METHOD AND SYSTEM FOR QUALITY CONTROL IN INDUSTRIAL MANUFACTURING

Abstract

A method for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece and/or product includes creating a learning model for at least one production process for the at least one workpiece and/or product. The learning model is trained and initialized using a meta-learning algorithm, and the learning model is calibrated using normalized data of the at least one production process for the at least one workpiece and/or product. Currently generated data of the at least one production process for at least one currently produced workpiece/product is forwarded to the learning model. The data is generated by sensors. The learning model compares the currently generated data with the normalized data and finds deviations. The learning model scales the deviations between the currently generated data and the normalized data, and the learning model communicates presence of an anomaly for the currently produced workpiece/product.

Claims

1. A method for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece, product, or workpiece and product, the method comprising: creating a learning model for at least one production process for a workpiece, product, or workpiece and product; training and initializing the learning model using a meta-learning algorithm; calibrating the learning model using normalized data of the at least one production process for the workpiece, product, or workpiece and product; forwarding currently generated data of the at least one production process for at least one currently produced workpiece/product to the learning model, the currently generated data being generated by sensors; comparing, by the learning model, the currently generated data with the normalized data, such that deviations are found; scaling, by the learning model, the deviations between the currently generated data and the normalized data; and communicating, by the learning model, presence of an anomaly for the currently produced workpiece/product.

2. The method of claim 1, wherein the learning model is in the form of a deep neural network.

3. The method of claim 1, wherein the meta-learning algorithm is an agnostic meta-learning algorithm that trains the learning model using a gradient method.

4. The method of claim 1, wherein a normalized mean value of a stipulated measured variable for a workpiece, product, or workpiece and product, production process, or a combination thereof is defined as the basis for calculating a deviation of the deviations.

5. The method of claim 1, wherein the currently generated data has a data identifier.

6. The method of claim 1, wherein the sensors use nonoptical methods for data generation.

7. The method of claim 1, wherein the anomaly is found when a deviation of the deviations is above or below a stipulated limit value.

8. A system for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece, product, or workpiece and product, the system comprising: a learning model for at least one production process for a workpiece, product, or workpiece and product, the learning model being configured to be trained and initialized by a meta-learning algorithm, and being configured to be calibrated using normalized data of the at least one production process for the workpiece, product, or workpiece and product; and one or more sensors configured to generate current data of a production process for a currently produced workpiece and to forward the currently generated data to the learning model, wherein the learning model is configured to: compare the currently generated data with the normalized data; find deviations; scale the deviations between the currently generated data and the normalized data; and communicate presence of an anomaly for the currently produced workpiece/product.

9. The system of claim 8, wherein the learning model is in the form of a deep neural network.

10. The system of claim 8, wherein the meta-learning algorithm is in the form of an agnostic meta-learning algorithm that trains the learning model by a gradient method.

11. The system of claim 8, wherein a normalized mean value of a stipulated measured variable for a workpiece, product, or workpiece and product, production process, or a combination thereof is defined as the basis for calculation of a deviation of the deviations.

12. The system of claim 8, wherein the currently generated data has a data identifier.

13. The system of claim 8, wherein the sensors are configured to use nonoptical methods for data generation.

14. The system of claim 8, wherein the anomaly is found when a deviation of the deviations is above or below a stipulated limit value.

15. (canceled)

16. In a non-transitory computer-readable storage medium that stores instructions executable by one or more processors for quality control in industrial manufacturing for one or more production processes for producing at least one workpiece, product, or workpiece and product, the instructions comprising: creating a learning model for at least one production process for a workpiece, product, or workpiece and product; training and initializing the learning model using a meta-learning algorithm; calibrating the learning model using normalized data of the at least one production process for the workpiece, product, or workpiece and product; forwarding currently generated data of the at least one production process for at least one currently produced workpiece/product to the learning model, the currently generated data being generated by sensors; comparing, by the learning model, the currently generated data with the normalized data, such that deviations are found; scaling, by the learning model, the deviations between the currently generated data and the normalized data; and communicating, by the learning model, presence of an anomaly for the currently produced workpiece/product.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1 shows a block diagram to explain an embodiment detail of a system;

[0026] FIG. 2 shows a flowchart to explain a method according to an embodiment; and

[0027] FIG. 3 shows a schematic depiction of a computer program product according to an embodiment.

