the sensor inside a mixture without the edge connector to touch fluid (and short circuit), allowing for material characterization data (conductivity, temperature and dielectric constant) to be monitored and visualized in real time.
  • FIG. 2B illustrates a sensor configuration 240, where electrode portion 220 is communicably coupled to a sensor reader 250, for example using edge connector 210.
  • Sensor reader 250 may couple to a suitable computing system such that signals from sensor 220 can be analyzed in real-time or substantially real-time.
  • Systems and methods herein may be used to sense electrical parameter values in a number of applications, such as adhesive dispensers, spray guns, bulk material holding tanks, pump systems, etc.
  • FIGS. 3A-3B illustrate material measurement flow sensors in accordance with embodiments herein.
  • FIG. 3A illustrates a PCB material measurement flow sensor 300.
  • a sensing system 300 includes a PCB board 302 with one or more grounds 330 and a TX contact 440.
  • the TX contact provides a transmitting signal to each transmitting electrode 310.
  • RX contacts located on the reverse side of the PCB, receive the indication of a sensed impedance from each of the electrode pairs.
  • the electrical potential of each receiving electrode 320 is electronically regulated to ground potential separately.
  • the regulator action for each receiving electrode in some embodiments, is interpreted as an impedance signal for each electrode pair.
  • four separate measurements channels can provide information, each through its own TX contact 340 and RX contact (not shown).
  • a sensing system 300 has four electrode pairs, with four transmitting electrodes 310, each paired with one of four receiving electrodes 320. However, it is expressly contemplated that more, or fewer, electrode pairs may be present, depending on available area on a PCB board and sensing needs.
  • Sensing system 300 is placed, in some embodiments, perpendicularly to the flow of material, such that a first sensing area 352 receives a first portion of material flow, a second sensing area 354 receives a second portion of material flow, a third sensing area 356 receives a third portion of material flow, and a fourth sensing area 358 receives a fourth portion of material flow. Therefore, system 300 can simultaneously generate four different signals relative to a single material flow, providing a better picture of whether a mixing ratio (or other measured parameter) is consistent across an entire sensing area.
  • system 300 allows for four measurements to be taken simultaneously with a single PCB. It also provides a larger surface area for material flow, through a shorter sensor distance.
  • FIG. 3A illustrates an embodiment where each electrode pair is part of a slot 352, 354, 356, 358.
  • a sensing area may include a pair of electrodes on a protrusion, or within an aperture, in a “comb”-like structure.
  • both ends may be closed from a structural standpoint, especially with viscous fluids.
  • the electrodes 310, 320 may be formed by metallization on the interior surface of slides 352, 354, 356, 358, using copper for example.
  • the metallization process may cause electrodes 320 to be connected to electrodes 410. Therefore, a decoupling or disconnecting step is needed. This can be done by breaking the connection, for example by drilling a hole in the positions 350A and 350B as illustrated, by punching out a perforated component, milling, nibbling, etching, laser cutting or another suitable method.
  • Systems and methods herein may be used for a variety of materials being dispensed.
  • PCB boards often have a maximum operating temperature less than 170° C, which limits the temperature of materials that can be dispensed through a sensor system 300.
  • Materials may have a range of viscosities, for example up to around 10 5 Pa s. Higher viscosity might result in a dispensing pressure being insufficient to force the material through slots 352-358 without breaking the sensor. However, higher viscosity materials may be accommodated by increasing the width of slots 352-358. However, sensing system 300 may be less sensitive. Similarly, for materials with particulates, such as suspensions for example, particle sizes have to be smaller than the width of slots 352-358. Additionally, systems herein may be limited to solvents that do not cause corrosion or otherwise damage the PCB 302 or electrodes 310, 320.
  • FIG. 3B illustrates another embodiment of a sensing system 360, which includes a built-in temperature sensor 370.
  • Temperature sensor 370 sits within a slot with a connection point 372 for a ground signal and a connection point 374 for a temperature signal.
  • Ground signal connection point 372 connects to a ground signal communicator 382.
  • Temperature signal communication point 374 connects to a temperature signal communicator 376.
  • four impedance or conductivity sensor slots 380 are also present, each connected to a ground signal 382. However, it is noted that two different spacings between slots are present in the embodiment of FIG. 3B.
  • a first spacing, 362 is present between a first and second slot 380, and between a third and fourth slot 380, while a second spacing 364 is present between second and third slots 380.
  • Increased spacing 364 may provide improved shielding against interference between electromagnetic fields generated by each electrode pair.
  • a temperature sensor is sealed within a housing, which keeps it isolated from the material.
  • the seal layer may be a layer of varnish, for example, which may allow for the thermal contact to be improved relative to other housing materials.
  • the temperature sensor connects via contacts 382 on the edge connector.
  • FIGS. 3A-3B illustrates an embodiment where slots 352-358, 370 and 380 are ovular in shape, with a generally straight body and rounded ends.
  • Electrodes 310, 320 may be curved, for example, or otherwise shaped to accommodate an available volume of a dispensing system.
  • FIGS. 3A-3B illustrate sensors that may be used in accordance with embodiments herein. However, it is expressly contemplated that other sensor configurations may be suitable. For example, Other example configurations are described in Provisional US Patent Application having Serial Number 63/486631, filed on Feb. 23, 2023. Other example configurations are also illustrated in FIGS. 9-11, 13-14, 16-18 of Provisional US Patent Application 63/507662, filed herewith. FIGS. 9-11, 13-14 and 16-18 of said application, along with the accompanying description, are incorporated by reference herein.
  • FIGS. 4A-4D illustrate material measurement flow sensors as used in accordance with embodiments herein.
  • sensors are used in an in-line configuration such that fluid flows through a sensor before being dispensed.
  • FIG. 4A illustrates a sensor 410 within conduit 400.
  • Sensor 410 has four electrode slots such that, as a mixture passes through conduit (into the field), it is forced through the slots of sensor 410, and electrical parameter measurements are passed to a control system, by edge connector 412, for example. Variation in the conductivity measurements between one electrode slot and another electrode slot indicates may indicate a variation in quality consistency of the mixture.
  • FIG. 4B illustrates a perspective view 400 of a conduit 422.
  • Conduit 422 may couple to another part of a dispensing system or a fluid transport system.
  • Conduit 422 may couple to another part of a fluid flow system using threading 426, or another suitable fastening system.
  • FIGS. 4C and 4D illustrate cutaway views of a conduit.
  • an over-molded plastic 444, 440, 460 is used as a seal to hold a PCB sensor in face.
  • Such a seal may have an end stop to confirm the sensor is in place.
  • other seal options, and position confirmation options e.g. a snap or clip
  • the illustrated seal may include barbs to maintain a connection.
  • FIG. 4D a different seal configuration is illustrated - an O-ring can be used.
  • Corresponding recesses that can receive an O-ring 464 may be machined into the conduit to stabilize the sensor against the pressure of fluid flow.
  • the illustrated conduit may be replaceable, such that a sensing assembly is a singleuse assembly, in some embodiments.
