Strategiskt innovationsprogram: Samlar Sverige för hållbar och konkurrenskraftig produktion

Welding process visualisation and cognitive support to the manual welder (WELDVISI)

Female automation engineer wear a blue uniform with helmet safety inspection control a robot arm welding machine with a remote system in an industrial factory. Artificial intelligence concept.

This project will develop a prototype for a cognitive support system (CSS) that provides interactive feedback for a manual operator in production in real time.

Physical data registered via sensors in a handheld tool will be sent to a local or cloud-based documentation and quality analysis system. After instant processing, production and quality information will be visualised for the operator via an in-helmet mounted CSS. This will support the operator and enable increased knowledge, confidence, quality, and productivity. It will reduce manual documentation work, improve working conditions, and the image of workers in production. It will also thereby increase social sustainability in manufacturing and lead to a more sustainable work.

In this project, the CSS will be developed towards applications in manual welding since both a large need and a large possibility exist to positively influence the work environment and the training capabilities for many workers. There are 25.000 full-time manual welders in Sweden and 200.000 more utilise welding partly in their manufacturing. There is a large shortage of skilled welders, and the developed system will aid in the development of skilled workers and create image-enhancement for welders in modern production. The cognitive support system supports the welder through real time feedback of important data, and good advice, which will increase quality of the work and the possibility to maintain it at a high level. The system will especially aid both older personnel that starts to lose accuracy due to aging, and newer workers with lack of experience.

Through the digitalised documentation of the manual manufacturing operations, a new level of certainty for both the production and the product will be achieved, which will free time, increase productivity and lead to a reduced scrapping of mis-manufactured components. Reduced scrapping will have a positive environmental and economic impact, and the reduced need for repair work will improve the work environment to some extent.

The WELDVISI system will be experienced as “information of reality + tips on needed actions”. This is different compared to existing digital training systems which are far from a feeling of reality. The WELDVISI system shall be usable for both beginners and experienced welders to see the required speed, angles, and other data (stipulated in welding procedures) during welding. Therefore, the project will have a large positive industrial impact by enabling increased knowledge and learning capabilities, increased support and feedback to manual operators, and increased process and component quality, productivity and adaptivity.

Dissemination of the results will be made through seminars, fairs and conferences where scope and status of the project will be presented. A scientific paper to open access journal will be prepared and published. When a prototype has been developed for testing in industry, a film will be made that describes the usage of the system and how it can be implemented. It will also describe the general configuration of the system. A demonstration will be set up in conjunction with open dissemination activities for people to test the system. It is planned that this prototype demonstration will be held on the ELMIA welding and joining technology exhibition in 2024.

The potential for this project and the CSS that will be developed is large and news value and originality are high – it is a unique project with a unique scope and application. Also, the positive impact that the utilization of digital tools and cloud based advanced systems have on attracting youth, both men and women, to work in manufacturing is substantial. And the learning curves will be significantly improved since instant and adapted feedback is given to the worker. It is also expected that a well-developed system can reduce the personal stress for the worker when relevant feedback from the manufacturing is available and visualized wisely.

This project addresses P2030 Challenge area 4 by supporting the operator in the production system by cognitive support, areas 3 and 1 are also partly addressed as well as UN Sustainable Development Goals No: 8, 9, and 12.

Projektledare
David Franklin, Swerim
[email protected]
+46 73 057 52 80

Vi som samverkar
→ Swerim
→ RISE
→ MIUN
→ Toyota Material Handling
→ ESAB
→ Ellagro
→ Skyllbergs
→ Väderstad
→ Winteria
→ Maskinarbeten
→ Uddcomb
→ Fredrika Bremergymnasiet

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