Welding Bead Inspection Using Master Data and Multi-AI Defect Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current bead appearance inspection methods for welding beads are inefficient and lack customizability, as they rely on visual inspections and do not uniformly standardize defect determination, leading to variations in quality assessment across users and products.
Innovation Solution
A bead appearance inspection system that includes an input unit for welding bead data, a first determination unit for shape comparison using master data, and multiple AI units for defect detection, providing comprehensive inspection results for shape and defect analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection methods are used for bead appearance inspection, then the inspection process is simple, but the inspection accuracy and consistency are poor
Solution Approach 1:
The inspection system is segmented into multiple specialized AI models, each trained to detect specific defect types (e.g., porosity, cracks, undercut). This segmentation allows each model to focus on particular features, improving overall inspection accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
Multiple AI models act as intermediaries between the raw inspection data and the final determination. Each AI model processes the data through its specialized lens, and their combined outputs feed into the comprehensive determination unit, which synthesizes all findings to reach a final inspection conclusion
2Reliability
If multiple AI models are used for comprehensive defect detection, then the inspection accuracy improves, but the device complexity increases
Solution Approach 1:
The system segments the defect detection task into multiple specialized AI models, each responsible for specific defect types. This segmentation improves reliability by ensuring comprehensive coverage of different defect categories while managing complexity through clear functional division among models
Solution Approach 2:
Multiple AI model outputs are merged in the comprehensive determination unit. The system combines the results from various specialized models, integrating their findings to form a unified inspection conclusion. This merging approach enhances reliability by aggregating multiple perspectives while presenting a single consolidated result
3Reliability
If comprehensive determination based on multiple AI results is implemented, then the inspection reliability improves, but the processing time increases
Solution Approach 1:
Each AI model performs preliminary analysis of the inspection data independently, identifying specific defect types and their characteristics. This preliminary action allows the comprehensive determination unit to work with pre-processed, organized findings rather than raw data, reducing the time required for final synthesis while maintaining comprehensive evaluation
Solution Approach 2:
The system implements continuous processing where multiple AI models analyze data simultaneously and continuously feed results to the comprehensive determination unit. This parallel, continuous operation maintains high reliability through thorough analysis while minimizing processing time by eliminating sequential bottlenecks
4Stability of the object's composition
If standardization of defect determination is implemented, then the quality assessment consistency improves, but the adaptability to different inspection requirements decreases
Solution Approach 1:
The system employs dynamic, adjustable determination standards within each AI model. The comprehensive determination unit can adaptively weight and combine results from different AI models based on the specific inspection requirements. This dynamic approach maintains consistency through standardized processing while allowing flexibility to adapt to different defect priorities and inspection criteria
Data Source
AI summary
A bead appearance inspection device includes an input unit configured to enter input data related to a welding bead of a workpiece produced by welding, a first determination unit configured to perform a first inspection determination related to a shape of the welding bead based on a comparison between the input data and a master data, k second determination units, where k is an integer of 1 or more, that are equipped with k types of artificial intelligence and that are configured to perform a second inspection determination related to a welding defect of the welding bead based on processings of the k types of artificial intelligence targeting the input data, and a comprehensive determination unit configured to output a result of an appearance inspection of the welding bead to an output device based on determination results of the first determination unit and the k second determination units.