  • the sensor is removeable such that the PCB sensor is a single-use sensor.
  • FIGS. 4A-4D concern a PCB-based impedance sensorthat can be attached to a static mixer, using an adapter or other connection mechanism, providing mix ratio information in real time.
  • the use of an adapter that can receive a PCB unit allows for compatibility of the PCB sensor with a number of dispensing systems.
  • FIGS. 5A-5C illustrate a sensor configuration that may be particularly useful to detecting bubbles or aggregation of droplets of a second phase forming prior to a phase separation.
  • FIG. 5A illustrates a dip sensor that may be particularly useful for detecting air bubbles or droplets in a mixture.
  • Sensor includes four electrode pairs in four slots, 502, 504, 506 and 508, that are different sizes.
  • Slot 502 is wider than slot 504, which is wider than slot 506, which is wider than 508.
  • Slots 502-508 are illustrated in an arrangement from thickest to thinnest, however it is expressly contemplated that other arrangements are possible.
  • four coplanar electrode pairs are illustrated, all substantially the same distance from edge connector 514.
  • Slots 502-508 are designed to both detect bubbles or droplets and provide an indication of size. Generally, a consistent mixture with no bubbles or droplets provides an insulation effect, and maintain a consistent conductivity across all electrode pairs. When a droplet reaches a width of one of the slots, the droplet will connect both sides of the electrodes, resulting in a detectable change in conductivity.
  • a thinnest slot may be as thin as 100 pm, or thinner than 150 pm, or thinner than 200 pm, or thinner than 300 pm, or thinner than 400 pm.
  • One or more slots may be thinner than 500 pm.
  • One or more slots may be thinner than 1 mm.
  • a stir stick being used in a larger measurement operation, such as checking a mixture quality of a 50 -gallon drum.
  • the slots may also change in length to suite a particular application.
  • the overall sensor may need to be much longer - for example up to, or over, 1 meter in length.
  • apertures must be larger - both to increase signal strength and to allow for significant flowthrough.
  • a length may be increased to increase signal strength, balanced with a width selected to allow flowthrough without sacrificing signal strength.
  • a grid of electrode pairs may be useful to detect consistency at multiple depths as well as mixing quality (or the presence of droplets / bubbles) simultaneously.
  • embodiments herein illustrate sets of four electrode pairs in different configurations, it is expressly contemplated that more, or fewer, electrode pairs may be present in any vertical or horizontal arrangement.
  • FIGS. 6A-6B illustrate electrical signal values and water content values measured using systems herein for commercialized product DP460 Epoxy, available from 3M® Company located in Minnesota. Using signals received from sensors described herein, a water content of a mixture can be calculated using a machine learning algorithm trained on data obtained from mixtures with known water content percentages. Illustrated in FIG. 6A is a graph 600 of conductivity over time along with measured water content over the same time . Conductivity signals 610 and water content 620 are recorded by an electrical parameter sensor as a dispensing operation is ongoing.
  • the sensor may be in-line in a dispenser, measuring a source of material being delivered to a dispenser, or in another suitable position such that water content 620 is measured, at a time sufficiently proximate a dispensing time, in time to take necessary action to prevent defects.
  • FIG. 6B illustrates a graph 650 of conductivity 660 and water content 670 measured over time, taken on a different day than that of FIG. 6A. It is seen, in the comparison between FIGS. 6A and 6B, that the conductivity curve shape remains the same, but the water content has dropped. The water content amount remains consistent over the dispensing operation in both FIGS. 6A and 6B. As illustrated in FIGS. 6A-6B, systems and methods herein can accurately calculate a water content of a mixture.
  • Systems and methods herein utilize a machine learning model trained using known data from dispensing operations. Previous methods attempting to fit data using multi-step curve fitting methods analytically, without machine learning, has been unsuccessful because of the number of variables involved. Similarly, making corrections individually for different variables - e.g. for temperature or mix ratio or dielectric constant - compared to conductivity has also not been successful because of the steps involved and precision required.
  • the machine learning model is trained using data for a single mixture type - e.g. such that a model is specific to data for DP460, and a different model is used for a second material, etc.
  • FIGS. 7A-7B illustrate mix ratio and water content predictions generated by systems and methods described herein.
  • FIGS. 7A-7B illustrate predicted mix ratio, in graph 700, and water content, in graph 750. While mix ratio and water content are illustrated in FIGS. 7A and 7B, it is expressly contemplated that systems and methods herein may also be useful for calculating other material properties, and for other materials.
  • FIG. 8 illustrates a method of quality controlling a material dispensing system in accordance with embodiments herein.
  • Method 800 may be used with the dispensers as described herein, in a bulk material characterization, in a spray or coating system, or another suitable application.
  • the sensing area may be a material dispenser, a transport line to a material dispenser, before a nozzle, atomizer, or other transportation mechanism or container within a fluid system.
  • the one or more components may include a premix, a component to be mixed, a mixture, or a bulk material.
  • a material dispenser may dispense a liquid 812, particles 814 either in suspension or otherwise.
  • the material may also be a mixture 816 of materials, for example an emulsion or another A and B component mixture. An emulsion must be dispensed as a stable emulsion, and reactive A:B components should be provided at a desired mix ratio.
  • Other components 818 may also be provided to a sensing area for characterization and / or quality control.
  • the one or more components contacts a sensing system.
  • Contacting a sensing system may include passing through a sensing system, e.g. through one or more apertures on a PCB, before being dispensed, stored, removed from storage.
  • Contacting a sensing system may also include the mixture contacting a surface of a sensor, the surface being part of a PCB.
  • direct contact between a material and an electrode pair ensures accurate measurements.
  • the sensing system may be a bulk sensing system or a surface sensing system.
  • the sensing system may receive signals from one, two, three, four or more discrete electrode pairs.
  • the sensing system may have multiple sensors, for example a plurality of electrode pairs that, when a sufficient voltage is passed through them, detects an electrical parameter of the material. Based on the sensor signals, a number of things may be determined for the material. For a mixture, a mixing ratio may be determined. For a curable material, a curing progressing may be detected. A water content for the material may be determined. Aging may also be detectable, as well as differences between batches of materials. Instability indications - such as entrained air, impending phase separation, contamination, etc. may also be detectable. Conductivity measurements may be taken serially, for example one signal received every second, or more frequently. Conductivity measurements may also be taken in parallel, for example from each of a plurality of electrode pairs. The electrode pairs may be coplanar with each other, in some embodiments.
  • Feedback is provided based on the conductivity measurements.
  • Feedback may include characterization of the material, as indicated in block 832. For example, a mix ratio may be detected, entrained air or single component fluid pockets, an age indication or other parameter of interest may be calculated and provided.
  • a prediction may also be provided, as indicated in block 834. For example, based on a trend of previous conductivity sensor readings, it may be possible to predict future behavior of the material being measured.
  • Other characterization information 838 may also be provided. For example, a conductivity reading trending in one direction may indicate that a mix ratio is moving toward an edge of an acceptable range and, therefore, that a mix rate should be changed, or that an increase in instability is trending toward phase separation. Similarly, a conductivity reading may indicate that a curable component is curing.
