Spot Weld Integrity Evaluation Using AI Neural Networks
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Solution Overview
Problem
Current spot welding processes in motor vehicle manufacturing lack an efficient method to evaluate the integrity of spot welds in real time, leading to inefficiencies and potential rework or disposal of components due to substandard welds.
Innovation Solution
A system utilizing a light source and camera assembly with an artificial intelligence neural network-based algorithm to evaluate the integrity of spot welds in real time, incorporating continuously updated training data that includes process, material, and sensitivity analysis, and capable of analyzing images or video frames to determine weld quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random sampling inspection is used to evaluate spot weld integrity, then inspection cost and time are reduced, but weld quality reliability is compromised due to inability to detect all defective welds
Solution Approach 1:
The patent replaces manual mechanical inspection methods (chisel tests, peel tests) with an automated optical inspection system using cameras and image processing algorithms. This substitution enables 100% inspection coverage while maintaining high productivity through non-contact, rapid automated evaluation of spot weld integrity.
Solution Approach 2:
The patent introduces an intermediary image processing system that captures visual data of spot welds and uses neural network algorithms to evaluate weld integrity. This intermediary system bridges the gap between physical weld quality and automated decision-making, enabling reliable 100% inspection without direct human intervention.
2Reliability
If 100% inspection of spot welds is implemented, then weld quality reliability is improved, but inspection time and production complexity increase
Solution Approach 1:
The patent replaces slow manual inspection with high-speed automated optical inspection and neural network-based image analysis. This substitution enables real-time evaluation of every spot weld without adding time to the manufacturing process, as the automated system operates at speeds compatible with production line requirements.
Solution Approach 2:
The patent implements continuous real-time inspection of spot welds during the manufacturing process rather than batch or random sampling. The automated system continuously captures images and evaluates weld integrity as welds are created, eliminating inspection delays and enabling immediate detection and correction of quality issues.
3Measurement precision
If traditional optical inspection methods are used, then device complexity is minimized, but measurement precision and detection capability are insufficient for accurate weld evaluation
Solution Approach 1:
The patent introduces an intermediary neural network-based image processing system that enhances the capabilities of standard camera equipment. This intermediary software layer extracts subtle features from images and provides precise weld evaluation without requiring complex hardware modifications, maintaining relative system simplicity while achieving high measurement precision.
Solution Approach 2:
The patent changes the inspection approach from direct physical measurement to optical parameter analysis. By capturing images and analyzing visual parameters (brightness distribution, edge characteristics, geometric features) through neural networks, the system achieves high measurement precision using standard optical equipment rather than complex measurement instruments.
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 real-time evaluation of spot weld integrity, reducing rework and disposal by predicting defects and optimizing welding parameters, allowing for 100% inspection of spot welds during manufacturing.
Implementation Method 1
projecting light from a light source at a spot weld to illuminate the spot weld
Data Source
AI summary
A method to evaluate the integrity of spot welds includes one or more of the following: projecting light from a light source at a spot weld to illuminate the spot weld; capturing an image of the illuminated spot weld with a camera; transmitting information about the image of the illuminated spot weld to a central processing unit (CPU); and evaluating with the CPU the information about the image of the illuminated spot weld coupled with an artificial intelligence neural networked-based algorithm to determine the integrity of the spot weld in real time.