Tool Condition Recognition Using Converted Training Images
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Solution Overview
Problem
Existing neural network training methods for recognizing tool conditions in machining production require extensive manual effort and high entry barriers, especially when adapting to new tool types, due to the lack of initial training data.
Innovation Solution
A method for training a neural network to recognize tool conditions by converting image data of a second tool type into image data of a first tool type through image processing, allowing the neural network to be trained using the converted data without the need for manual acquisition of image data for the first tool type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained on new tool types using traditional methods, then recognition accuracy is improved, but manual effort and training costs increase significantly
Solution Approach 1:
The patent creates synthetic image data by copying and transforming images from a first tool type to generate training data for a second tool type. This involves applying geometric transformations, color adjustments, and compositional changes to existing images, thereby eliminating the need for manual image acquisition and expert annotation for new tool types while maintaining training effectiveness
Solution Approach 2:
The patent performs preliminary actions by pre-processing and transforming images from available tool types before they are needed for training new tool types. By establishing a library of transformed images in advance, the system prepares training data proactively, reducing the time and effort required when new tool types need to be trained
2Adaptability or versatility
If neural networks are adapted to new tool types with different geometries and coatings, then versatility is improved, but training complexity and costs increase
Solution Approach 1:
The patent implements a universal image transformation framework that can handle multiple tool types with different geometries and coatings through a single systematic approach. The method uses standardized transformation parameters and processing steps that can be applied across diverse tool types, making the training system versatile without proportionally increasing complexity
Solution Approach 2:
The patent systematically changes image parameters such as color channels, brightness, contrast, and geometric properties to simulate different tool types, coatings, and wear conditions. By manipulating these parameters algorithmically, the system generates diverse training data without requiring physical samples of every possible tool variation, thereby improving adaptability while controlling complexity
3Reliability
If actual image data of new tool types is acquired and classified by experts, then training data quality is improved, but manual assessment effort increases
Solution Approach 1:
Instead of manually acquiring and classifying actual images of new tool types, the system copies and transforms images from existing tool types to generate synthetic training data. This automated copying process eliminates the need for expert manual assessment while maintaining data quality through algorithmic transformation and consistency with the original imaging conditions
Data Source
AI summary
In a method for training a neural network to recognize a tool condition based on image data, the neural network is trained to recognize the tool condition of a first tool type, and image data of a second tool type is applied. The image data is subjected to image processing. Via this, the image data of the second tool type is converted into image data of the first tool type. The neural network is trained based on the converted image data. In a method for machining and/or production via the first tool type, the tool condition of the first tool type is recognized via a neural network that is trained in accordance with such a method.