Machining State Learning for Workpiece Model Shape Correction
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Solution Overview
Problem
Existing machine learning technologies for robot operations struggle to accurately correct workpiece models to match target shapes, leading to errors in machining processes, requiring manual intervention and inefficient error reduction.
Innovation Solution
A machine learning apparatus that observes machining state data and error measurements to learn an optimal correction amount for workpiece models, using state variables to automatically determine the necessary adjustments, thereby simplifying the correction process and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to determine correction amounts for workpiece models, then flexibility and adaptability are maintained, but the process is time-consuming and lacks precision
Solution Approach 1:
The patent replaces manual mechanical determination methods with an automated learning section that uses machine learning algorithms. The learning section automatically determines correction amounts by learning from machining state data and error measurements, eliminating the need for manual intervention while improving both speed and precision.
Solution Approach 2:
The system enables self-service by allowing the learning section to autonomously determine correction amounts without human intervention. The apparatus learns from accumulated data and automatically adjusts workpiece models, making the system self-correcting and eliminating dependency on manual operations.
2Productivity
If automated learning is used to determine correction amounts, then speed and precision are improved, but system complexity increases
Solution Approach 1:
The learning section serves multiple functions: it learns from machining state data, measures errors, determines correction amounts, and updates workpiece models. This multi-functionality consolidates what could be separate complex systems into a single integrated component, improving productivity while managing complexity.
Solution Approach 2:
The learning section acts as an intermediary between the machining system and the workpiece model correction process. It receives raw machining state data and error measurements, processes this information through machine learning, and outputs correction amounts, thereby simplifying the overall system architecture while enabling automated high-speed operation.
3Manufacturing precision
If correction amounts are determined without learning from machining state data, then the system is simpler, but accuracy and adaptability to various machining conditions deteriorate
Solution Approach 1:
The system implements feedback by continuously measuring errors between machined workpieces and target shapes, then using this error information along with machining state data to learn and determine correction amounts. This closed-loop feedback mechanism improves manufacturing precision by adapting to actual machining conditions while the learning process manages the complexity of data processing.
Solution Approach 2:
The learning section performs preliminary learning actions by accumulating and analyzing machining state data and error measurements before actual correction is needed. This preliminary learning enables the system to quickly and accurately determine correction amounts during production, improving manufacturing precision without adding complexity to the real-time correction process.
Data Source
AI summary
A machine learning apparatus capable of reducing an error between a machined workpiece and a target shape when the workpiece is machined based on a workpiece model modeling the target shape of the workpiece. A machine learning apparatus includes a state observation section configured to observe machining state data of a machine tool configured to machine the workpiece, and measurement data of an error between a shape of the workpiece machined by the machine tool based on the workpiece model and the target shape, as a state variable representing a current state of environment in which the workpiece is machined, and a learning section configured to learn the correction amount in association with the error by using the state variable.