Machine Tool Chip Detection Using Mesh-Based Image Segmentation
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Solution Overview
Problem
Existing chip detection systems struggle with accurately and efficiently identifying chips produced during machine tool processing due to variations in shape, color, size, and environmental conditions, requiring significant computational resources and time.
Innovation Solution
A chip detection apparatus that divides the target area into mesh images using a mesh divider and employs a chip information determiner with a determination parameter to identify chip information for each mesh image, utilizing a machine learning algorithm to enhance detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing is performed on the entire image to detect all chips, then chip detection coverage is improved, but calculation amount and processing time increase enormously
Solution Approach 1:
The image processing region is divided into multiple sub-regions (first image processing region and second image processing region) based on chip density characteristics. The processor performs image processing separately on each sub-region, reducing the computational complexity compared to processing the entire image at once while maintaining comprehensive chip detection coverage.
2Measurement precision
If image processing is performed on the entire image to detect all chips, then chip detection coverage is improved, but calculation amount increases enormously
Solution Approach 1:
The processor divides the image processing into multiple regions and selectively applies different processing intensities. High-density regions receive focused processing while low-density regions receive reduced processing, significantly reducing the total calculation amount required for comprehensive chip detection.
Solution Approach 2:
Different image processing strategies are applied to different regions based on their chip density characteristics. The first image processing region (high density) and second image processing region (low density) receive tailored processing approaches, optimizing computational resource allocation while maintaining detection accuracy.
3Quantity of substance
If conventional image processing is used on complex in-machine environments with various chip variations, then detection comprehensiveness is improved, but detection precision deteriorates
Solution Approach 1:
By dividing the image into multiple processing regions based on chip density, the system can apply region-specific detection algorithms. This segmentation allows for more precise detection in high-density areas while maintaining comprehensive coverage across all chip types in the entire image.
Data Source
AI summary
To easily and accurately detect chips produced when processing a work by a machine tool, there is provided a chip detection apparatus for detecting chips produced when processing a work by a machine tool. The chip detection apparatus includes a mesh divider that performs processing of dividing, into a plurality of mesh images in a predetermined mesh size, at least part of an area image obtained by capturing a target area where the chips are to be detected, and a chip information determiner that determines chip information concerning the chips for each of the mesh images using a determination parameter preset for determining the chip information.