AI Tool Life Detection for Machining Wear and Tolerance Control
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Solution Overview
Problem
Existing detection systems for predicting the remaining service life of machining tools are either manual, prone to errors, or do not account for various factors such as rigidity of the part, drilling time, and force applied, leading to potential errors in the machining process and increased operational costs.
Innovation Solution
A detection system comprising a control unit with an artificial intelligence model, an image capturing device, and an anti-vibration apparatus, which captures images of the machined part, compares them to reference images, and uses deep learning techniques to detect deformations, tolerances, and remaining service life of machining tools independently of human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used to predict remaining service life of machining tools, then the system is simple to implement, but the detection accuracy is low and prone to errors
Solution Approach 1:
The patent replaces manual visual inspection with an automated image recognition system using deep learning algorithms. The system captures images of machining tools during operation and automatically analyzes them to detect wear, deformations, and predict remaining service life, eliminating human subjectivity and error while maintaining operational simplicity through automated processing.
Solution Approach 2:
The system creates digital copies (images) of the machining tool at different stages of wear and uses these copies to train an artificial intelligence model. By comparing current images against the trained model and historical image data, the system accurately predicts remaining service life without requiring physical measurement or manual assessment.
2Productivity
If predetermined service life replacement is used for machining tools, then the operation process is simple, but the operational costs increase due to premature or delayed replacement
Solution Approach 1:
The system implements continuous feedback by capturing images of the machining tool during operation, analyzing wear patterns in real-time, and providing predictions about remaining service life. This feedback loop enables dynamic adjustment of replacement schedules based on actual tool condition rather than fixed predetermined intervals, optimizing both productivity and operational costs.
Solution Approach 2:
The system performs preliminary detection and analysis of tool wear before critical failure occurs. By continuously monitoring the machining tool and predicting remaining service life in advance, the system enables proactive planning of tool replacement, avoiding both premature replacement (wasting resources) and delayed replacement (causing errors and downtime).
3Loss of energy
If machining tools are used beyond their actual service life to reduce costs, then operational costs decrease, but errors occur in the machined parts
Solution Approach 1:
The patent replaces subjective judgment about tool life with an objective automated image recognition system. The deep learning model analyzes visual characteristics of the machining tool and machined parts to detect subtle signs of wear and degradation, providing an accurate, data-driven determination of when the tool should be replaced to maintain quality standards.
Solution Approach 2:
The system introduces an intermediary detection layer between the machining tool and the final product quality. By capturing and analyzing images of both the tool and the machined parts, the system provides early warning signs of tool degradation before they manifest as visible defects in the product, enabling timely intervention to maintain reliability.
4Measurement precision
If deep learning methods are used to analyze reference images and detect deformations, then the detection precision is high, but the processing time increases
Solution Approach 1:
The system performs preliminary action by pre-training the deep learning model with extensive reference images of machining tools at various wear stages before actual detection begins. This pre-training phase enables the model to quickly and accurately analyze new images during operation, reducing processing time while maintaining high detection precision through already-learned patterns.
Solution Approach 2:
The system segments the image analysis process into distinct stages: capturing reference images during normal operation, separately training the deep learning model with this reference data, and then using the trained model for rapid detection during production. This segmentation allows computationally intensive training to occur offline while keeping online detection fast and efficient.
Data Source
AI summary
A body and at least one control unit that stores and/or controls data for drilling processes is disclosed. At least one machining tool is located on the body extends outward from the body, and provides part shaping, at least one image capturing device that is controlled by the control unit and connected with the control unit for capturing images is also present.