  • Feedback may also indicate corrective action is needed. For example, a water content may be detected, as illustrated in block 842. Based on a water content percentage, a corrective action or parameter adjustment can be made. Feedback may also include an alert, as indicated in block 844, e.g. that a detected water content is too high, too low, or indicates that corrective action is needed. Feedback may also include indicating that a purge of one component, multiple components, or a mixture, is needed. Other feedback may be p In embodiments where a material has corrosive effects, or cures over time, predictive feedback may provide an indication that the sensor needs to be replaced, as indicated in block 846. Other predictive information may also be provided, as indicated in block 838, that may trigger other actions, as indicated in block 848.
  • providing feedback may also include providing electrical parameter readings, material characterizations or predictions to a user or controller of a dispenser, or other useful information such as material source, batch number, material name, dispensing temperature, dispensing pressure, material concentration(s), mix ratio, or any other information.
  • Sensed or calculated parameter values may also be provided to a storage or historical data repository for later retrieval.
  • FIG. 9 illustrates a quality control system, in accordance with embodiments herein.
  • Quality control system 950 may be used to identify and correct a detected inconsistency in a mixture.
  • Quality control system 950 may be implemented in a static environment - e.g. as a dip stick or other analysis tool for a contained fluid - or a dynamic environment - e.g. in a fluid flow conduit where fluid moves through, or past, electrode pairs.
  • Base levels may be important to measure to have a more accurate relative threshold. For example, if a conductivity measurement drops below a proportionate factor to the base level (e.g. to 50% of the base level) then it can be determined that an inconsistency is present - e.g. a concentration gradient indicative of poor mixing, droplets indicative of phase separation, or entrained air. Relative thresholds may be helpful to reduce waste of material on accidental purges or wasted time in attempting to correct an inconsistency that may not be present or may not be at a level that requires correction. Similarly, relative thresholds may be useful for determining whether a detected water content, or mix ratio, is too high or too low, or is changing too quickly.
  • Quality control system 950 may be implemented by a suitable computing device in communication with a sensing system 910 - e.g. a compact signal receiving and analysis system as described in FIGS. 14A-D below, or a more traditional set up with signals being directed to a computing system using multiple cables. While quality control system 950 is illustrated as incorporating multiple functionalities within a single architecture 950. However, it is expressly contemplated that the illustrated functionalities may be performed by separate processing devices, processors or processing circuitry in multiple locations.
  • Sensing system 910 may be any suitable sensing system that senses an electrical parameter by direct contact with a material. Sensing system 910 may include one or more electrode pairs 912 in direct contact with a material flow. Sensing system 910 may also include a temperature sensor 914. Electrode pairs 912 may be part of a printed circuit board, for example, formed within apertures machined or built into the printed circuit board. The apertures may be closed on both ends, or open on one end, in a comb-like structure, for example. Temperature sensor 914 may be shielded from direct contact with a material flow, in some embodiments. Sensing system 910 may include other features 916.
  • Sensor signals from sensing system 910 are received by quality control system 950 using an active signal retriever 952.
  • Active signal retriever 952 may receive signals from sensing system 910 periodically or continuously during an operation.
  • Received sensor signals may be an electrical parameter value relevant to a use case, including, but not limited to: impedance signals, conductivity signals, dielectric constant signals, or a combination thereof. In embodiments where a conductivity value is used to detect an inconsistency.
  • a historic signal retriever 954 may communicate with a data store to retrieve previously captured signal values, or to send captured signals as they are captured.
  • Historic signal values of interest may include signal values retrieved in a recent period of time, from the same batch or mixture of materials. For example, values retrieved over a previous number of seconds or minutes may be important. In some embodiments, signal values may drift over longer periods of time due to changes in temperature, material aging, mixture ratio fluctuations, etc. But inconsistencies may be detectable as a rapid change in conductivity or a divergence of conductivity measurements in a sensing system from each other.
  • a threshold can be retrieved that dictates when corrective action should be taken. For example, a time or amount of cure that indicates a purge should be taken, a range for mix ratio that is acceptable, an amount of water that is acceptable for a mixture, etc.
  • Threshold generator 960 in some embodiments, generates a relative threshold either periodically or continuously, based on historic signals.
  • the relative threshold may be an absolute value, for example specifying that an increase or decrease of X% over Y time indicates an inconsistency. If received signal values have fluctuated more significantly, the threshold change value may be larger, while if conductivity values have not fluctuated significantly, the threshold change value may be smaller.
  • Signal analyzer 962 compares the received signal, or calculated conductivity, to the threshold and, if a deviation outside the allowed threshold is detected, command generator 964 generates a command, which is communicated, using command communicator 966.
  • Analyzing the signal may include a mix ratio calculator 956 that, based on the received electrical parameter signal, calculates a mix ratio of a material. Analyzing the signal may include a water content calculator 958 calculating a water content of a material.
  • feedback may be generated by feedback generator 972.
  • Feedback may include an alert, a received value, or other communication.
  • a feedback communicator 974 may communicate feedback.
  • a command or feedback may be communicated to a device 920 may, in some embodiments, include a display component, and the generated command may be an update to a graphical user interface, using graphical user interface generator 980, and presented on the display component.
  • Device 920 may, in some embodiments, include a feedback component, such as audio, visual or haptic feedback that indicates to a controller that an air bubble is detected.
  • Device 920 may also be a correction mechanism, and command generator 964 may generate a command to conduct a correction mechanism selected based on the detected inconsistency, e.g. a purge valve, a re-mixing command, a degassing command, etc.
  • System 950 may include other features 976.
  • Measuring electronic parameter values can be used to determine a current mix ratio and / or a current water content for a material. However, it is expressly contemplated that systems and methods herein may also determine other information about a material. For example, as described herein, and in the Examples Section of PCT/US2022/52343, conductivity measurements may be used for determining consistency issues due to lot-to-lot variation, entrained air, droplet formation, aging, concentration gradients, dispersion separation or emulsion separation.
  • Described herein thus far are sensor systems that are based on a single PCB board. Such systems are relatively inexpensive and, therefore, cost effective to use and replace.
  • one disadvantage of designs described thus far is the large stray field compared to the main field present between each electrode pairs.
  • the stray field effect is caused by the short distance between material flow input and output, e.g. the thickness of the PCB.
  • One way to reduce the stray field effect is to solder multiple PCBs, each with electrodecontaining apertures, into a PCB stack.
  • FIGS. 10A-10B illustrate stacked electrical parameter sensors in accordance with embodiments herein.
  • FIG. 10A illustrates a stacked PCB sensor as described in FIG. 10B of U.S. Provisional Patent Serial No. 63/386715, filed December 9, 2022 and Ser. No. 63/486631, filed February 23, 2023, .
  • FIG. 10B illustrates a stacked PCB sensor of PCB boards similar to FIGS. 5A-5C.
