Welding Bead Inspection Using Shape Normalization and Multi-AI Detection
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Solution Overview
Problem
Existing bead appearance inspection devices struggle to accurately detect welding defects in welding beads with diverse shapes, due to differences in the actual shape of the welding bead and the shape specified by point group data, leading to reduced detection accuracy.
Innovation Solution
A bead appearance inspection device equipped with k types of artificial intelligence, which performs preprocessing to convert the shape of the welding bead into a predetermined shape, such as linear, and uses these AI types to inspect and determine the presence or absence of welding defects based on processed input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional point group data-based inspection methods are used, then the inspection process is simple, but detection accuracy decreases for welding beads with diverse shapes
Solution Approach 1:
The patent applies preliminary action by performing preprocessing on input data to convert welding beads of diverse shapes into a predetermined standard shape before inspection. This preprocessing step standardizes the data representation, ensuring that subsequent AI inspection processes operate on uniform data structures, thereby improving detection accuracy without requiring complex shape-specific inspection algorithms for each welding bead variation.
Solution Approach 2:
The patent employs parameter changes by transforming the shape parameters of welding beads through preprocessing operations. The input data representing various welding bead shapes is converted into a standardized shape with consistent geometric parameters, allowing the AI inspection system to focus on defect detection rather than adapting to shape variations, thus improving measurement precision.
2Measurement precision
If multiple AI types are equipped for inspection, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent applies universality by developing a single AI inspection system that can handle multiple types of welding defects across diverse welding bead shapes. Through the preprocessing step that standardizes all welding bead shapes into a predetermined form, the AI system achieves multi-functionality, capable of inspecting various defect types without requiring separate specialized systems for each welding bead configuration.
Solution Approach 2:
By performing preprocessing to convert diverse welding bead shapes into a standard predetermined shape before AI inspection, the system eliminates the need for multiple shape-specific AI models. This preliminary standardization action allows a single AI system to universally inspect all welding beads regardless of their original shape variations, improving detection accuracy while avoiding the complexity of maintaining multiple AI systems.
3Measurement precision
If preprocessing is performed to convert shape, then AI inspection accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by performing shape conversion preprocessing before the AI inspection process. This preprocessing step converts welding beads of diverse shapes into a predetermined standard shape, enabling the AI system to operate more efficiently on standardized data. The time investment in preprocessing is offset by the improved efficiency and accuracy of the subsequent AI inspection, reducing the need for repeated inspections or complex shape-specific processing.
Solution Approach 2:
The preprocessing operation changes the geometric parameters of welding bead representations into a standardized form. This parameter transformation improves AI inspection accuracy by providing consistent input data structures, and the efficient implementation of these parameter changes minimizes the time penalty, achieving a favorable balance between processing time and inspection accuracy.
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 preprocessing unit configured to perform a preprocessing of converting a shape of the welding bead into a predetermined shape on the input data, and k inspection 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 inspect and determine presence or absence of a welding defect of the welding bead based on processings of the k types of artificial intelligence targeting input data on which the preprocessing is performed.