NC Machining Parameter Learning for Faster Program Generation
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Solution Overview
Problem
The complexity of machining programs for numerically controlling machine tools requires adjusting a wide variety of parameters, making the generation process time-consuming and effort-intensive.
Innovation Solution
A machine learning device that extracts first and second parameters from existing machining programs, using a data extraction unit and machine learning unit to learn the value of the first parameter based on a dataset, facilitating the generation of machining programs through a learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine tool structures are made complicated with more axes to precisely create complicated shapes, then manufacturing precision is improved, but device complexity increases and the number of processes to be numerically controlled increases
Solution Approach 1:
The patent replaces manual mechanical parameter adjustment with an automated information processing system. The machining program generation unit automatically generates NC programs by extracting machining conditions from CAD data and determining optimal parameters, substituting the mechanical adjustment process with computational algorithms that analyze geometric data and generate control code without manual intervention.
Solution Approach 2:
The system enables self-service by allowing the machining program generation unit to autonomously determine machining parameters and generate complete NC programs without requiring operator intervention. The unit automatically processes CAD data, selects appropriate machining conditions, and outputs ready-to-execute programs, making the system self-sufficient in program creation.
2Manufacturing precision
If a wide variety of parameters are adjusted to generate machining programs for complicated shapes, then manufacturing precision is improved, but the time and effort required increases
Solution Approach 1:
The patent replaces the manual mechanical process of parameter adjustment with an automated computational system. The machining program generation unit uses algorithms to automatically determine optimal machining parameters based on CAD data, eliminating the time-consuming manual adjustment process while maintaining or improving parameter quality through systematic analysis.
Solution Approach 2:
The patent introduces a machining program generation unit as an intermediary between CAD data and NC program output. This intermediary automatically processes geometric data, determines machining conditions, and generates control programs, serving as a bridge that eliminates the need for manual parameter adjustment and reduces generation time while ensuring quality results.
3Ease of operation
If manual parameter adjustment is used to generate machining programs, then ease of operation is maintained, but productivity decreases due to time-consuming processes
Solution Approach 1:
The system achieves self-service by enabling the machining program generation unit to autonomously complete the entire program generation process. Operators simply input CAD data and receive automatically generated NC programs with optimized parameters, eliminating manual adjustment steps while maintaining operational simplicity and dramatically improving generation efficiency.
Solution Approach 2:
The system performs preliminary action by pre-calculating and storing optimal machining parameters and conditions in databases before actual machining operations. The machining program generation unit retrieves and applies these pre-determined parameters automatically, eliminating the need for real-time manual adjustment and enabling rapid program generation without sacrificing optimization quality.
Data Source
AI summary
A machine learning device includes a data extraction unit that extracts first and second parameters from a plurality of machining programs. The machining programs numerically control a machine tool. The first parameter is a parameter to be adjusted, and the second parameter is a parameter used to adjust the first parameter. The machine learning device also includes a machine learning unit that learns a value of the first parameter according to a data set that includes the first and second parameters.