  • sensor stack 1000 may include four PCB sensors, with one 4-layer PCB 1010, two stacking PCBs 1020, which are provided to get the required sensitivity by increasing the electrode surface area, and a top PCB 1030.
  • flow through sensor stack 1000 is indicated by arrow 1040.
  • FIGS. 10A-10B both illustrate a four-layer sensor stack, it is expressly contemplated that fewer, or more PCB sensors, may be coupled together. For example, as few as two PCBs or as many as five, six, seven, eight, nine, ten or more PCBs.
  • Stacked sensor 1000 provides the benefits of a single PCB sensor with reduced stray field effects.
  • the compact design also improves the shielding of the sensitive electrodes and may also be used as an electrode cartridge without needing additional housing as the sensitive area can be internally sealed.
  • the sensitive area is internally sealed by soldering, and can withstand applied pressure from a material sensor without requiring an additional housing.
  • stacked sensor 1000 can utilize smaller electrodes, allowing for sensor stack 1000 to be integrated into an active or passive mixing nozzle at the material inputs as well as the material output.
  • a sensor stack only one electrode 1010 with an edge connector configured to connect to lead wires.
  • stacked sensors may include a temperature sensor and may include an elongated portion for embodiments where a stir stick is an appropriate vehicle for detecting conductivity.
  • a stacked sensor of either configuration can, without the elongated portion, be suitable for placement in a conduit, such as that illustrated in FIGS. 4A-4D.
  • FIGS. 11A-11B illustrate a sensor in accordance with embodiments herein. It is illustrated herein that a number of electrode slots may be organized in a row, such that each slot is roughly the same distance from an edge connector. It is also illustrated herein that a number of electrode slots may be organized in a column, such that each slot has a different distance from an edge connector. It is also expressly contemplated that, in some embodiments, electrode slots may be organized in both rows and columns.
  • FIG. 11A illustrates a sensing setup 1100, with a sensor 1110 partially submerged in a solution 1120.
  • Sensor 1110 includes electrode slots of a first size 1102 and a second size 1104. Electrode slots are arranged in both rows 1108 and columns 1108. Arranging electrode slots in both rows and columns provides additional insight into a material.
  • FIG. 11 A illustrates a solution 1120 that is homogeneous
  • FIG. 1 IB illustrates a solution 1150 that has experienced settling, which may be a sign of material age.
  • Sensor 1140 may provide twelve different sensor signals for analysis, one from each electrode pair through which material can flow.
  • a difference between signals from electrode slots 1142 and 1144 may indicate aging.
  • a difference between a signals from electrode slots 1144 and 1146 may indicate a viscosity of solution 1150.
  • Electrode slots with smaller widths may not handle higher viscosity materials well, while electrode slots with wider widths may not be as precise for low-viscosity materials.
  • Sensors 1110, 1140 may handle a wider range of viscosities while also providing signals along a depth of a material container. While only four rows of electrode pairs are illustrated, it is expressly contemplated that more rows may be present in other embodiments, to suit a container depth. Additionally, while only three columns are illustrated, it is expressly contemplated that additional columns with wider or narrower electrode slots are also possible.
  • Electrode 1144 for example, which is at a lowest depth measures a first conductivity.
  • Electrode pair 1152 at a different depth, measures a second conductivity. Because some settling or separation has occurred, the received electrical parameter values will differ.
  • FIGS. 11A and 11B illustrate a sensor 1100 with twelve electric pairs arranged in a grid on a PCB, it is expressly contemplated that a different number of electrode pairs may be present, for example more than four rows, e.g. 5, or 6, or 8, or 10 or more, and more than three columns, e.g. 4, or 5, or 6, or 8, or 10. Additionally, the spacing present between electrodes may be longer or shorter than that illustrated.
  • FIGS. 12A-B illustrates another embodiment of a system in which embodiments herein may be useful.
  • FIG. 12A specifically shows that a sensing system 1210 can be located at a remote server location 1202. Therefore, computing device 1220 accesses those systems through remote server location 1202.
  • User 1250 can use computing device 1220 to access user interfaces 1222 as well.
  • a user 1250 may interact with an application on the user interface 1222 of their smartphone 1220, or laptop 1220, or other computing device 1220 to receive information from a dispensing system or a quality control system.
  • FIG. 12A shows that it is also contemplated that some elements of systems described herein are disposed at remote server location 1202 while others are not.
  • data stores 1230, 1240 and / or 1260 can be disposed at a location separate from location 2002 and accessed through the remote server at location 1202. Regardless of where they are located, they can be accessed directly by computing device 1220, through a network (either a wide area network or a local area network), hosted at a remote site by a service, provided as a service, or accessed by a connection service that resides in a remote location.
  • the data can be stored in substantially any location and intermittently accessed by, or forwarded to, interested parties.
  • physical carriers can be used instead of, or in addition to, electromagnetic wave carriers. This may allow a user 1250 to interact with system 1210 through their computing device 1260, to initiate a seal check process.
  • a conductivity measurement system may be any suitable system configured to, using systems and methods herein, collect conductivity measurements, conduct analysis and provide the analysis to a receiving device, storage or graphical user interface generator.
  • FIG. 19 of PCT7US2022/52343, describes operation of such a system and is hereby incorporated by reference.
  • System 1210 receives conductivity measurements from one or more sensors 1270.
  • Each sensor may include one or more pairs of electrodes on a PCB.
  • the electrodes may be coplanar and spaced similarly away from one end of the PCB, in some embodiments, or may be coplanar and in line with a length of the PCB.
  • Sensors may be formed either by metallization or another process.
  • Sensors 1270 are decoupled from each other such that independent conductivity signals are received from each sensor.
  • Sensors 1270 may each include a positive and negative electrode, decoupled from one another.
  • Electric parameter measurement system 1210 may receive a sensor signal as a conductivity signal or a dielectric constant signal, but may also be received as an impedance signal.
  • a received signal is an impedance signal
  • a conductivity value may be calculated based on the impedance signal
  • a dielectric constant may be calculated based on a received impedance signal.
  • calculations and / or predictions may be undertaken, as described in FIG. 12, for example.
  • a mixing ratio may be calculated based on calibration data, stored in a datastore 1260, which may be indicative of conductivity data from pure components and / or known mixtures of components.
  • sensors may be placed at both the inlets and outlet of a sensing zone and, therefore, system 1210 may receive sensor signals from all sensors associated with a material dispensing system.
  • System 1210 may be configured to correct for the time delay between sensor signal capture and analysis, in some embodiments. In other embodiments, where trend information is particularly relevant, correction may not be needed.
  • machine learning models may receive information from multiple systems, such as multiple sensors within a dispensing system including conductivity sensors, temperature sensors, motor speed signals, material information, etc.
  • multiple machine learning models are used simultaneously, each by an individual system such that each system’s model can learn and the overall model can be improved.
  • non-machine learning models may also be used.
  • Sensing systems herein are described as having the functionality of receiving and sending communicable information to and from other devices. This may be done through an application program interface, for example, such that system 1210 can receive and communicate with pump controllers, line pressure sensors, movement controllers for portions of dispensing system, temperature sensors, heating elements, datastores having information for any of the materials being dispensed or the mixture being generated, etc.
