Systems and Methods for Computer-Aided Machining

Abstract

Systems methods for computer-aided machining includes A) providing a material batch with an undetermined machinability to a machining tool, B) specifying a set of machining conditions having a machining speed, C) inserting a tool that has a predetermined type and a predetermined wear into the machining tool, D) machining the material batch with the machining tool, monitoring wear of the inserted tool during the machining, and determining a first tool life of the tool, E) repeating Steps B, C and D to determine a second tool life, while setting a different machining speed in Step B and inserting a tool of the same type in Step C, F) determining coefficients of a model associated with the material batch, based on the machining speed, the different machining speed, the first and the second tool life, and G) determining machinability of the material batch based on the model.

Claims

1.-19. (canceled)

20. A computer-aided machining method comprising: A) providing a material batch with an undetermined machinability to a machining tool; B) specifying a set of machining conditions comprising a machining speed; C) inserting a tool into the machining tool, the inserted tool being a predetermined type and having a predetermined wear; D) machining the material batch with the machining tool, monitoring wear of the inserted tool during the machining, and determining a first tool life of the tool; E) repeating Steps B, C and D to determine a second tool life, while setting a different machining speed in Step B and inserting a tool of the same type in Step C; F) determining coefficients of a model associated with the material batch, based on the machining speed, the different machining speed, the first and the second tool life; and G) determining machinability of the material batch based on the model.

21. The method of claim 20, wherein the model is a Taylor model.

22. The method of claim 20, wherein the machining is cutting; and the cutting conditions further comprise at least one of a feed rate and a cutting depth, the method further comprising: determining at least one further tool life of the cutting tool; and determining coefficients of an extended model associated with or describing the material batch; wherein the extended model is an extended Taylor model.

23. The method of claim 21, wherein the machining is cutting; and the cutting conditions further comprise at least one of a feed rate and a cutting depth, the method further comprising: determining at least one further tool life of the cutting tool; and determining coefficients of an extended model associated with or describing the material batch; wherein the extended model is an extended Taylor model.

24. The method of claim 20, wherein, during Step E, a different machining speed is chosen, the rest of the machining conditions are maintained unchanged.

25. The method of claim 21, wherein, during Step E, a different machining speed is chosen, the rest of the machining conditions are maintained unchanged.

26. The method of claim 22, wherein, during Step E, a different machining speed is chosen, the rest of the machining conditions are maintained unchanged.

27. The method of claim 20, wherein using copies of the same tool are utilized during Step C and during Step E.

28. The method of claim 27, wherein the copies are unused copies or copies of the same predetermined wear.

29. The method of claim 20, further comprising: adding the machinability and the coefficients of the model to a knowledge database.

30. The method of claim 20, wherein the tool is one of a drill, a milling tool and a cutting tool.

31. The method of claim 20, wherein the determining of at least of the first tool life and the second tool life is performed without removing the tool from the machining tool.

32. The method of claim 20, wherein Step D includes sub-steps comprising: D1) halting the machining at least once at regular intervals; D2) acquiring at least one image of the tool, while the machining is halted; and D3) determining the wear of the tool based on the at least one image of the tool.

33. The method of claim 32, wherein Step D3 includes sub-steps comprising: D31) segmenting the at least one image of the tool via a trained image segmentation model; D32) calculating a flank wear width of the tool; and D33) determining whether the flank wear width meets a pre-defined flank wear width threshold associated with the wear of the tool.

34. The method of claim 33, wherein the image segmentation model is pre-trained on a training dataset comprising images of tools, each image being associated with at least one tool characteristics and/or defects.

35. The method of claim 33, wherein the training is performed by providing images of tools comprising images that show the tools laterally, annotating the images in accordance with the characteristics and/or defects of the tools and generating masks for the images, and training a model on the images of tools and generated masks.

36. The method of claim 34, wherein the training is performed by providing images of tools comprising images that show the tools laterally, annotating the images in accordance with the characteristics and/or defects of the tools and generating masks for the images, and training a model on the images of tools and generated masks.

37. The method of claim 33, wherein the image segmentation model is based on a pattern recognition algorithm.

38. The method of claim 34, wherein the image segmentation model is based on a pattern recognition algorithm.

39. The method of claim 35, wherein the image segmentation model is based on a pattern recognition algorithm.

40. The method of claim 20, further comprising: determining a machining parameter based on the machinability of the material batch and machining the material batch.

41. A computer program comprising instructions which, when executed by a computing device associated with a machining tool, cause the computing device and the machining tool to perform the method in accordance with claim 20.

42. A computing device comprising the computer program of claim 41.

43. The computing device of claim 42, wherein the computing device comprises an edge device.

44. The computing device of claim 42, wherein the edge device is connected to a numerical control device.

45. A system for machining comprising a machining tool and the computing device of claim 42 associated with the machining tool.

