Critical joint identification for efficient sequencing
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Critical joint identification for efficient sequencing Roham Sadeghi Tabar1
· Kristina Wärmefjord1 · Rikard Söderberg1 · Lars Lindkvist1
Received: 29 April 2020 / Accepted: 23 August 2020 © The Author(s) 2020
Abstract Identifying the optimal sequence of joining is an exhaustive combinatorial optimization problem. On each assembly, there is a specific number of weld points that determine the geometrical deviation of the assembly after joining. The number and sequence of such weld points play a crucial role both for sequencing and assembly planning. While there are studies on identifying the complete sequence of welding, identifying such joints are not addressed. In this paper, based on the principles of machine intelligence, black-box models of the assembly sequences are built using the support vector machines (SVM). To identify the number of the critical weld points, principle component analysis is performed on a proposed data set, evaluated using the SVM models. The approach has been applied to three assemblies of different sizes, and has successfully identified the corresponding critical weld points. It has been shown that a small fraction of the weld points of the assembly can reduce more than 60% of the variability in the assembly deviation after joining. Keywords Critical joint · Sequence · Machine learning · PCA · Assembly · SVM
Introduction In the manufacturing industry, nominal geometries do not exist. Every component to be assembled is subjected to geometrical variation. Identifying the sources of variation and controlling them is a common challenge. Robust design perspectives [14] and geometry assurance activities [16,28] are developed to control the effect of geometrical variation in the final product. Joining operation is one of the most critical processes causing the final geometrical variation. For sheet metal assemblies, where the sheets are compliant, the joining operation is mainly spot welding or riveting. With the recent development within digitalization and availability of data, a self-compensating assembly line for sheet metal assemblies is developed based on the digital twins [18]. In this concept, a Computer-Aided Tolerancing (CAT) tool is taking advantage of local optimizers to improve the geometrical quality of the assemblies in different disciplines. For the joining operation, the optimal sequence of the spot welding process is proposed using the digital twin for individualized assemblies [22]. For each assembly, a number of the most important welds is needed to be selected and set in a sequence
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Roham Sadeghi Tabar [email protected] Department of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, Sweden
and secure the geometry in each assembly station. Therefore, first, the number of welds that are critical for the assembly needs to be determined. Secondly the sequence of such welds considering the rest of the weld points should be decided on. The question of the sequence of welding is categorized as a classical Operation Research (OR) problem. It
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