Neural Image Analysis for Flexible Tool Wear Assessment
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Solution Overview
Problem
Existing methods for determining tool wear in industrial production are inaccurate and often lead to unnecessary tool replacements, increasing costs due to unutilized tool life and batch dependencies, with limitations to specific tool types and cutting geometries.
Innovation Solution
A computer-implemented method using an artificial neural network to analyze image data of a tool's wear-relevant region, allocating classes to each image point and determining characteristic values for wear levels, allowing precise wear determination regardless of tool type or cutting geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tools are exchanged prior to maximum wear to avoid quality deterioration, then component quality and processing power are maintained, but tool costs increase due to unutilized tool life
Solution Approach 1:
The patent replaces indirect mechanical measurement methods (vibration sensors, acoustic emissions, cutting force measurements) with direct optical imaging and machine learning-based image analysis. This substitution enables precise visual assessment of tool wear by analyzing image data of the wear-relevant region, allowing accurate determination of actual wear levels without unnecessary tool replacements
Solution Approach 2:
The patent transforms the wear assessment approach by changing from indirect parameter measurements (vibrations, forces, currents) to direct visual parameter analysis through image data. The neural network processes image characteristics to determine wear levels, enabling precise monitoring that matches actual wear conditions rather than relying on indirect proxies
2Ease of operation
If indirect calculation methods are used to estimate tool wear, then tool wear can be monitored without direct measurement, but estimation accuracy is insufficient
Solution Approach 1:
The patent replaces indirect calculation methods based on machine signals (vibrations, acoustic emissions, cutting forces, currents) with direct optical imaging combined with neural network analysis. This substitution provides visually direct assessment of wear by analyzing image data of the wear-relevant region, achieving high measurement precision while maintaining ease of operation through automated image processing
3Measurement precision
If existing wear estimation methods are applied, then tool wear can be assessed, but the methods are limited to specific tool types or cutting geometries
Solution Approach 1:
The patent creates a universal wear assessment system where the neural network can analyze image data from various tool types and cutting geometries. The system processes images of wear-relevant regions regardless of specific tool characteristics, enabling the same methodology to be applied across different tool types, materials, and geometries without requiring tool-specific calibration or adjustment
Data Source
AI summary
Various embodiments include a computer-implemented method for determining a level of wear of a tool. The method includes: obtaining an image data set mapping a wear-relevant region of the tool; allocating, using a computing unit and an artificial neural network, one class of a predetermined quantity of classes to each image point of a plurality of image points of the image data set; and determining a characteristic value based on a result of the allocation for the level of wear. The quantity of classes includes at least one wear class.