NC Tool Path Generation Using Geometric Learning Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inexperienced operators face difficulties in generating desired tool paths for complex workpieces during NC machining, as existing methods rely heavily on operator experience and know-how.
Innovation Solution
A method and device that utilize machine learning based on geometric information and tool path patterns from multiple known workpieces to automatically generate new tool paths for target workpieces, employing a neural network to input geometric information and output tool path patterns, with visual feedback on machining surfaces to facilitate operator recognition and adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If operator experience and know-how are used to generate tool paths, then the quality of tool path generation improves, but the ease of operation deteriorates for inexperienced operators
Solution Approach 1:
The system copies successful tool path patterns from multiple known workpieces with similar geometric characteristics and automatically applies them to the target workpiece. This eliminates the need for operators to possess extensive experience while maintaining high-quality tool path generation by replicating proven patterns.
Solution Approach 2:
The system transforms the qualitative, experience-based knowledge into quantitative geometric parameters (curvature, area, aspect ratio, etc.) that can be objectively measured and compared. By changing the representation from subjective operator knowledge to objective geometric parameters, the system makes tool path generation accessible to inexperienced operators.
2Measurement precision
If machine learning based on multiple workpiece examples is used, then the accuracy of tool path generation improves, but the device complexity increases
Solution Approach 1:
The system segments the complex task of tool path generation into distinct geometric features (curvature, area, aspect ratio, etc.) and processes each feature independently through the machine learning model. This segmentation simplifies the overall system complexity by breaking down the problem into manageable, independent analysis components.
Solution Approach 2:
The machine learning model automatically learns from multiple known workpiece examples and generates tool paths without requiring manual programming or complex configuration. The system serves itself by autonomously acquiring knowledge from training data and applying it to new workpieces, reducing the operational complexity despite the sophisticated underlying algorithms.
Data Source
AI summary
The step for performing machine learning includes acquiring shape data; acquiring geometric information for each of a plurality of machining faces; acquiring a tool path pattern selected for the machining faces from among a plurality of tool path patterns; and performing machine learning by using the geometric data for known workpieces and the tool path patterns wherein the input is the geometric information for the machining faces and the output is the tool path pattern for the machining faces. The step for generating a new tool path includes: acquiring shape data for the workpiece; acquiring geometric information for each of the plurality of machining faces of the workpiece to be machined; and generating a tool path pattern for each of the plurality of machining faces on the workpiece on the basis of the results of the machine learning using the geometric information of the workpiece to be machined.