  • datastore may also include an analyzer that learns usage behavior of a particular dispensing system in order to improve operation and predictions.
  • frequency and patterns of dispensing may provide information about curing and improve mixing models. For example, usage data such as frequency of dispense, purging frequency, pattern of dispense, change out of the sensor, etc., can be collected and used to train a model to more accurately predict trends and provide corrective action.
  • display 1260 may display a GUI created by generator 1220 that is updated periodically with information collected by system 1210 and / or any of datastores 1230-1260.
  • Information may be passively updated or provided with an alert or notification as it is updated, for example current status information may be presented and an alert (visual, audio, or haptic) may be provided if the mixing ratio is drifting toward an unacceptable range.
  • notifications may be provided when a device command is generated, or when operator intervention is needed.
  • a signal encoder and regressor may operate locally, for example using a computer processing device associated with a material dispensing system.
  • either encoder or regressor, or both, may be deployed in a cloud-based storage system.
  • the output of encoder may be directly used to change a dispensing parameter of one or more material components to ensure that the mixture meets a predefined mixing ratio.
  • a dispensing parameter may include changing a speed or output of an extruder, a progressive cavity pump, a gear pump, or another suitable positive displacement pump.
  • a regressor may then take the encoded signals and produce a mixing ratio signal.
  • the regressor may be a machine learning based algorithm that can be trained in any suitable way.
  • a first training option is a separate training option where the Encoder-Decoder model is trained on a set of signals of a variety of parts for part A, part B, and diverse mixtures.
  • the Machine Learning Regressor is trained in a second step afterwards on the encoded signals and the corresponding mixing ratios.
  • a second training option is an alternating training option, where one batch of signals is used for one training step in the Encoder-Decoder and then used for one training step in the Encoder-Machine Learning Regressor part.
  • a training step consists of a forward pass of the data in a batch, the calculation of the gradient, and an application of the gradient to optimize the weights in the model.
  • a third training option is a combined training option where the triplet of Encoder- Decoder pair and Machine Learning model are optimized simultaneously. This means that a batch is forward through the Encoder, and the representation obtained is forwarded through the Decoder and the Machine Learning Regressor. Then the gradients calculated with both outputs are applied in a weighted combination in the backwards pass.
  • Alternating or combined training may provide a benefit in that the representation of the signals is learned in a way that it has a positive effect on the performance of the Regressor which can lead to a lower error when estimating the mixing ratio.
  • Learning a representation of signals on a variety of materials and mixing ratios also allows the models to be used on previously unseen materials of the same chemical family.
  • this novel approach allows adaption for lot-to-lot variation of the raw material, where a change in one of the parts can lead to a change in the mixed signal for the same mixing ratio. It also enables tracking the mixing of the new materials of the same family be learning to fuse the signals of two parts into a mixed signal.
  • Data traces collected from a sensor system can be processed to provide other information as described herein.
  • sensors may provide signals that can be processed to indicate that corrective action is needed.
  • a sensor includes four electrode pairs.
  • a time series of conductivity can be analyzed from the four sensor capacitors to determine when corrective action has been successful - e.g. when remixing has completed, when phase separation is reversed or a mixture has again reached stability.
  • mixing may take time to reach a steady state.
  • backpressure and different viscosities of components can cause mixing to start off poorly and gradually stabilize.
  • the same variance can be used to track the stabilization and indicate when the dispenser can dispense material on a workpiece or to a receiving container.
  • the trend of the variance can be analyzed against a threshold.
  • the threshold is specific for each material.
  • the signal can be tested for stationarity using the Augmented Dickey-Fuller test. The advantage with this is that manual thresholds often need to be tuned for a new batch, but the ADF test is adaptable.
  • Inhomogeneity can also be detected using sensors described herein.
  • the four electrode pairs should also record similar readings. Some constant offset is possible due to manufacturing tolerances, but in a stable mixing process, the variations of the four signals should be synchronous.
  • Negative covariance indicates a persisting anti-correlated behavior and signifies spatial inhomogeneity.
  • a single component of amixture can also be inhomogeneous, e.g., because of settling in the barrel or insufficient mixing during manufacturing.
  • An augmented Dickey- Fuller test can again be used to confirm stationarity over a longer time. The relevant time frame would be determined by the time it takes to empty the container.
  • FIG. 12A illustrates a concentration profile simulation system architecture 1200.
  • Architecture 1200 can provide computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services.
  • remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers can deliver applications over a wide area network and they can be accessed through a web browser or any other computing component.
  • Software or components shown or described in FIGS. 1- 20 as well as the corresponding data, can be stored on servers at a remote location.
  • the computing resources in a remote server environment can be consolidated at a remote data center location or they can be dispersed.
  • Remote server infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user.
  • the components and functions described herein can be provided from a remote server at a remote location using a remote server architecture.
  • they can be provided by a conventional server, installed on client devices directly, or in other ways.
  • FIG. 12B illustrates one configuration of a system that may be able to provide such functionality.
  • FIG. 12B illustrates a signal analysis system that communicates with a number of devices using a cloud-based network.
  • Signal analysis system 2100 may communicate with a local analysis system 2140.
  • Signal analysis system 2100 may receive a number of sensor signal data 2110 from a number of dispensing operations, such as a pilot line 2104, any of an operational line 2102, and/or a laboratory set up 2106.
  • Sensor signals 2100 may be digital signals, analog signals, conductivity (or impedance or dielectric constant) measurement signals, pressure signals, or other signal information. For example, a low reservoir detected signal, a valve switch indication, or any other detectable indication from any of systems 2102 - 2106.
  • Signal analysis system 2100 may conduct analysis on receive sensor signal information 2100, for example using any suitable analysis tool such as lookup table, comparison thresholds, and/or machine learning algorithms to detect parameter trend information that may indicate a problem, or an action that needs to be taken, such as purging, adjusting mix ratio, etc.
  • suitable analysis tool such as lookup table, comparison thresholds, and/or machine learning algorithms to detect parameter trend information that may indicate a problem, or an action that needs to be taken, such as purging, adjusting mix ratio, etc.
  • Signal analysis of 2100 may provide output indicia 2120 a number of suitable devices 2150.
  • Signal analysis system 2100 may provide output information 2120 continuously, or in response to a request 2134 information.
  • Our request 2130 may be a onetime request for current status information, or a request to receive continuous updates going forward.
  • FIG. 13A-13D illustrate a sensing system in according with embodiments herein.
  • Current sensing setups include components from different manufacturers, and data preparation and processing is done using a separate computing device.
  • a sensor contains signal preparation and processing within a single housing, e.g. a “smart” sensor.
  • Such smart sensors contain a processing component - e.g. a microprocessor, a microcontroller, a digital signal processor or other processing circuitry.
  • a sensor also includes one or more standardized interfaces for interfacing with other systems - e.g. fieldbus systems, sensor networks, input/output links, etc.