46. A system for machining comprising a machining tool and the computing device of claim 43 associated with the machining tool.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The above and other objects and advantages of the invention will be apparent upon consideration of the following detailed description of certain aspects indicating only a few possible ways which can be practiced. The description is taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which:

[0051] FIG. 1 illustrates a system for machining a material batch;

[0052] FIG. 2 shows a flowchart of a machining method; and

[0053] FIG. 3 shows a machining process of determining a tool life.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

[0054] The reference signs used in the figures and in the claims are used for demonstrative purposes and shall not be considered as a limitation of corresponding claim features.

[0055] FIG. 1 illustrates a system upon which the methods disclosed herein can be implemented. The system comprises a machining tool MT for machining, e.g., subtractive machining, such as milling, drilling, and/or cutting. In particular, the machining tool MT is a CNC machining tool.

[0056] In the machining tool MT there is (a piece of) a material batch M that is to be machined by a tool TL inserted in the machining tool MT. The tool can be, e.g., a cutting tool.

[0057] The machining tool MT usually comprises one or more spindles (not shown here for simplicity) that are driven by feed drives, e.g., motors during machining.

[0058] The system further comprises an edge device EDG that is communicatively and, in particular also operatively coupled to the machining tool MT.

[0059] The edge device EDG is adapted and configured to receive machining data from the machining tool and in particular also configured to control the machining tool. In the latter case, the edge device EDG may have a functionality of an NC-device for the machining tool. It (the edge device EDG) can be also established to comprise the NC-device of the machining tool MT.

[0060] The data that the edge device EDG receives may comprise signals for each spindle and feed drive, e.g., position, current, acceleration, and/or torque. Such signals can be NC signals, i.e., coming from the NC device (not shown here for simplicity) of the machining tool MT.

[0061] The system may also comprise a (digital) camera CAM for computer vision purposes. The camera CAM may be carried by the machining tool MT, e.g., for grabbing pictures and/or videos in-situ, e.g., during the machining process or during the pauses in the machining process.

[0062] In particular, the camera CAM can be configured to take images of the tool TL to analyse its wear. To perform the tool wear analysis, the edge device EDG can comprise an executable routine, a software TWA, e.g., in form of an app, that can be run on the edged device and comprises instructions, which when executed by the edge device EDG implement an algorithm that analyses the wear of the tool TL. The algorithm can be based on a function or model that was, for example, previously trained on a predetermined training set of image data associated with tool's wear. In an embodiment, the function or model can perform a best match with images from a database DB associated with the system and in particular with the edge device EDG to determine the wear of the tool TL. For that the images in the database DB can be labelled accordingly. Such images can be also used to train the function or model mentioned earlier, i.e., the predetermined training set can comprise or consist of images of/from the database DB. The term predetermined may refer to the labels or label values of the images that are chosen for the training. For example, only images whose label has a flank wear value above (or below) some determined threshold may be chosen to form the training dataset.

[0063] Often, edge devices EDG considered in the context of the present disclosure are regarded as industrial computers. It means they comprise all usual functionalities of such industrial computers, interfaces such as communication interfaces, software and so on and so forth.

[0064] With respect to FIG. 2, the machining method disclosed herein begins by providing a material batch with an undetermined machinability to a machining tool (Step A). The material can be a metal or a metal alloy.

[0065] This can be performed fully automatically. For example, the edge device can be in communication with a robot R and be configured to command the robot R to grab a piece of a material M and to put it in to the machining tool MT.

[0066] It will be appreciated by the skilled person that two-way arrows mean two-way communication.

[0067] The edge device EDG can also comprise a software module or a program NIM configured for determining whether the material batch M has novel behaviour, i.e., its machinability is unknown implying that the optimal machining parameter for this material batch M are to be determined.

[0068] This novelty identification module NIM can be based on a trained model (e.g., a novelty detection model). In particular, the novelty detection model can be trained on a set of historic data associated with a respective machining state so that the novelty detection model computes a score (e.g., a novelty score), and yields high scores for data points, i.e., data points (current, voltage, axis and/or tool centre point positions, torque) at a particular time, which match the distribution of the historic data and low scores for data points that do not match the distribution. In other words, novelty of a sample M of the material batch can be expressed by the novelty score. Novelty scores that are above some predetermined threshold but within a decision boundary, thus considered known, while samples that fall outside the decision boundary are considered novel. In this way, the novelty of the material batch M can be determined fully automatically.

[0069] The machining state can be defined as a plurality of (all) machining parameters that are adjustable fully automatically or by an operator (e.g., axis feed, etc.)

