Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
  • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
  • B25J9/00—Program-controlled manipulators
  • B25J9/16—Program controls
  • B25J9/1628—Program controls characterised by the control loop
  • B25J9/163—Program controls characterised by the control loop learning, adaptive, model based, rule based expert control
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/32—Operator till task planning
  • G05B2219/32194—Quality prediction
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/33—Director till display
  • G05B2219/33321—Observation learning
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/39—Robotics, robotics to robotics hand
  • G05B2219/39271—Ann artificial neural network, ffw-nn, feedforward neural network
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
  • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
  • Y02P90/80—Management or planning

Definitions

• the invention relates to the products and process as per the first portion of the independent claims.
• NC numerical control
• CNC computerized numerical control
• a CNC machine alters a blank piece of material such as metal, plastic, wood, ceramic, or any composite thereof to meet precise specifications by following programmed instructions and without a manual operator.
• PTL1 discloses a closed-loop control process for a machining tool based on a numerical control program.
• the NC program (1) is generated offline by an NC programming
• An essential software component of CNC controller commonly referred to as the interpolator (2) computes the most important commanded points for the programmed tool path.
• Another software task of the controller is known as the position controller and, based on the path, periodically calculates commanded axes positions, velocity and acceleration along each of the axes.
• the generated commanded position is transmitted to a drive or current controller which periodically and precisely computes the required electrical current.
• all commanded values are reliably and periodically updated periodically at varying time intervals.
• the interpolator task of today’s CNC-controller is being executed every 4 msec/250Hz, the position task at 1 msec/1.000 Hz, the drives control task works at a frequency of 50 microsec/2.000 Hz.
• each CNC machine is equipped with position sensors that constantly measure the current position of each axis. From these sensor values (3), further variables such as speed,
• the drives provide the real current values and current acceleration for the execution of the drives control loop.
• measurements (4) are typically taken by means of conventional coordinate measuring machines (CMM) to inspect the piece’s geometrical and surface quality.
• CMM coordinate measuring machines
• the measurements can be defined in terms of tolerance, such as may apply to parallelism, perpendicularity, angularity, position, concentricity, or circularity.
• tolerance such as may apply to parallelism, perpendicularity, angularity, position, concentricity, or circularity.
• NC program is static and does not change during series
• Figure 1 shows a conventional CNC machining process.
• Figure 2 illustrates Data-Preparation and Pre-Processing
• Figure 3 illustrates the model-generation process
• Figure 4 illustrates the quality root cause analysis. It identifies the NC- program-parameters impacting the part tolerances.
• Figure 5 illustrates the prediction of part quality based on the digitally
• Figure 6 illustrates the online adaption of NC-program-parameters in order to accomplish the required part quality.
• Figure 7 illustrates the root cause analysis of all NC-parameters which influence the cycle time of the manufacturing process.
• Figure 8 illustrates the online adaption of the relevant NC-program
• Figure 9 illustrates the applied Artificial Intelligence methods.
• Figure 10 illustrates the data-driven modelling
• Figure 11 illustrates how Machine Learning generates the digital model.
• Figure 12 illustrates how Machine Learning optimizes the input
• Figure 13 illustrates the methodological components of the Machine
• Figure 14 illustrates two methodological types of root cause analytics.
• Figure 15 illustrates two methodological types of machine learning. Description of embodiments
• the input data used is being prepared and pre-processed for the subsequent Quality and Productivity Analytic applications.
• the input data include:
• phase 2 the machine learning model is generated and parameterized.
• the input data (configuration data of the machine, controllers and drives, NC program, dynamically generated setpoint data, sensor data and the output data are correlated.)
• the output data are the quality data measured by a measuring machine such as parallelism, rectangularity, centricity, concentricity or circularity accuracy.
• the manufacturing tolerances specified in all production processes are quantitatively evaluated during the workpiece measurement and it is determined whether these tolerances are in the defined window or outside, i.e. whether reject parts were produced by tolerance violation.
• the machine learning therefore establishes the correlation between the input data and the actual production tolerances.
• the resulting quality data of the workpieces can in one case be predicted from the input data, in the other case can be identified from the measured manufacturing tolerances, which input parameters for the result was responsible at what impact.
• the input data can also online being adapted during machining in order to produce 100% good workpieces.
• Figure 4 illustrates an example where a given tolerance - here, in terms of circularity - was violated with a deviation of 15 pm, the affected contours of the workpiece (6) being circled in the drawing.
