Adaptive CNC Control Data Generation for Customer-Specific Machining
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Solution Overview
Problem
Current CAD/CAM systems require significant expert knowledge for designing and programming machine tools, leading to inefficiencies and errors in machining processes, particularly when adapting to specific customer environments and material properties.
Innovation Solution
A computer-implemented method using a trained machine learning algorithm to generate and optimize computerized numerical control (CNC) data sets, which updates parameters based on usage-environment-specific training data, enabling automated and adaptive CNC program creation for machine tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert knowledge is used to design and program machine tools, then machining quality and reliability are improved, but productivity decreases and errors increase due to manual inefficiencies
Solution Approach 1:
The system enables self-service by allowing the machine tool and CAD/CAM system to automatically generate and optimize CNC programs without requiring expert manual intervention. The machine tool controller reads component data sets, generates control data sets autonomously, and iteratively optimizes parameters based on feedback, replacing the need for expert programmers while maintaining high reliability through automated quality checks
Solution Approach 2:
The patent replaces the mechanical system of manual expert programming with an automated computational system. Instead of relying on human experts to manually create CNC programs, the system uses computer-based algorithms to automatically generate, simulate, and optimize control data sets, substituting human cognitive work with automated software processes that improve both productivity and consistency
2Productivity
If standard CNC programs are used, then productivity increases, but adaptability to specific customer environments and material properties decreases
Solution Approach 1:
The system implements dynamics by creating adaptable CNC programs that can automatically adjust to different customer environments and material properties. The machine tool controller reads component data sets, generates appropriate control data sets for specific machining conditions, and iteratively optimizes parameters based on feedback from the actual usage environment, allowing the system to dynamically adapt rather than relying on fixed standard programs
Solution Approach 2:
The patent applies parameter changes by automatically adjusting CNC program parameters based on the specific component data set and usage environment. The system reads component data sets, generates control data sets with appropriate parameters for the specific material and machine tool, and iteratively optimizes these parameters based on feedback, enabling the same system to handle diverse materials and conditions without sacrificing productivity
3Adaptability or versatility
If manual adaptation of CNC programs is performed, then adaptability to specific conditions is improved, but time consumption and errors increase
Solution Approach 1:
The system performs self-service adaptation by automatically adjusting CNC programs based on the specific component data set and usage environment. The machine tool controller autonomously reads component data, generates appropriate control data sets, and iteratively optimizes parameters based on feedback without requiring manual intervention, achieving both adaptability and time efficiency simultaneously
Solution Approach 2:
The patent implements feedback mechanisms where the system reads component data sets, generates control data sets, executes machining operations, and uses the results to iteratively optimize the CNC programs. This automated feedback loop enables the system to adapt to specific conditions automatically, eliminating the time-consuming manual adaptation process while maintaining high adaptability to different materials and environments
Data Source
AI summary
A method creates numerical control data sets for controlling machine tools. The control data sets are read from the machine tools. A first component data set representing a first component design model is received. A first numerical control data set is created for the first component data set using control program generation software, having an assessment routine using a trained machine learning algorithm with settable parameters. A first additional training data set is compiled from the component data set and the created numerical control data set. The first additional training data set is output to a training database. The machine learning algorithm is updated by setting usage-environment-specific values for the parameters determined by training the machine learning training algorithm using the training database.