Workpiece Model Correction Learning for Precision Machining
Find Innovative SolutionsGenerate Solutions
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, simplifying the correction process and improving precision through automated learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine correction amounts, then flexibility in handling various machining conditions can be maintained, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs self-learning by automatically observing machining states and measurement results, then autonomously determining correction amounts without requiring manual intervention. The learning section accumulates data and generates correction amounts independently, enabling the system to serve itself in the correction determination process.
Solution Approach 2:
The patent replaces manual mechanical correction determination with an automated learning-based system. The learning section uses accumulated machining state data and measurement results to automatically calculate correction amounts, substituting human operators and manual processes with an intelligent automated system.
2Ease of operation
If correction amounts are determined manually under various machining conditions, then adaptability to different conditions can be achieved, but the task becomes extremely complex and time-consuming
Solution Approach 1:
The system performs preliminary learning by accumulating machining state data and measurement results in advance. The learning section builds a knowledge base from historical data, enabling quick correction determination when actual machining occurs. This preliminary data accumulation and learning process eliminates the need for time-consuming manual analysis during production.
Solution Approach 2:
The system implements feedback by using measurement results of machining errors to continuously improve correction amounts. The learning section analyzes the relationship between machining states, measurement outcomes, and correction effectiveness, automatically adjusting and optimizing correction strategies based on accumulated feedback from actual machining operations.
3Manufacturing precision
If learning is performed based on a huge data set, then high precision correction can be achieved, but data processing complexity increases
Solution Approach 1:
The system segments the huge data set into structured components including machining state data, measurement results, and correction amounts. The learning section processes this segmented data systematically, organizing it into manageable categories that can be analyzed efficiently. This segmentation enables high-precision learning without overwhelming the processing system.
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.