  • sensor signal processing is completed without an external computer. Sensing systems herein provide decentralization, increased reliability, reduced cost, increased flexibility and simplification.
  • a sensor system herein includes a concentrator which integrates electronic parts in a single housing. In some embodiments, all electronic components are on one PCB. In some embodiments, an analog frontend with signal conversion (e.g. AD-Converters, DA-Converters or both) are connected to a microcontroller that performs signal converting, processing and provide an output signal. Sensing systems herein may also incorporate operational circuitry, including power-supply, I/O protection circuitry, signal conditioning, reset management and / or debugging circuitry and interfaces. In some embodiments herein, the concentrator includes user-interface components such as LED signaling, UART, USB, wireless interfaces (e.g. Bluetooth®, WiFi, Zigbee®, cellular network), dot-matrix or alphanumeric display, industrial bus systems and / or tactile interface components such as push-buttons, switches, touchscreens, etc.
  • an analog frontend with signal conversion e.g. AD-Converters, DA-Converters or both
  • the concentrator
  • FIG. 13A illustrates a schematic of a sensing system in accordance with embodiments herein.
  • Sensing system 1300 may be used with sensor described in embodiments herein, for example, or with another suitable sensor.
  • a sensor signal reader 1302 connects to a sensor, for example an edge connector of a PCB-board that includes one or more electrode pairs.
  • a trans-impedance amplifier is present to convert current measurements to voltage.
  • a concentrator 1310 receives sensor signals, processes said sensor signals, and provides an output. An output may be provided using an I/O device 1306 and / or another wired or wireless communication protocol 1308.
  • a power source 1312 may provide power to concentrator 1310. While a wired power source 1312 is illustrated, it is possible that power may be provided wirelessly, or concentrator 1310 may be integrated into a material dispensing system from which it draws power.
  • FIG. 13B illustrates one example interface 1320 of a concentrator, that may receive sensor signals using one or more sensor signal receiving ports 1324. Other data or inputs may be received through another receiver 1322, in some embodiments.
  • FIG. 13C illustrates another interface 1330, which may receive a coupling to an input/output device.
  • Power may be provided, for example using port 1334.
  • Data may be communicated from a concentrator using a computer link 1336.
  • FIG. 13D illustrates a component diagram of a sensing system 1340 in accordance with embodiments herein.
  • One or more sensors 1342 provide sensor signals, received by one or more receivers 1344 coupled to, or included within, a housing 1370.
  • system 1340 includes an analog front-end which may include a fdter 1348 and / or an analog multiplexor 1346.
  • a converter e.g. a DA- or DC-converter 1349 may be present.
  • Concentrator 1350 may include non-volatile memory 1352, flash memory 1354, or another suitable information storage.
  • a temperature sensor 1356 may be incorporated into concentrator 1350, or receive atemperature signal from a temperature sensor.
  • Concentrator 1350 may include a clock 1358.
  • Concentrator 1362 may also include reset functionality 1362.
  • a sensor analyzer 1370 may include calibration data and / or functionality 1372.
  • a real-time operating system 1373 may manage functionality.
  • Sensor analyzer 1370 my include Fourier transformer 1376.
  • Sensor analyzer 1370 may include a waveform generator 1376.
  • Sensor analyzer may include other applications 1375 that provide other functionality, such as detecting of material characteristics like mix ratio, material age, curing progress, etc.
  • Sensor analyzer 1370 may also include an identifier 1374 that identifies a type of sensor.
  • Concentrator 1350 may include a power management system 1360 that includes, or accesses, a power supply 1366.
  • a power quality 1368 may be monitored.
  • Energy consumption 1369 may be tracked.
  • Conversion input and output ranges 1364 may be stored.
  • a symmetric voltage 1367 may be used.
  • FIG. 14 illustrates a dispensing system in accordance with embodiments herein.
  • Many dispensing operations are done with a portable, handheld system. Errors in dispensing or adhesive failure can result if material quality or machine settings are not correct. For example, an incorrect mix ratio or an incorrect pressure setting may result in an unacceptable product.
  • Described in FIG. 14 is one example of a system that can receive and process sensor signals without a separate computing device.
  • Described herein are many embodiments of sensors that may be used with a dispenser.
  • Described herein are systems for measuring pressure in a dispensing system.
  • System 1400 includes a dispenser 1410.
  • Dispenser 1410 is illustrated as an adhesive dispenser 1410, however other dispensers may also benefit from systems described herein.
  • Dispenser 1410 includes an in-line sensor 1430 that senses electrical properties of a material being dispensed.
  • a pressure sensor 1440 is incorporated into dispenser 1410 and monitors the pressure within the dispenser.
  • Dispenser 1410 also includes a signal processing system 1420.
  • a signal receiver receives a sensed parameter signal from sensor 1430.
  • a processing unit which may include any suitable processor or processing circuitry, processes the sensed signal.
  • a memory may store calibration data, historic signals, etc.
  • a display 1450 may present processed information to a user, the information received from signal processing system 1420, for example using a communication module. Display 1450 may be integrated into dispenser 1410, or another display visible to a dispenser operator, such as a mobile computer, a worksite display, etc. However, while a display 1450 is illustrated as conveying processed information to an operator, it is expressly contemplated that output from signal processing system 1420 can be presented as audio or haptic feedback in some embodiments herein.
  • signal processing system 1420 may also actuate a change in dispensing parameters. For example, a mix ratio may be sensed that as drifted away from a specified mix ratio. Signal processing system 1420 may, based on the sensed mix ratio drift, adjust a mix ratio by changing a pump speed for one component. Signal processing system 1420 may control pump speed directly, or indirectly, such that an instruction to change the pump speed is sent to a pump controller. Signal processing system 1420 may also communicate the mix ratio drift, e.g. through display 1450. In some embodiments, signal processing system 1420 may only communicate a detected material issue - e.g. mix ratio, aging, curing, pressure, etc. - and an operator may need to take steps to address the issue manually. However, it is expressly contemplated that, in some embodiments, dispenser parameters are adjusted automatically, in real-time, based on signals from sensors 1430, 1440.
  • dispensing system 1400 includes a material inventory system 1460.
  • Material inventory system 1460 may store physical materials 1462 and dispensers 1464 (e.g. different static mixer types for placement in dispensing system 1410) available for operator use. However, it is expressly contemplated that, in some embodiments, material inventory system 1460 stores only information about materials 1462 and dispensers 1464.
  • Materials 1462 may include information relevant to a dispensing operation that utilize them.
  • a dispensing cartridge may include an RFID tag, NFC tag, or other wirelessly accessible data storage. Information may also be transferred using a printed code - such as a bar code or QR code. In some embodiments, a printed RFID label is applied to dispensing cartridges.
  • dispensing information can be retrieved by dispensing system 1410, or from material inventory system 1460.
  • Dispensing information may include dispensing parameters 1466, such as an operating pressure for one or both components, and / or a preferred 1468 for use with material 1462. Other information may also be provided.
