Machining Error Correlation Imaging for Workpiece Shape Analysis
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Solution Overview
Problem
There is a need for a technique to easily identify the cause of errors between the shape of a workpiece machined by an industrial machine and its target shape, due to various factors.
Innovation Solution
An image analysis device that generates first image data indicating the distribution of errors between the machined workpiece shape and the target shape, and second image data indicating the distribution of errors between commands and feedback from the industrial machine. The device then obtains a correlation between these two distributions to help identify the cause of the errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operators manually analyze machining errors to identify causes, then they can understand error patterns, but the process is time-consuming and complex
Solution Approach 1:
The patent creates visual copies (images) of error distributions from machining data and command/feedback data. These image representations allow operators to quickly analyze error patterns without manually processing complex numerical data, thereby reducing analysis time while maintaining accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis methods with automated image processing and correlation analysis. The system automatically generates images, calculates correlations, and identifies error causes, substituting operator manual work with automated computational processes.
2Measurement precision
If comprehensive error analysis is performed to identify all possible causes, then accuracy improves, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments the complex error analysis into two distinct image types: one showing machining errors and another showing command/feedback errors. This segmentation allows the system to analyze different error sources separately and then correlate them, reducing overall analytical complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent introduces images as an intermediary representation between raw machining data and error cause identification. These images serve as a bridge that simplifies the correlation analysis process, making it easier to identify error causes without directly handling complex raw data relationships.
3Measurement precision
If detailed error distribution data is collected and analyzed, then measurement precision improves, but the difficulty of detecting and measuring error patterns increases
Solution Approach 1:
The patent uses visual representation where different error magnitudes and patterns are displayed with varying colors and intensities in the generated images. This allows operators to quickly detect error patterns and distributions that would be difficult to identify in raw numerical data, reducing detection difficulty while maintaining measurement precision.
Data Source
AI summary
Various factors may cause an error between the shape of a workpiece machined by an industrial machine and the target shape of the workpiece. An image analysis device includes a first image generating section that generates first image data indicating a first distribution of locations on a workpiece of an error between a shape of the workpiece machined by an industrial machine and a pre-prepared target shape of the workpiece; a second image generating section that generates second image data indicating a second distribution of locations on the workpiece of an error between a command transmitted to the industrial machine for machining the workpiece and feedback from the industrial machine corresponding to the command; and a correlation acquisition section that obtains a correlation between the first distribution and the second distribution, based on the first image data and the second image data.