Capturing Variation in Welding : A method to map variation in welding production, creating a basis for production improvements

Abstract: Weld quality is essential when manufacturing fatigue-loaded structures. Defective welds are problematic, causing increased lead times, repairs and breakdowns. Over- processing as a result of unnecessary safety margins and poor quality is another issue, leading to a more expensive process and heavier products. Knowledge about what variation in weld quality can be expected in the process enables more efficient problem solving and use of resources. Reduced variation equals increased quality. Consequently, reduced variation is both a manufacturer and customer gain. This thesis studies in what way variation in welding production can be mapped and presented, to guide towards the right improvement actions. A literature study addresses welding, weld quality, measurement methods and variation. Two empirical studies using laser scanning equipment along with staff interviews will then be conducted to develop the method to capture variation in welding. In the journey towards machine learning and elimination of operator decisions, knowledge and understanding of variation in the process is necessary. Concrete results of the empirical studies gave new and valuable information to the company. Further, the method to map, analyse and display variation was believed to be useful in several ways, both at the case company but also at other plants. The results show that knowledge about variation could have a large financial effect. By identifying the areas of over-processing and deficient quality, the process can be optimized to increase productivity. While technical issues such as equipment to collect data can be barriers, soft issues like competence, a common understanding, and visualisation of variation seem just as essential.