  • system 1410 may display operating guidance on a display, e.g. 1450, for an operator.
  • a dispenser receives expected process parameters from material information system 1460 based on an identification of material 1462 from an NFC tag, RFID tag, or other information storage system on a material to be dispensed.
  • FIG. 15 provides a general block diagram of the components of a mobile cellular device 1516 that can run some components shown and described herein. Mobile cellular device 1516 interacts with them or runs some and interacts with some.
  • a communications link 1513 is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically, such as by scanning. Examples of communications link 1513 include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks.
  • SD Secure Digital
  • Interface 1515 and communication links 1513 communicate with a processor 1517 (which can also embody a processor) along a bus 1519 that is also connected to memory 1521 and input/output (I/O) components 1523, as well as clock 1525 and location system 1527.
  • processor 1517 which can also embody a processor
  • bus 1519 that is also connected to memory 1521 and input/output (I/O) components 1523, as well as clock 1525 and location system 1527.
  • I/O components 1523 are provided to facilitate input and output operations and the device 1516 can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port.
  • Other I/O components 1523 can be used as well.
  • Clock 1525 illustratively comprises a real time clock component that outputs a time and date. It can also provide timing functions for processor 1517.
  • location system 1527 includes a component that outputs a current geographical location of device 1516.
  • This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. It can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions.
  • GPS global positioning system
  • Memory 1521 stores operating system 1529, network settings 1531, applications 1533, application configuration settings 1535, data store 1537, communication drivers 1539, and communication configuration settings 1541.
  • Memory 1521 can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include computer storage media (described below).
  • Memory 1521 stores computer readable instructions that, when executed by processor 1517, cause the processor to perform computer-implemented steps or functions according to the instructions. Processor 1517 can be activated by other components to facilitate their functionality as well. It is expressly contemplated that, while a physical memory store 1521 is illustrated as part of a device, that cloud computing options, where some data and / or processing is done using a remote service, are available.
  • FIG. 16 shows that the device can also be a smart phone 1671.
  • Smart phone 1671 has a touch sensitive display 1673 that displays icons or tiles or other user input mechanisms 1675.
  • Mechanisms 1675 can be used by a user to run applications, make calls, perform data transfer operations, etc.
  • smart phone 1671 is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone. Note that other forms of the devices are possible.
  • FIG. 16 illustrates an embodiment where a device 1600 is a smart phone 1671, it is expressly contemplated that a display may be presented on another comping device.
  • FIG. 17 is one example of a computing environment in which elements of systems and methods described herein, or parts of them (for example), can be deployed.
  • an example system for implementing some embodiments includes a general -purpose computing device in the form of a computer 1710.
  • Components of computer 1710 may include, but are not limited to, a processing unit 1720 (which can comprise a processor), a system memory 1730, and a system bus 1721 that couples various system components including the system memory to the processing unit 1720.
  • the system bus 1721 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect to systems and methods described herein can be deployed in any suitable configuration.
  • Computer 1710 typically includes a variety of computer readable media.
  • Computer readable media can be any available media that can be accessed by computer 1710 and includes both volatile/nonvolatile media and removable/non-removable media.
  • Computer readable media may comprise computer storage media and communication media.
  • Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile/nonvolatile and removable/non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD- ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer 1710.
  • Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media.
  • modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
  • the system memory 1730 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 1731 and random -access memory (RAM) 1732.
  • ROM read only memory
  • RAM random -access memory
  • BIOS basic input/output system
  • RAM 1732 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 1720.
  • FIG. 17 illustrates operating system 1734, application programs 1735, other program modules 1736, and program data 1737.
  • the computer 1710 may also include other removable/non-removable and volatile/nonvolatile computer storage media.
  • FIG. 17 illustrates a hard disk drive 1741 that reads from or writes to non-removable, nonvolatile magnetic media, nonvolatile magnetic disk 1752, an optical disk drive 1755, and nonvolatile optical disk 1756.
  • the hard disk drive 1741 is typically connected to the system bus 1721 through a non-removable memory interface such as interface 1740
  • optical disk drive 1755 are typically connected to the system bus 1721 by a removable memory interface, such as interface 1750.
  • the functionality described herein can be performed, at least in part, by one or more hardware logic components.
  • illustrative types of hardware logic components include Field- programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.
  • FPGAs Field- programmable Gate Arrays
  • ASICs Application-specific Integrated Circuits
  • ASSPs Application-specific Standard Products
  • SOCs System-on-a-chip systems
  • CPLDs Complex Programmable Logic Devices
  • hard disk drive 1741 is illustrated as storing operating system 1744, application programs 1745, other program modules 1746, and program data 1747. Note that these components can either be the same as or different from operating system 1734, application programs 1735, other program modules 1736, and program data 1737.
  • a user may enter commands and information into the computer 1710 through input devices such as a keyboard 1762, a microphone 1763, and a pointing device 1761, such as a mouse, trackball or touch pad.
  • Other input devices may include a joystick, game pad, satellite receiver, scanner, or the like.
  • These and other input devices are often connected to the processing unit 1720 through a user input interface 1760 that is coupled to the system bus but may be connected by other interface and bus structures.
  • a visual display 1791 or other type of display device is also connected to the system bus 1721 via an interface, such as a video interface 1790.
  • computers may also include other peripheral output devices such as speakers 1797 and printer 1796, which may be connected through an output peripheral interface 1795.
  • the computer 1710 is operated in a networked environment using logical connections, such as a Local Area Network (LAN) or Wide Area Network (WAN) to one or more remote computers, such as a remote computer 1780.
  • logical connections such as a Local Area Network (LAN) or Wide Area Network (WAN)
  • remote computers such as a remote computer 1780.
  • the computer 1710 When used in a LAN networking environment, the computer 1710 is connected to the LAN 1771 through a network interface or adapter 1770. When used in a WAN networking environment, the computer 1710 typically includes a modem 1772 or other means for establishing communications over the WAN 1773, such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device. FIG. 17 illustrates, for example, that remote application programs 1785 can reside on remote computer 1780.
  • spatially related terms including but not limited to, “proximate,” “distal,” “lower,” “upper,” “beneath,” “below,” “above,” and “on top,” if used herein, are utilized for ease of description to describe spatial relationships of an element(s) to another.
  • Such spatially related terms encompass different orientations of the device in use or operation in addition to the particular orientations depicted in the figures and described herein. For example, if an object depicted in the figures is turned over or flipped over, portions previously described as below or beneath other elements would then be above or on top of those other elements.
  • an element, component, or layer for example when an element, component, or layer for example is described as forming a “coincident interface” with, or being “on,” “connected to,” “coupled with,” “stacked on” or “in contact with” another element, component, or layer, it can be directly on, directly connected to, directly coupled with, directly stacked on, in direct contact with, or intervening elements, components or layers may be on, connected, coupled or in contact with the particular element, component, or layer, for example.
  • an element, component, or layer for example is referred to as being “directly on,” “directly connected to,” “directly coupled with,” or “directly in contact with” another element, there are no intervening elements, components or layers for example.
