Optimised welding in high value industries

WeldVue: Optimised welding in high value industries

Background

One of the most critical processes employed in the manufacturing industry is welding. It is applied to a variety of materials and products in different industrial sectors (e.g. steel fabrication, automotive, aerospace, construction). Therefore, the condition of welding has direct impact on the quality of final products beyond a good design and high-quality materials. Welds should have the proper penetration and the correct welding process and technique must be used for the particular alloy, position, and overall condition.

Currently, the growing use of robots in welding processes, particularly in the automotive sector, is improving and accelerating production. However, quality monitoring is still rather manual: testing of parts happens after different stages of manufacturing and in some cases with destructive techniques (i.e., welded samples are cut to be inspected).

Therefore, the technological challenge is to provide a solution enabling intelligent optimisation and monitoring of welding processes in serial production/welding of metal parts for manufacturing sectors.

Objectives

The objectives of the WeldVue project are to:

• Implement an advanced AI-based model for automotive parts manufacturing processes, optimisation and reconfiguration (resulting in a process reconfiguration time reduction of 15%);
• Implement an operational pilot line with sensors and quality inspection NDT systems for process and product quality monitoring and inspection, with probability of defect detection > 99%);
• Improve quality control by generating 50% less scrap and waste and near-zero defect parts;
• Increase process efficiency by decreasing downtime by 15%

Benefits

The WeldVue solution will allow;

• Applicability to any type of welding technique;
• Fast resolution of welding parameters from weld geometry specifications (less than a second);
• Automatic identification of possible welding defects by analysing the process parameters through monitoring weld features, benchmarking and product requirements
• Effective provision of welding procedure specifications for repair;
• Possibility of testing the series of optimal welding parameters against cost models and manufacturability.

These characteristics will enable manufacturing of near-zero defect parts, reducing scrap waste, and fast delivery of quality products to market, meeting customers’ quality requirements and needs.

Brunel Innovation Centre's Role

Brunel Innovation Centre will validate the initial model and develop algorithms to be used in WeldVue, linking with industry partners and Tier 1 manufacturers in order to turn the developments into market-based solutions. In addition to the design and validation of the model and algorithms, BIC will assist in ensuring technical robustness of the platform.

Project Partners

• STL Tech Ltd
• Ether NDE Ltd
• Teknopar Endustriyel Otomasyon San. A.S.
• Coskunoz Kalip Makina San Ve Tic. A.S.
• TWI Ltd
• Brunel University London

Meet the Principal Investigator(s) for the project

Professor Tat-Hean Gan - Professional Qualifications CEng. IntPE (UK), Eur Ing BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham) MSc in Advanced Mechanical Engineering (University of Warwick) MBA in International Business (University of Birmingham) PhD in Engineering (University of Warwick) Languages English, Malaysian, Mandarin, Cantonese Professional Bodies Fellow of the British Institute of NDT Fellow of the Institute of Engineering and Technology Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.

Related Research Group(s)

Brunel Innovation Centre - A world-class research and technology centre that sits between the knowledge base and industry.

Project last modified 07/06/2021