[0070] Otherwise, the novelty of the material batch M can be determined by an operator of the machining tool MT. The material batch can be simply marked as having an undetermined machinability.

[0071] In what follows, a particular example of cutting will be considered. However, in view of the above, it will be appreciated that this example can be extended to any type of machining mentioned with respect to the machining tool MT.

[0072] Referring to FIG. 2, the next step of the method (Step B) constitutes specifying a (first) set of machining (here cutting) conditions c1 comprising a machining (here cutting) speed. This can be performed either fully automatically based, e.g., on a historical data that may comprise an expert's knowledge, or by an operator. For example, the operator can asked to choose an initial set of cutting conditions c1.

[0073] Given the set of initial cutting conditions c1, a fresh cutting tool TL is inserted (Step C). The insertion itself can be performed fully automatically by the machining tool MT, or the operator can initiate the procedure of inserting the tool TL. It will be appreciated that the cutting tool TL does not have to be fresh or unused. It must, however, have a predetermined or known type and must have a predetermined degree of wear.

[0074] In a further step (Step D), the material batch M is machined with the cutting tool. Throughout the machining process, the monitoring of the tool and the analysis of its wear is executed, where the process is stopped once the cutting tool is worn out and, therefore, the tool life Tc1 of the tool is determined. That is, in order to perform the tool wear analysis, the edge device EDG with the app TWA can be used.

[0075] In an embodiment, the cutting process is halted to take an image of the tool with the camera CAM. It will be appreciated by the skilled person that the cutting process can be halted several times to analyze the wear of the tool TL. Preferably, the process is halted at regular intervals. The image acquisition can be performed without removing the tool TL from the machining tool MT. Based on the acquired image of the tool, the wear of the tool can be determined by the tool wear analysis software TWA. As mentioned above, the tool wear analysis software TWA can comprise a trained function or perform a best match comparison to determine the wear of the tool TL.

[0076] In an embodiment, determining the tool wear based on the at least one image of the tool comprises segmenting the at least one image of the tool by a trained image segmentation model, calculating a flank wear width of the tool, and determining whether the flank wear width meets a pre-defined flank wear width threshold associated with the wear of the tool.

[0077] The image segmentation model can be trained, as explained above, on a predetermined dataset of images, where each image is associated to a characteristic and/or defect of the tool T, e.g., flank wear, chipping, and/or breakage. The image segmentation model can be based on a pattern recognition algorithm. Once the measured flank wear width meets a predefined threshold v.sub.b,th, the tool is considered worn out and the tool life Tc1 is determined.

[0078] While machining, a tuple 1 of ground-truth data can be yielded consisting of the selected cutting conditions c1, the respective achieved tool life Tc1, and the feature vectors Xc1 computed from the observed process data. By doing this, initial information about the machinability of the novel material batch M at c1 is obtained. For example, the tuple 1 can be stored in the database DB to train/improve model(s) that is (are) used in the novelty identification module NIM and/or in classifiers (as in DE 10 2020 201 077 A1) of material batches.

[0079] In an embodiment, the operator can be asked, whether there is already enough information to determine a model that can be used to determine machinability of the material batch. It will be appreciated by the skilled person that the step can also be performed in a fully automatic way. In FIG. 2, n denotes the number of performed runs performed during the machining process, e.g., the number of the tool lives measured, whereas N denotes the number of the runs needed to derive a model.

[0080] For example, a simple Taylors model can be used. To enable process optimization based on Taylors model, at least two support points, thus tool life at different cutting conditions, are needed.

[0081] Therefore, in the next step (Step E) another fresh or unused cutting tool is inserted, where the other cutting tool is of the same tool type as the previous cutting tool. It will be appreciated by the skilled person that another tool does not have to be new but has to be of a predetermined wear.

[0082] Further, the material batch is machined by using another cutting conditions c.sub.2 including cutting speed v.sub.c2 and the wear of the another tool is monitored to determine a second tool life Tc2.

[0083] In an embodiment, the second cutting conditions c.sub.2 differ from the first cutting conditions c.sub.1 only in the cutting speed v.sub.c, while the remaining cutting conditions are kept constant.

[0084] In the next step (Step F), parameters of the Taylors model are determined based on the first and second cutting speeds (v.sub.c1 and v.sub.c2) and the first and the second tool life (Tc1 and Tc2). The calculated parameters determine the Taylors model associated with the material batch M that has been machined as described above.

[0085] In other words, a machinability model for the unknown material batch is established. This model can be stored in the database DB for further use.

[0086] In the next step (Step G), the machinability of the material batch M can be determined based on the Taylor model.

[0087] The above is with regards to the simplest case of the Taylor model that needs only two support points. In this case, only two runs during the machining process are needed.

