WeldZero: Improved automotive resistance spot welding
The WeldZero project focused on the development of intelligent robotic welding system solutions within a cyber-physical production system (CPPS), in conjunction with end users Laing O’Rourke, BMW and SubSea7. The aim was to contribute to international competitiveness for the UK as it modernises industrial capabilities to steer industry towards the wider adoption of automation and autonomous manufacturing solutions. These solutions would lead to improvements in accuracy, precision and quality, and help reduce manufacturing costs.
Within the project, the Joining 4.0 Innovation Centre (J4IC) supported the BMW’s Mini plant in Oxford, UK to reduce instances of weld expulsion during resistance spot welding processes used in Mini bodies. Each Mini vehicle contains over 6,000 resistance spot welds that can potentially be subject to weld expulsion, i.e., when a physical instability in the welding process causes the ejection of liquid metal from the weld. While this expulsion is not often related to the production of a defective weld, manual identification and cleaning is required. The expelled metal can damage paintwork and initiate later corrosion failure, leading to costs related to the personnel that are needed to rectify expulsion damage and potential corrosion warrantee claims.
Data collection at BMW is able to identify when and where expulsion occurs on the 1,000+ robot production line, but not the reason why. Therefore, it was important to determine the cause of expulsion in each individual case in order to take steps to prevent it.
J4IC and BMW performed a study of production processes to identify the main causes of expulsion in resistance spot welding. These processes were then replicated in a TWI laboratory, using a similar robot and welding gun with the same weld timer, electrodes, tip dressers and welding programs. This enabled simulation and study of production, and collection of data on instances of expulsion.
This data was then exported to J4IC partner Lancaster University where it was used to develop and train a diagnostic algorithm to identify the defects that cause expulsion events. Blind data sets were then applied to the algorithm to test if it could determine the cause of defects from the data, and these tests showed a success rate of >90% with laboratory data. Finally, the lab data and algorithm were tested in a production environment at BMW Oxford, with the help of consortium partner ATS who embedded the predictive algorithm in their Atlas software in order to analyse data fed from the BMW production line.
This work, which took place during the 2-year duration of WeldZero, showcased the benefits of digital technologies when applied to welding operations in an industrial manufacturing context, to support a zero defect strategy and help improve UK competitiveness through the wider adoption of Industry 4.0 strategies.
Partners: ATS, BMW, HAL Robotics, Laing O’Rouke, Prodtex (first year only), Subsea7, TWI and J4IC.
The WeldZero project received funding from Innovate UK under grant agreement No. 105812.