CAD-CAM CNC Program Generation Adapted to Customer Machining Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating machine numerical control data sets for machine tools are inefficient and fail to adapt to specific customer environments, leading to unstable and unreliable machining processes.
Innovation Solution
A computer-implemented method using a trained machine learning algorithm that adapts to customer-specific usage environments by incorporating additional training data sets, allowing for the automatic generation and optimization of CNC programs for machining processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design and programming methods are used, then flexibility in adapting to specific circumstances is maintained, but productivity and reliability are reduced due to human error and inconsistency
Solution Approach 1:
The system enables self-service through automated machine learning algorithms that continuously learn from operational data and automatically optimize control data set generation without requiring manual intervention for each new component, thereby improving both reliability through consistency and productivity through automation
Solution Approach 2:
The patent replaces the mechanical/manual system of design and programming with an automated machine learning-based system that processes component data and generates control data sets algorithmically, eliminating human error while maintaining adaptability through continuous learning from usage data
2Reliability
If generic control data sets are used without adaptation, then productivity is maintained through standardized processes, but reliability deteriorates due to lack of optimization for specific customer environments
Solution Approach 1:
The system dynamically changes parameters of the machine learning model based on customer-specific usage data, adjusting the control data generation process to optimize for each customer's specific machine tools, materials, and operational requirements while maintaining a standardized underlying platform
Solution Approach 2:
The system performs preliminary learning and adaptation by collecting usage data and training the machine learning model in advance for each customer environment, so that when new components need control data sets, the system is already optimized for that specific customer context
3Ease of operation
If extensive expertise is required for manual design and programming, then adaptability to specific circumstances is improved, but ease of operation deteriorates due to the need for highly trained personnel
Solution Approach 1:
The system performs self-learning and self-optimization through automated machine learning algorithms that continuously improve their performance based on usage data, eliminating the need for highly trained personnel while maintaining high adaptability to specific customer environments and operational requirements
Solution Approach 2:
The system implements continuous feedback loops where operational data from machine tools is collected, used to retrain and refine the machine learning models, and then applied to improve future control data set generation, enabling the system to adapt automatically without human expertise intervention
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to a computer-implemented method for creating machine-numerical control data sets (17) for controlling machine tools (9) in a usage environment (3), which method comprises the steps of: receiving (step 201) a first component data set (13), which constitutes a digital design model of a first component (21A); creating (step 203) a first machine-numerical control data set (17) for the first component data set (13) using control program generation software, which uses, in an evaluation routine, a trained algorithm (11) for machine learning having trained parameters (Pi); combining (step 205) a first additional training data set (31) from the component data set (13) and the created machine-numerical control data set (17) for a usage-environment-specific training database (119); updating (step 209) the algorithm (11) for machining learning by setting usage-environment-specific values for the parameters (Pi), the usage-environment-specific values having been determined by training the training algorithm (111) for machine learning with the usage-environment-specific training database (119); receiving (step 211) a second component data set (13A), which constitutes a digital design model of a second component (21A); and creating (step 213) a second machine-numerical control data set (17A) for the second component data set (13A) using the control program generation software and running through the evaluation routine, wherein the algorithm (11) for machine learning which has been updated with respect to the parameters (Pi) is used.