DETAILED DESCRIPTION

[0028] Additional features, aspects and advantages of the exemplary embodiments will become apparent from the detailed description.

[0029] According to the present embodiments, a deep neural learning model (deep learning model) 100 is used to detect defects in workpieces in a production run. An example of such a learning model 100 is a convolutional neural network. A deep learning model 100 denotes a class of optimization methods for artificial neural networks that have multiple intermediate layers (e.g., hidden layers) between an input layer and an output layer and, as a result, have a comprehensive internal structure. Such a learning model 100 is optimized for fast adaptation to a new target problem, even if only few input data is available from a normal production and/or machining process. The learning model may be programmed by frameworks such as, for example, TensorFlow or PyTorch.

[0030] For training and calibrating the learning model 100, an agnostic meta-learning algorithm 200 is used according to the present embodiments, as described, for example, by Chelsea Finn et al. (Chelsea Finn, Pieter Abbeel, Sergey Levine: Model-Agnostic Meta-Learning for Fast Adaption of Deep Networks, 18 Jul. 2017). The agnostic meta-learning algorithm 200 is capable of training the learning model 100 by a gradient method in order to perform initialization of the parameterized deep learning model 100.

[0031] The data conditioning, the model training, and the model application for an instance of application are described in more detail below. The detection of anomalies and/or faults during the production and/or machining of workpieces and/or other products is based exclusively on sensor signal data generated by data capture devices and sensors in a production installation.

[0032] As a result of the training by the agnostic meta-learning algorithm 200, the deep learning model 100 is capable of adapting to a new manufacturing process after the training phase and of using only data from workpieces that are of sufficiently good quality. Ideally, the learning model 100 achieves the required adaptation to a new manufacturing process cycle simply by virtue of the data generated for the test runs during the calibration phase.

[0033] According to the present embodiments, the learning model 100 is calibrated only based on data relating to normal states of a workpiece and/or product (e.g., normal state data) and when processing, for example, nonvisual sensor data with the aim of anomaly detection in manufacturing processes.

[0034] The data conditioning for training the learning model 100 is explained in more detail below. In order to train the learning model 100, a sufficient number of sensors are to be provided in a manufacturing installation. By way of example, these sensors may measure torques of various axles in a milling machine and may control deviations. Additionally, data from a sufficient number of workpieces and machining processes are to be provided. Further, a data identifier (e.g., label) that appropriately marks the data that indicates anomalies may be provided.

[0035] In one embodiment, an expert undertakes identification of the data using data identifiers (e.g., labeling) with an appropriate reference. An example that will be mentioned is torque measurements for a milling spindle. By way of example, the control errors in the Z-axis of 100 tools and for 8 different processing processes are examined. The processing processes include various rough machining processes (e.g., the cutting of a pocket into the workpiece) and refining processes such as smoothing the top surface of the workpiece.

[0036] As a preprocessing act of the method according to the present embodiments, the various data signals generated for each process act are captured according to deviation from a stipulated standard value on a scale, and are thus scaled. For some data signals, it may also be appropriate to subtract the data value of the data signal of the representative data for the normal machining (e.g., standard machining) from the newly generated data signals in order to be able to detect the deviation more distinctly. This is appropriate, for example, if the value of the deviation is small in comparison with the value of the data signal. The deviations from a normalized mean value of a stipulated measured variable are therefore captured for a workpiece and/or product.

