Tool Wear Assessment Using Multi-Light Imaging and Machine Learning
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Solution Overview
Problem
Existing methods for assessing tool wear in machining tools are labor-intensive, inconsistent, and often result in premature tool replacement or reconditioning, leading to shortened tool service life and increased resource consumption.
Innovation Solution
A method involving the capture of multiple optical images of a tool surface under different lighting conditions, processing the image data to generate images with surface structure information, and classifying the tool condition using machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by operating personnel is used to assess tool wear, then the assessment can be performed with simple equipment, but the inspection is labor-intensive, time-consuming, and produces inconsistent results
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical measurement system using a camera to capture images of the tool and workpiece. Image processing algorithms automatically analyze the captured images to detect tool wear, eliminating the need for manual inspection and ensuring consistent, objective measurements while reducing inspection time.
Solution Approach 2:
The system enables automatic self-assessment of tool wear by processing images and generating wear assessments without human intervention. The automated image analysis performs the entire inspection process, from capturing images to determining wear extent and classification, making the system self-sufficient and eliminating labor-intensive manual evaluation.
2Reliability
If tools are removed and replaced after a specified period or number of machining cycles to ensure safety, then tool wear can be prevented, but the effective service life of tools is shortened and the number of tool changes is higher than necessary
Solution Approach 1:
The system performs preliminary detection of tool wear by analyzing images captured during or between machining operations. By detecting wear early through automated image processing, the system allows tools to be used until actual wear thresholds are reached, preventing premature replacement and maximizing tool service life while maintaining machining quality.
Solution Approach 2:
The patent implements a feedback mechanism where image analysis results provide real-time information about tool wear status. This feedback enables dynamic adjustment of tool usage decisions, allowing tools to continue operation when wear is within acceptable limits and triggering replacement only when actual wear thresholds are approached, thereby optimizing both reliability and service life.
3Extent of automation
If automatic wear detection systems are implemented, then inspection can be automated and consistency improved, but the complexity of the device increases
Solution Approach 1:
The patent employs a camera system that serves multiple functions: capturing tool images, capturing workpiece images for context, and providing visual records for documentation. This multi-functional approach achieves high automation without proportionally increasing device complexity, as a single imaging device performs several roles in the inspection process.
Solution Approach 2:
The system uses image processing algorithms as an intermediary between the physical tool and the assessment decision. Rather than requiring complex direct measurement devices, the patent captures optical images and uses computational analysis to detect wear, simplifying the physical hardware while achieving automated inspection through software-based mediation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables reliable, automated, and efficient assessment of tool wear, reducing inconsistencies and preventing defects in machining results by accurately identifying when tools need reconditioning or replacement.
Implementation Method 1
capturing multiple optical images of a surface of the tool under different illumination conditions
Data Source
Figure 1A~2
Figure 3a~3e
Figure 4~6
AI summary
In a method for determining the condition of a tool, several optical images of a tool surface are first acquired under different lighting conditions. Image data from the multiple optical images is then processed to generate an image containing surface structure information. This image is then preprocessed to generate one or more preprocessed images. Finally, based on the one or more preprocessed images, the condition of the tool is classified into one of at least two classes using a machine learning method.