Enhancing Welding Processes Using Machine Learning-Based Defect Analysis

The problem

Welding is a commonly used method of joining metals together in various industries such as construction, automotive, aerospace, and shipbuilding. However, during the welding process, various defects can occur during the process that could compromise the quality and structural integrity of the weld. These defects can range from porosity (air pockets within the weld), weld spatter (excessive weld material on the surface), arc-strikes (improper weld placement), to defective cross-sectional profiles (the weld does not have the correct shape).

Detecting and repairing weld defects can be a time-consuming and costly process, especially when done manually. This project aims to address the above problem by developing an AI algorithm that can detect weld defects in real-time based on camera images of the weld and cross-sectional profiles captured using a 2D laser scanner. By automating the process of detecting weld defects, the project could save manufacturers time and money by enabling them to quickly identify and address defects during the welding process. This would improve the overall quality and strength of the weld, reducing the risk of product failures, safety hazards, and costly repairs.

Furthermore, the developed solution could help to improve the efficiency of the manufacturing process by reducing the need for manual inspection and enabling real-time detection and correction of defects. This could ultimately lead to increased productivity and cost savings for manufacturers.

The project goals

The ultimate objective of this project is to develop an AI algorithm that can accurately detect weld defects in order to improve the quality and safety of welded components.

The main goals of this Omdena-Reachbots Challenge are:

Develop an AI algorithm that can accurately detect different types of weld defects, including porosity, weld spatter, arc strikes, and defective cross-sectional profiles.

Integrate the algorithm with camera images of the weld and cross-sectional profiles captured using a 2D laser scanner.

Train the algorithm on a large dataset of weld defects to ensure that it can accurately identify defects in a variety of welding scenarios and conditions.

Optimize the algorithm to minimize false positives and false negatives, ensuring that it can accurately identify defects while avoiding unnecessary rejections or repairs.

Test the algorithm on a range of real-world welding scenarios to validate its effectiveness and performance.

Why join? The uniqueness of Omdena AI Innovation Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.