EP-3982211-B1 - METHOD AND DEVICE FOR SIMULATING PROCESSING ON A MACHINE TOOL BY MEANS OF A SELF-LEARNING SYSTEM

EP3982211B1EP 3982211 B1EP3982211 B1EP 3982211B1EP-3982211-B1

Inventors

KUHN, TOMMY
NIEDERWESTBERG, Daniel

Dates

Publication Date: 20260513
Application Date: 20211008

Claims (14)

Computer-implemented method for simulating a machining process of a workpiece on a machine tool as a function of NC data and/or PLC data, wherein a digital machine model of the machine tool is used to simulate the machining process, the method comprising the steps of: - executing a digital machining process by simulating the machining process on the digital machine model in a simulation section (SA) based on input NC data and/or PLC data and storing the simulation data; - recording machining data (R1) of the machining process on the machine tool, wherein the machining process is carried out as a function of the input NC data and/or PLC data, wherein the NC data and/or PLC data used to control the machine tool are also used to move the simulated elements of the digital machine model; - feeding the simulation data of the digital machining process and the machining data of the machining process on the machine tool to an analysis section (AA) and linking the simulation data and the machining data; - after ensuring the quality of the simulation results: adapting the machining process on the machine tool on the basis of the simulation data, wherein the analysis section (AA) comprises a machine learning device for analyzing the machining process based on the linked data and wherein the analysis section (AA) outputs the result of the analysis, wherein an output data set of the analysis section includes simulation change parameters and is fed back to the simulation section, wherein the machining process simulation is optimized on the basis of the output data set, characterized in that for linking the simulation data with the machining data, sensor data of the machine tool are assigned to the corresponding analysis data of the simulation section (SA) via a continuous data mapping, based on a temporal assignment of the corresponding operations, the NC lines and/or the axis positions.
Computer-implemented method according to claim 1, wherein the analysis section (AA) has a data linking section AADV, in which process parameters of the simulation data (R2) of the digital machining process and process parameters of the machining data (R1) of the machining process on the machine tool are linked to one another and are used to teach the machine learning device.
Computer-implemented method according to claim 1 or 2, wherein the output data set (F1) of the analysis section (AA) is fed back to the simulation section for adapting the digital machining process.
Computer-implemented method according to one of the preceding claims, wherein the simulation of the machining process, the feeding of the simulation data (R2) into the analysis section (AA), the feeding back of the output data set (F1) to the simulation section (SA) and the changing of simulation parameters in the simulation section (SA) on the basis of the output data set (F1) are formed in a program loop for continuously adapting the simulation of the machining process.
Computer-implemented method according to at least one of the preceding claims, wherein at least one simulation parameter is changed within the simulation section (SA) on the basis of the output data set (F1) of the analysis section (AA), and the simulation data (R2) generated from the simulation or at least one process parameter of the simulation data (R2) are stored in a simulation database (DB2) before the feeding into the analysis section (AA).
Computer-implemented method according to at least one of the preceding claims, wherein at least one corresponding process parameter exists within the simulation data (R2) for each process parameter within the machining data (R1) and/or is generated within the simulation process and assigned to the respective process parameters of the machining data (R1), and/or wherein the machining data (R1) and simulation data (R2) are compared within the analysis section (AA) and input parameters for the machine learning device are defined by means of a comparison of the machining data (R1) and simulation data (R2).
Computer-implemented method according to at least one of the preceding claims, wherein the digital machining process is carried out temporally parallel to or before the machining process on the machine tool and in this case a real-time output of performance data of the current machining process is made possible by outputting the simulation data and machining data to the analysis section, and preferably instructions for optimizing the machining process are output on the machine tool.
Computer-implemented method according to at least one of the preceding claims, wherein the machine learning device is an artificial neural network (AAKI) configured to optimize the simulation parameters of the simulation of the machining process such that there is as minimal a difference as possible between the selected process parameters of the machining data (R1) and the simulation data (R2).
Computer-implemented method according to at least one of the preceding claims, wherein the learning of the machine learning device and the optimization of the simulation process is carried out by the machine learning device in parallel and/or independently of the machining process on the machine tool.
Computer-implemented method according to at least one of the preceding claims, wherein the output data set (F1) output by the machine learning device is stored in an expandable technology database (DB3) and the machine learning device resorts to the output data sets (F1) stored in the technology database (DB3) for feedback of the learning process; and/or wherein the same NC data are used for the machining process on the machine tool and for the simulation of the machining process on the digital machine model to align the working steps between the machining process and the simulation process; and/or wherein physical parameters of the machine tool, of the tools and of the workpiece to be machined are output by the simulation of the machining process on the digital machine model, and the physical parameters of the machine tool, of the tools and of the workpiece to be machined are defined as a function of the time of the machining process and/or of the respective working step.
Computer-implemented method according to at least one of the preceding claims, wherein the NC data are provided with an additional marker to identify the respective working step, and the machine tool and the digital machine tool model can be interpreted with the aid of the marker within the NC data, as a result of which they can reconstruct in which working step and/or in which position the machine tool and/or the digital machine tool is located at a determinable time.
Computer-implemented method according to at least one of the preceding claims, wherein the working steps of the machining process are additionally output as structure data (G3) for interpreting the working steps in other simulation devices, and the machine tool and the digital machine model can extract and implement working steps and/or process information from other data formats via a parser; and/or wherein the structure data (G3) for interpreting the working steps in other systems are output as an XML file or as a STEP file.
Apparatus for controlling a machining process of a workpiece by means of a machine tool as a function of NC data and/or PLC data, comprising - a machine tool for machining the workpiece using specified NC data and/or PLC data, - a simulation device configured to be controlled independently of the machine tool, for simulating the machining process on a digital machine model based on the specified NC data and/or PLC data, - an analysis unit connected to the machine tool and the simulation device for adapting simulation parameters within the simulation device, wherein the machine tool is configured to transmit machining data (R1) of the machining process on the machine tool to the analysis unit, and the simulation device is configured to transmit simulation data (R2) of the machining process simulated on the digital machine model to the analysis unit, and the analysis unit (AA) is configured to learn the behavior of the machine tool, of the tool and/or of a workpiece to be machined of a machine learning device arranged in the analysis unit by means of transmitted machining data (R1) and simulation data (R2) and to output the result of an analysis of the machining process of the machine learning device, wherein the apparatus is configured to execute a method according to one of claims 1-12.
Apparatus according to claim 13, wherein the machine tool, the simulation device and the analysis unit are configured to mutually transmit data, in particular parameter data and/or performance data and/or hardware data and/or program data, wherein the data are transmitted by means of an intranet and/or by means of the Internet; and/or wherein the simulation device and the analysis unit are independent of the machining process on the machine tool, and the analysis unit is configured to continuously match the machining process on the digital machine model to the machining process of the machine tool by means of transmission of the output data set (F1) of the machine learning device to the simulation device.