• the proposed method (10 - Figure 2) pinpoints among the parameters of the NC program (1) the interpolation method, commanded feed rate, and tool data as causing this circularity defect with at an impact ratio of 53 % to 19 % to 28 %.
• the NC programmer will readily apprehend that the deviation may be reduced by adapting said parameters.
• the conventional approach does not have a root-cause model between input- and output variables and a very time- consuming trial and error process must be executed. To reach an optimum is practically impossible.
• the machine learning system may adapt parameters of the
• NC program (1)“online” to minimize any quality breaches, striving for zero- defect production.
• Figure 6 Since the machine learning system has generated a root cause model between all input- and dynamically generated commanded values, resulting real values and the resulting part quality the system can also be applied to achieve Zero-Defect
• the computational model of the process may be trained for optimization goals other than quality of the workpiece, for instance, the cycle time or net process time required by the tool for machining the workpiece.
• the network establishes any correlations between, for instance, parameters of the NC program and resulting cycle time.
• the method identifies commanded feed rate, commanded acceleration, commanded jerk, commanded drives current and commanded power consumption of the spindle as influential at a ratio of 21 %, 27%, 19%, 21 % and 12%. Knowledge of these correlations will allow the NC programmer to adjust said parameters to eliminate wasteful expenditure of resources.
• embodiments may fine-tune the identified parameters of the NC program online in order to minimize cycle time.
• the system is trying to achieve the minimum cycle time.
• Pre-processing the data is essential for good results.
• Correlation Analysis prunes the feature space by finding relevant or redundant features, while Autoencoders do the same by compressing and reconstructing input vectors.
• Principal Component Analysis also simplifies the data for the later stages by finding the features which best represent the data with minimal loss in information.
• a Fast Fourier Transformation converts cyclic data (might be a subset of the original features) into its spectral representation which yields information about the main frequencies observed in the data. A change in frequency often is the result of an anomaly/error in real use cases and even small changes in the spectrum can be detected via the later stages.
• the pre-processed data is then feed into a Machine Learning model
• the output of the trained model when presented with new data can be used for root cause analysis, optimizing input parameters or predictive maintenance.
• the training step is using a model specific algorithm to learn the relation between the input data (sensors, parameters, ...) and the output (quality measurements, cycle times, error cases).
• the goal is a model which can produce accurate predictions about the output when presented with new input data, reducing for example the need for manual measurements.
• the process of model creation usually consists of three steps.
• a naive model tries to predict a known output value based on the corresponding input. By comparing the prediction with the actual value, it creates an error measure which it uses to update itself to minimize this error. This process is repeated for all datapoints in the training set or until the prediction is optimal.
• the system can also search for better combinations of input parameters given the environmental features and a metric to optimize (like no defects, shortest cycle time, most throughput).
• correlation analysis yields insight about the weight of impact each input dimension (sensors, parameters, ...) has on the output. Additionally, after analyzing a datapoint as anomalous, the feature relevance can be computed to find the root cause of the problem.
• the process of model creation usually consists of three steps.
• a naive model tries to predict a known output value based on the corresponding input. By comparing the prediction with the actual value, it creates an error measure which it uses to update itself to minimize this error. This process is repeated for all datapoints in the training set or until the prediction is optimal.
• the learned model can then be used to detect anomalies, classify errors or predict attributes. With new data, the model can also be updated to improve the precision or learn new classes.
• Irregular behavior can also be traced back and attributed to individual features/sensors by calculating the distances between the model’s representation of normal behavior and current sensor values.
• the invention is applicable, among others, throughout the CNC-controller and robot-based discrete manufacturing industry. Reference signs list
• NPL1 SMID, Peter. CNC programming handbook: a comprehensive guide to practical CNC programming. Industrial Press Inc., 2003.

Landscapes

• Engineering & Computer Science (AREA)
• Artificial Intelligence (AREA)
• Evolutionary Computation (AREA)
• Mechanical Engineering (AREA)
• Health & Medical Sciences (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Robotics (AREA)
• Medical Informatics (AREA)
• Software Systems (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Numerical Control (AREA)

Applications Claiming Priority (1)

Publications (1)

Family

ID=66554368

Family Applications (1)

Country Status (2)

Families Citing this family (7)

Family Cites Families (2)

• 2019
  • 2019-05-11 WO PCT/EP2019/062104 patent/WO2020228932A1/en not_active Ceased
  • 2019-05-11 EP EP19724411.4A patent/EP3969230A1/de active Pending

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events