Stud Welding Anomaly Prediction From Joining Machine Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for identifying discontinuities and anomalies in joinings, such as stud welding and riveting, are unreliable due to human error and subjective skills, leading to inconsistent results and potential delays or recalls in the automotive manufacturing process.
Innovation Solution
A computer-based system utilizing artificial intelligence models to analyze real-time data from joining machines, including voltage, current, and lift values, to detect and classify discontinuities and anomalies, and generate corrective or preventative actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual inspection by technicians is used to identify discontinuities and anomalies, then the method is simple and low-cost, but the reliability and consistency of detection results deteriorate due to human error and subjective skills
Solution Approach 1:
The patent replaces the mechanical visual inspection system performed by technicians with an automated optical detection system using cameras and image processing algorithms. This substitution eliminates human error and subjective judgment while maintaining ease of implementation through standard imaging equipment.
Solution Approach 2:
The inspection system performs self-detection and self-analysis of discontinuities and anomalies through automated image processing and machine learning algorithms, eliminating the need for human technicians to manually inspect each joining while ensuring consistent and reliable detection results.
2Device complexity
If visual inspection by technicians is used to identify discontinuities and anomalies, then the equipment requirement is minimal, but the detection speed and timeliness worsen due to the rapid pace of automatic joining machines
Solution Approach 1:
The optical detection system operates continuously and simultaneously with the automatic joining machine, capturing images of each joining as it is created and processing them in real-time. This continuous detection ensures that no discontinuities are missed while maintaining the high productivity of the joining process without requiring slower manual inspection intervals.
3Ease of manufacture
If conventional anomaly-detection methods are used, then the implementation is straightforward, but the ability to provide real-time detection and immediate corrective action deteriorates
Solution Approach 1:
The system implements real-time feedback by continuously monitoring joining processes, automatically detecting discontinuities through image analysis, and immediately alerting operators or adjusting process parameters. This closed-loop feedback mechanism enables immediate corrective action while maintaining straightforward implementation through automated decision-making algorithms.
Data Source
AI summary
Disclosed herein are systems and methods for identifying welding anomalies and discontinuities in stud welding using AI models. Instead of conventional welding accuracy methods (e.g. destructive and/or image generation methods) a processor may communicate with one or more sensors associated with a joining machine to retrieve joining data and attributes. The processor may then execute an AI model that is trained based on previously performed stud welding, their corresponding welding attributes, and their corresponding discontinuities and/or anomalies. The processor may execute the AI model using data retrieved from the sensors and may calculate a likelihood of a discontinuity and discontinuity attributes, such as, location, depth, and the like. The processor may also execute a second AI model to identify an appropriate course of action to remedy the identified/predicted discontinuity.