Wire EDM Controller Path Correction for Corner Precision
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Solution Overview
Problem
Conventional wire electrical discharge machining techniques fail to accurately correct machining paths in corner and arc portions, leading to shear drops and reduced shape precision, often requiring increased machining time and operator intervention.
Innovation Solution
A machine learning device is integrated into a controller to learn and optimize machining paths, machining conditions, and environments, using state observation, determination data acquisition, and reinforcement learning to prevent shear drops without significantly increasing machining time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional machining condition control is used to reduce wire electrode deflection in corner portions, then manufacturing precision is improved, but productivity deteriorates due to decreased machining speed
Solution Approach 1:
The machining path is corrected in advance before machining begins, using pre-calculated deflection compensation values based on the workpiece shape data. This eliminates the need to slow down machining speed during corner portions, as the deflection compensation is already built into the machining path. The numerical control device performs path correction calculations beforehand, allowing high-speed machining to proceed without real-time speed reductions.
Solution Approach 2:
The invention replaces the conventional mechanical approach of reducing machining speed to compensate for deflection with a computational approach. Instead of physically slowing down the wire electrode through mechanical means, the system uses numerical calculations to pre-determine and correct the machining path, substituting mechanical speed control with computational path planning.
2Ease of operation
If machining path correction is performed based on rule of thumb by experienced operators, then ease of operation is improved, but manufacturing precision deteriorates for complex corner shapes
Solution Approach 1:
The numerical control device performs automatic machining path correction using built-in computational algorithms that analyze the workpiece shape data and calculate appropriate deflection compensation values. This eliminates the need for operator intervention and rule-of-thumb methods, allowing the system to self-correct the machining path based on objective mathematical calculations rather than subjective operator judgment.
Solution Approach 2:
The system incorporates feedback mechanisms where the actual machining results and deflection patterns are used to refine and update the path correction values. The numerical control device continuously improves its path correction accuracy by learning from machining outcomes, ensuring progressively better precision for complex corner shapes without requiring operator expertise.
Data Source
AI summary
A machine learning device provided in a controller for controlling a wire electrical discharge machine uses state variables (including data relating to a correction amount, a machining path, machining conditions, and a machining environment) observed by a state observation unit and determination data acquired by a determination data acquisition unit to machine-learn a correction for a machining path. Using the learning result, the machining path can be corrected automatically and accurately on the basis of a partial machining path, the machining conditions and the machining environment of the machining performed by the wire electrical discharge machine.