[0088] In an embodiment, an extended Taylor model can be used that includes other cutting conditions, e.g., feed rates f and cutting depths a.sub.p, as variables.

[0089] The extended Taylor model with coefficients m, n and q:

[00001]$\ln T=\ln c_t+m\ln v_c+n\ln f+q\ln a_p$

[0090] In order to determine the parameters of the extended Taylor model, more support points N, i.e., more cutting runs during the machining process, are needed. The number of the support points depends on the number of model parameter coefficients.

[0091] Different feed rates f and/or cutting depths a.sub.p can be selected only when using the extended Taylor model.

[0092] Once the minimum amount N of support points are generated, these can be used to compute the respective model parameters of the Taylor model.

[0093] In an embodiment, a dataset comprising tool life and cutting conditions v.sub.c, f, and a.sub.p can be used to optimize a linear regression model using least squared error to approximate the coefficient parameters c.sub.t, m, n, and q of the transformed extended Taylor model.

[0094] The coefficient parameters can then be added to the knowledge base DB as new instances for novelty detection and/or better classification models for further training (see FIG. 2).

[0095] In an embodiment, the initial set of cutting conditions (c) is kept constant for the remainder of the cut to ensure a constant machining state, when the tool life is determined.

[0096] Turning to FIG. 3, a machining process for determining a tool life is illustrated in more detail. FIG. 3 illustrates how a single support point is acquired.

[0097] As discussed above, the cutting process begins at t.sub.0 and can be halted at regular intervals t.sub.1, t.sub.2, . . . t.sub.n. When the cutting process is halted, the tool wear analysis TWA is performed. The cutting tool is moved in front of the image acquisition unit CAM (see FIG. 1), or the image acquisition unit CAM is moved into position to acquire an image of the tool TL. A picture of the cutting tool is obtained, segmented by the image segmentation model, and used to calculate the flank wear width v.sub.b,1. The flank wear v.sub.b,1 is compared to a predefined threshold v.sub.b,th. If the threshold is reached, then the tool is worn out. The above-described procedure is repeated every time when the machining process is interrupted. The flank wear values v.sub.b,2, . . . v.sub.b,n are measured/determined and compared to the threshold value v.sub.b,th until this value is reached. When the threshold value v.sub.b,th is reached the total machining time t.sub.m defines the tool life T.

[0098] In an embodiment, the set of all detected wear defects can be stored in the database DB and/or shown to the operator for manual machinability assessment. In parallel, the feature vectors (shown as circles FIG. 3) and, preferably, the total machining time t.sub.m computed from the process data are stored, e.g., in the database DB.

[0099] At the end of the process, when the tool life T is determined, the set of ground-truth information consisting of the tool life reached T at cutting conditions c with the observed feature vectors X is provided and, preferably stored in the database DB.

[0100] In summary, the disclosed embodiments of the method can be fully automated material characterization routines or automated material characterization routines that guide the operator through the automated procedure for characterizing the machinability of a novel material batch.

[0101] The disclosed embodiments of the methods and systems allow integration of the steps of material batch characterization into the machining process, because not all material batch variations are known prior to machining. By integrating material batch characterization into the machining process, testing efforts, thus costs, can be reduced. Furthermore, a technical system is thereby capable of assessing novel material batches on its own without the need for consulting a technical expert and asking her or him to characterize the material batch of the unknown machinability.

[0102] After the novel material batch is characterized, a proposal of optimized machining parameters can be made to the operator or adopted by the control of the machining tool.

[0103] It will be appreciated by the skilled person that there are several approaches for describing the machinability of a material.

[0104] In this disclosure, the machinability assessment based on tool life is used as ground truth machinability data. For this method of machinability determination, the wear of the cutting tool is monitored in relation to the tool life. As the rate of tool wear typically depends on the cutting conditions, at least the machining speed needs to be considered. While the usage time of the tool can be derived from the meta data of the machining operation, the tool condition/tool wear can be acquired using a visual tool condition monitoring system.

[0105] The above-described embodiments of the present disclosure are presented for purposes of illustration and not of limitation. In particular, the embodiments described with regard to figures are only few examples of the embodiments described in the introductory part. Technical features that are described with regard to systems can be applied to augment methods disclosed herein and vice versa.

[0106] Thus, while there have been shown, described and pointed out fundamental novel features of the invention as applied to a preferred embodiment thereof, it will be understood that various omissions and substitutions and changes in the form and details of the methods described and the devices illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit of the invention. For example, it is expressly intended that all combinations of those elements and/or method steps that perform substantially the same function in substantially the same way to achieve the same results are within the scope of the invention. Moreover, it should be recognized that structures and/or elements and/or method steps shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.