Description

The present invention relates to a device and a method for simulating a machining process of a workpiece on a machine tool by means of a self-learning artificial neural network, in which the artificial neural network can obtain process and parameter data of the machining process from both a real machine tool and a digital machine model and uses it to optimize the simulation and/or the real machining process. Background of the invention Due to the continuous increase in complexity of today's workpiece machining processes, particularly in the area of machine-assisted or automated machining, new machine tools are typically confronted with a multitude of increased qualitative and economic demands. Increasingly sophisticated process mechanics require more powerful and/or precise machine kinematics, which, while accompanied by improved functions of the machine mechanics, drives, or control systems, also leads in most cases to increased setup times and difficult, lossy, and, above all, costly test runs. A machine tool simulation preferably replicates the course of the respective workpiece machining process on a digital machine tool model. For this purpose, various mechanical models, such as multi-mass models, geometric kinematics, or finite element models, are usually used to describe the physical properties and interactions of the machine elements and workpieces, and are combined with control software for The movement of the machine elements is combined. Furthermore, process simulation based on a penetration calculation between the workpiece and the tool can also be used advantageously. In EP 1 901 149 B1 A machine simulation for defining a process for machining a workpiece on a machine tool is shown, in which in particular a data structure has been integrated into the simulation that makes it possible to integrate data or behavior of elements recorded by sensors on real machine tools and thus to further improve the control description of the implemented machine model. In WO 2012/168427 A1 Furthermore, a machine simulation of a work process on a machine tool using a virtual machine is shown, in which CNC-controlled sub-processes are divided among different processor cores acting in parallel and can thus be calculated in parallel to accelerate the simulation processes. DE 11 2018 005 809 T5 reveals an adaptation of a machine model based on a comparison between signals from a machine and the machine model. However, in state-of-the-art simulations of machine tools within a tool machining process, the problem always arises that an accurate specification of all state parameters of the machine tool, the tool used and/or the workpiece required for the simulation, especially the time course of the physical properties of the latter, is not possible without a great deal of effort. One object of the invention is therefore to provide a method for simulating a machining process of a workpiece on a machine tool and a device for simulating such a machining process, which solve the aforementioned problems from the prior art and which in particular make it possible to adapt the digital machine model within the process simulation to the conditions and properties of the real machine tool as efficiently, cost-effectively, and quickly as possible, and/or to improve it. Furthermore, it is a task to optimize the adaptation of the simulation and the associated simulation parameters so that it can be carried out as automatically as possible and thus independently of human error. Detailed description of the invention To solve the aforementioned problems, the features of the independent claims are proposed. The dependent claims relate to preferred embodiments of the present invention. The invention discloses a method and a device for simulating a machining process of a workpiece on a machine tool. These devices are configured to collect comparative data between the simulated and the real machining process by generating simulation data through a simulation section of the method, in which the machining process is simulated on a digital machine model, and by recording machining data of the machining process on the real machine tool in an independent manufacturing section of the method. This comparative data is then fed to an artificial intelligence (AI) implemented in an analysis section of the method to improve the effectiveness of the simulation. Advantageously, an artificial neural network is used as the artificial intelligence, and the data model for storing the simulation data is particularly advantageously designed as a continuous-time data model. The control of the digital machine model and the real machine tool is carried out depending on previously defined NC and/or PLC data, and the analysis section is configured so that it is carried out by the feeder The AI uses simulation and machining data to train the behavior of the machine tool, at least one tool, and/or the workpiece, and outputs simulation parameters as a data set for mo