Machining Condition Feedback Control for Real-Time Accuracy Tuning
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Solution Overview
Problem
In machining equipment systems, detecting abnormalities in workpieces during inspection requires manual adjustments to machining conditions, disrupting production and lacking real-time calibration capabilities.
Innovation Solution
A machining equipment system incorporating a control device, state obtaining device, inspection device, and machine learning device that performs reinforcement learning to adjust machining conditions based on inspection results and equipment states, improving accuracy and minimizing defects without stopping the equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of machining conditions is performed when abnormality is detected, then machining accuracy can be improved, but production stops and productivity decreases
Solution Approach 1:
The machining equipment system performs self-adjustment of machining conditions through a machine learning device that automatically modifies parameters based on inspection results and equipment state data, eliminating the need for manual intervention and continuous production stoppages
Solution Approach 2:
The system implements a closed-loop feedback mechanism where inspection results and equipment state information are continuously fed back to the machine learning device, which then automatically adjusts machining conditions in real-time to maintain accuracy without stopping production
2Manufacturing precision
If real-time calibration of machining conditions is implemented, then machining accuracy improves, but system complexity increases
Solution Approach 1:
The machine learning device serves multiple functions simultaneously: it analyzes inspection results, processes equipment state data, determines optimal machining conditions, and controls the machining equipment, consolidating what would otherwise require multiple separate systems into a single multi-functional unit
Solution Approach 2:
The machine learning device acts as an intermediary that integrates and processes information from diverse sources (inspection devices, state obtaining devices) and translates it into actionable machining conditions, simplifying the overall system architecture through a central coordinating component
Data Source
AI summary
Provided is a machining equipment system including machining equipment that performs machining of a workpiece; a control device that controls the machining equipment on the basis of a machining condition; a state obtaining device that obtains a state of the machining equipment during the machining; an inspection device that inspects the workpiece after the machining; and a machine learning device that performs machine learning on the basis of a result of inspection by the inspection device and the state of the machining equipment, obtained by the state obtaining device, wherein the machine learning device modifies the machining condition on the basis of a result of the machine learning so as to improve the machining accuracy of the workpiece or so as to minimize the defect rate of the workpiece.