  • the techniques of this disclosure may be implemented in a wide variety of computer devices, such as servers, laptop computers, desktop computers, notebook computers, tablet computers, hand-held computers, smart phones, and the like. Any components, modules or units have been described to emphasize functional aspects and do not necessarily require realization by different hardware units.
  • the techniques described herein may also be implemented in hardware, software, firmware, or any combination thereof. Any features described as modules, units or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. In some cases, various features may be implemented as an integrated circuit device, such as an integrated circuit chip or chipset.
  • modules have been described throughout this description, many of which perform unique functions, all the functions of all of the modules may be combined into a single module, or even split into further additional modules.
  • the modules described herein are only exemplary and have been described as such for better ease of understanding.
  • the techniques may be realized at least in part by a computer-readable medium comprising instructions that, when executed in a processor, performs one or more of the methods described above.
  • the computer-readable medium may comprise a tangible computer-readable storage medium and may form part of a computer program product, which may include packaging materials.
  • the computer- readable storage medium may comprise random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), nonvolatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like.
  • RAM random access memory
  • SDRAM synchronous dynamic random access memory
  • ROM read-only memory
  • NVRAM nonvolatile random access memory
  • EEPROM electrically erasable programmable read-only memory
  • FLASH memory magnetic or optical data storage media, and the like.
  • the computer-readable storage medium may also comprise a non-volatile storage device, such as a hard-disk, magnetic tape, a compact disk (CD), digital versatile disk (DVD), Blu- ray disk, holographic data storage media, or other non-volatile storage device.
  • a non-volatile storage device such as a hard-disk, magnetic tape, a compact disk (CD), digital versatile disk (DVD), Blu- ray disk, holographic data storage media, or other non-volatile storage device.
  • processor may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described herein.
  • functionality described herein may be provided within dedicated software modules or hardware modules configured for performing the techniques of this disclosure. Even if implemented in software, the techniques may use hardware such as a processor to execute the software, and a memory to store the software. In any such cases, the computers described herein may define a specific machine that is capable of executing the specific functions described herein. Also, the techniques could be fully implemented in one or more circuits or logic elements, which could also be considered a processor.
  • the two-part adhesive 3MTM Scotch-WeldTM Epoxy Adhesive DP460 was dispensed through two progressive cavity pumps, one for each part of the adhesive. After pumping, the two parts were joined in a static mixer that mixes the adhesive.
  • the sensor described here was attached to the end of the static mixer so that mixed adhesive passes through the sensor and electrical signals are obtained as described above. That sensor collected permittivity data at two frequencies along with the temperature of the adhesive.
  • the following variables were used to create the dataset: flow rate, temperature, water content, frequency, permittivity, mix ratio. Sensor data was collected continuously for about two hours.
  • the resulting labeled data was split 70/30 for training/testing datasets and the training dataset was used to train a machine learning model to predict the mix ratio of the adhesive.
  • the testing dataset was then used to score the model and provide indication that the model was correctly identifying the mix ratio with only the data from the sensor.
  • Figure 18 shows the model predicting the mix ratio at the various conditions described above.
  • the line chart shows the electrical signal during the dispensing process. The actual and predicted results are illustrated. Differences between actual and predicted mix ratio were within 3% in this example.
  • the two-part adhesive 3MTM Scotch-WeldTM Epoxy Adhesive DP460 was dispensed according to Example 1.
  • the output data was relabeled with the water content as the output variable and machine learning model training was done with a 70/30 split training/testing datasets.
  • Figure 19 shows the capability of the model to predict the water content as the water content was steadily raised from 0% to 1% water.

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Abstract

L'invention concerne un système de détection pour un mélange, qui comprend une zone de détection contenant un mélange. Un capteur se trouve à l'intérieur de la zone de détection, qui comprend une carte de circuit imprimé (302), une électrode émettrice (310) configurée pour générer un champ électrique, et une électrode réceptrice (320). Les électrodes émettrice et réceptrice sont conçues pour entrer directement en contact avec le mélange. Le capteur est configuré pour détecter une valeur de paramètre électrique pour le mélange. Le système comprend également un analyseur de signal (962) qui, sur la base du paramètre électrique détecté, détermine une teneur en eau du mélange.
PCT/IB2024/055619 2023-06-12 2024-06-07 Systèmes et procédés de détection d'eau dans un mélange Ceased WO2024256941A1 (fr)

Priority Applications (3)

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KR1020257042727A KR20260018875A (ko) 2023-06-12 2024-06-07 혼합물 내 수분 검출을 위한 시스템 및 방법
EP24733762.9A EP4724799A1 (fr) 2023-06-12 2024-06-07 Systèmes et procédés de détection d'eau dans un mélange
CN202480038937.XA CN121285736A (zh) 2023-06-12 2024-06-07 用于混合物中的水检测的系统和方法

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US63/507,715 2023-06-12

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110050257A1 (en) * 2009-08-27 2011-03-03 John Ashworth Moisture meter
US20110068809A1 (en) * 2009-09-18 2011-03-24 Rainmaker Holding Company System and method for determining moisture content in a bale of hay
DE102011056548A1 (de) * 2011-12-16 2013-06-20 Kompetenzzentrum Strukturleichtbau E.V. Messeinrichtung und Verfahren zum Ermitteln des Feuchtegehaltes eines Materials
WO2018087221A1 (fr) * 2016-11-10 2018-05-17 Windmolders Beton N.V. Procédé et dispositif de fabrication d'une pierre à pavé
CN111624238A (zh) * 2020-03-03 2020-09-04 红塔烟草(集团)有限责任公司 一种烟叶分级用水分检测手套
US20210063336A1 (en) * 2018-05-03 2021-03-04 Pouria Ghods Construction material assessment method and systems
WO2021056362A1 (fr) 2019-09-26 2021-04-01 小米通讯技术有限公司 Procédé de traitement d'ensemble de ressources de commande, dispositif et support de stockage informatique

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110050257A1 (en) * 2009-08-27 2011-03-03 John Ashworth Moisture meter
US20110068809A1 (en) * 2009-09-18 2011-03-24 Rainmaker Holding Company System and method for determining moisture content in a bale of hay
DE102011056548A1 (de) * 2011-12-16 2013-06-20 Kompetenzzentrum Strukturleichtbau E.V. Messeinrichtung und Verfahren zum Ermitteln des Feuchtegehaltes eines Materials
WO2018087221A1 (fr) * 2016-11-10 2018-05-17 Windmolders Beton N.V. Procédé et dispositif de fabrication d'une pierre à pavé
US20210063336A1 (en) * 2018-05-03 2021-03-04 Pouria Ghods Construction material assessment method and systems
WO2021056362A1 (fr) 2019-09-26 2021-04-01 小米通讯技术有限公司 Procédé de traitement d'ensemble de ressources de commande, dispositif et support de stockage informatique
CN111624238A (zh) * 2020-03-03 2020-09-04 红塔烟草(集团)有限责任公司 一种烟叶分级用水分检测手套

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