Neural Network Tool-State Training Using Converted Tool Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training neural networks to detect tool wear require extensive manual effort and specific training data, making it difficult to adapt to new tool types, especially in industrial machining where various tool geometries and coatings are common, leading to high complexity and costs.
Innovation Solution
A method that converts image data from one tool type into another, allowing neural networks to be trained using automated image processing, reducing the need for manual data acquisition and enabling adaptation to new tool types by transforming shape and color characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained using actual image data of a new tool type, then detection accuracy for that tool type is improved, but manual effort and costs increase significantly
Solution Approach 1:
The patent creates synthetic image data by copying and transforming images from a first tool type to represent a second tool type. Instead of manually acquiring actual images of the new tool type, the system generates synthetic copies by applying geometric transformations, color adjustments, and noise addition to existing images, thereby eliminating manual data acquisition effort while maintaining sufficient realism for training purposes
Solution Approach 2:
The patent systematically changes key parameters of existing images to create synthetic data for new tool types. This includes modifying geometric parameters (rotation, scaling, translation), visual parameters (color channels, brightness, contrast), and adding controlled noise. These parameter transformations allow the neural network to learn from synthetic data that reflects the characteristics of new tool types without requiring manual image collection
2Reliability
If neural networks are trained specifically for each new tool type, then detection reliability is improved, but device complexity and training costs increase
Solution Approach 1:
The patent creates a universal image synthesis system that can generate training data for any tool type using a single base set of images. The synthesis process incorporates tool-type-specific parameters (geometry, color, texture characteristics) that can be adjusted without changing the underlying neural network architecture or training methodology, thereby reducing the complexity of adapting to new tool types while maintaining reliable detection
Solution Approach 2:
The patent performs preliminary synthesis of diverse training images before neural network training begins. By pre-generating a comprehensive set of synthetic images that cover various tool states, angles, and conditions, the system eliminates the need for complex real-time data collection and manual annotation processes when deploying to new tool types, simplifying the overall adaptation workflow
3Manufacturing precision
If manual assessment of image data is performed by technical experts, then training data quality is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent implements self-service mechanisms where the synthetic image generation process automatically incorporates tool-type characteristics and wear patterns without requiring expert intervention. The system uses predefined models of tool geometry and wear progression to generate realistic synthetic images that are inherently labeled with correct tool states, eliminating the need for manual assessment while maintaining data quality
Solution Approach 2:
The patent replaces the mechanical process of manual image assessment by technical experts with an automated computational process. Synthetic images are generated algorithmically with embedded ground truth labels based on the transformation applied to the source images, substituting human expert time with automated image processing and data synthesis operations that are both faster and scalable
Data Source
Figure 1~2
Figure 3
AI summary
In the method for training a neural network to recognize a tool's state using image data, the neural network is trained to recognize the state of a first tool type. Image data of a second tool type is then used, which undergoes image processing to convert the image data of the second tool type into image data of the first tool type. The neural network is then trained using this converted image data. In the method for machining and/or manufacturing using the first tool type, the tool's state is recognized by a neural network trained using this method.The system for processing and/or manufacturing using a first type of tool includes a neural network which is trained according to such a method for training a neural network and/or which is trained to execute such a process for processing and/or manufacturing.