[0037] The learning model 100 is trained by the agnostic meta-learning algorithm 200. The algorithm 200 for training the model may be represented as pseudocode as follows:

TABLE-US-00001  1. Preprocess the data.  2. Randomly initialize the learning model parameters θ.  3. while not converging  4.  Take a set of n processes P.sub.i ~ p (P)  5.  For every P.sub.i, the following should be performed:  6.   Take k training examples for the normal machining behavior from the process P.sub.i.  7.   Rate the performance of the anomaly detection of the learning model on the basis of these examples.  8.   Update the learning model parameters θ according to θ′ using a gradient method.  9.  End for 10.  Take n x k training examples for the detection of anomalies from the taken set of n processes. 11.  Update the learning model parameters θ with reference to the error that arose from the model parameterized by θ′ on account of the gradient method. 12. End while

[0038] In the training phase, the learning model 100 is optimized for the instance of application. If the learning model 100 is used for detecting anomalies for a new workpiece and/or for a new machining process, it is assumed according to the present embodiments that sufficient data based on the normal machining have been captured during the calibration phase of the machine and the process (e.g., as a result of the making of test workpieces). According to the present embodiments, the method acts of the inner training loop of the meta-learning algorithm 200 are used to quickly adapt the learning model parameters 100 to the new situation (e.g., new workpiece and/or new manufacturing process):

1. Preprocess the calibration data.
2. Initialize the learning model 100 using the parameters θ found during the learning model training.
3. Take k training examples for the normal machining behavior after the new process P.sub.i.
4. Update the learning model parameters θ to θ′ using the same configured gradient method as was used during the training.

[0039] If the learning model 100 changes to the production mode, the calibrated learning model 100 is used in order to find anomalies for the data currently generated by the sensors (e.g., live data) for a workpiece and/or product during a specific production process:

1. Preprocess the currently generated data.
2. Initialize the learning model 100 using the parameters e′ found during the model calibration.
3. Predict anomaly probabilities for the currently generated data using the learning model 100.

[0040] The learning model 100 is trained under similar conditions to learning models that would arise in a real production scenario. In one embodiment, the learning model 100 is trained in a laboratory environment using a sufficient volume of data with a data identifier from different machining processes and/or production steps, such as, for example, the use of different machines. The agnostic meta-learning algorithm 200 allows the learning model 100 to quickly adapt itself to a new production environment and/or production processes.

[0041] In one embodiment, a process step and a limited dataset from the normal manufacturing and machining processes for manufacturing workpieces are used in each training iteration in order to calibrate the learning model 100. The learning model 100 is therefore capable of finding anomalies for individual workpieces from sensor signal data that is generated during a new manufacturing process and was not used during the training phase of the learning model 100. When the learning model 100 is used during manufacture, anomalies that are below or above a stipulated limit value are then detected quickly and efficiently for each individual workpiece for which current data is generated.

[0042] FIG. 3 shows a flowchart for a method according to an embodiment for quality control in industrial manufacturing.

[0043] In act S10, a learning model 100 is created for one or more production processes for at least one workpiece and/or product.

[0044] In act S20, the learning model 100 is trained and initialized using a meta-learning algorithm 200.

[0045] In act S30, the learning model 100 is calibrated using normalized data of at least one production process for the at least one workpiece and/or product.

[0046] In act S40, currently generated data of the at least one production process for at least one currently produced workpiece/product is forwarded to the learning model (100). The data is generated by sensors.

[0047] In act S50, the learning model 100 compares the currently generated data with the normalized data and finds deviations.

[0048] In act S60, the learning model scales the deviations between the currently generated data and the normalized data.

[0049] In act S70, the learning model communicates the presence of an anomaly for the currently produced workpiece/product.

[0050] FIG. 3 schematically depicts a computer program product 300 containing one or more executable computer codes 350 for performing the method according to the first aspect of the present embodiments.

[0051] The present embodiments may be used very inexpensively because the calibration complexity for the learning model 100 for a new manufacturing process is low and may easily be implemented. Further, the present embodiments allow guided quality control as compared with planned quality control in industrial manufacturing. The period to react to defects while production is ongoing may be significantly shortened, and the reject rate for products may be substantially reduced. The present embodiments may be used in the case of large quantities and during mass production, because each workpiece may be inspected using high-resolution data. The present embodiments may be used to deal with the unique features that any production process, any machine, and any site has.

[0052] The elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present invention. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent. Such new combinations are to be understood as forming a part of the present specification.

[0053] While the present invention has been described above by reference to various embodiments, it should be understood that many changes and modifications can be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.