Thanks to advanced industrial robots, automated welding is state of the art. Automation of the welding process provides opportunities to meet the high standards and requirements of Industry 4.0. Maintaining high quality at high throughput rates is a crucial factor in the success of automated welding processes. Moreover, in case of malfunction, the processes of such robots are easy to trace, which enables straightforward quality control.
In parts of the industry, the already optimized welding process is becoming a bottleneck. Unlike in production, the visual inspection of a welding seam is typically done by humans. While these trained employees can respond flexibly to contingencies, they still depend on their day-to-day fitness for consistency and speed. Therefore, manual visual inspection is more prone to errors than automated optical inspection of a welding seam.
For a component to meet the required quality and comply with relevant standards simultaneously, it must undergo thorough material testing. Just like magnetic particles and indentation testing, automatic optical inspection of a welding seam using artificial intelligence provides a non-destructive method for quality control.
Automated weld inspection offers multiple benefits, allowing production to meet growing market demands. On the one hand, less dependence on daily fitness and increased speed and consistency is apparent.
On the other hand, these non-destructive optical methods blend into the production flow more effectively, as they can be integrated in-line. As a result, the production of such items becomes even more efficient thanks to less downtime and fewer rejects. Moreover, early defect detection of the welding seam through AI-powered visual inspection helps to rework the component as quickly as possible.
In summary, this automated quality control system for welding seams promises high-cost savings. Production processes can thus be reliably optimized and ensure the company’s competitiveness.
AI.SEE™ from elunic will outperform current systems leveraging machine learning algorithms mimicking the adaptability of trained professionals.
Adaptive machine learning models can improve their average precision by up to 250 % throughout the project compared to a conventional computer vision algorithm. This increases the effectiveness of optical weld inspection significantly and cuts costs.