Welding sequence optimisation in the automotive industry: A review

Spot welding is the most common technique used to join sheet metals in the automotive industry due to the fast rate of production. Optimising the welding process including the sequence, number and location of the welds would significantly improve the quality of the final product and production cost. This paper presents an overview on the available methods to plan and optimise various aspects of the welding process including welding sequence, weld quantity and location. Firstly, the welding concept in the automotive industry is briefly reviewed. Secondly, the welding process optimisation with emphasis on the welding sequence is discussed. The common gaps and challenges are identified and, lastly, future research to plan and optimise the welding sequence in the automotive body is outlined.

[1]  M. Mochizuki,et al.  Residual Stress Distribution Depending on Welding Sequence in Multi-Pass Welded Joints With X-Shaped Groove , 2000 .

[2]  Mauricio G. C. Resende,et al.  Random-Key Genetic Algorithms , 2018, Handbook of Heuristics.

[3]  Daniel,et al.  Variable Speed Transmission Using a Planetary Gear System for High Speed Rotorcraft Application , 2010 .

[4]  John Goldak,et al.  Combinatorial optimization of weld sequence by using a surrogate model to mitigate a weld distortion , 2011 .

[5]  Sang-Chul Park,et al.  Distortion mechanisms and control methodology for welding thin-plate panel structures / , 1998 .

[6]  Shuichi Fukuda,et al.  Determination of welding sequence : a neural net approach , 1990 .

[7]  Rikard Söderberg,et al.  Minimizing Dimensional Variation and Robot Traveling Time in Welding Stations , 2014 .

[8]  Yoram Koren,et al.  Stream-of-Variation Theory for Automotive Body Assembly , 1997 .

[9]  Mahyar Asadi,et al.  A Method to Define the Best Weld Sequence Using a Limited Number of Welding Simulation Analysis , 2015 .

[10]  Ashutosh Tiwari,et al.  A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches , 2012 .

[11]  E. Hinton,et al.  Optimization of trusses using genetic algorithms for discrete and continuous variables , 1999 .

[12]  Jaime A. Camelio,et al.  Compliant Assembly Variation Analysis Using Component Geometric Covariance , 2004 .

[13]  Bhushan Lal Karihaloo,et al.  Comprehensive structural integrity , 2003 .

[14]  Zhongqin Lin,et al.  Application of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis , 2002 .

[15]  M. H. Kadivar,et al.  Optimizing welding sequence with genetic algorithm , 2000 .

[16]  Eric Michielssen,et al.  Genetic algorithm optimization applied to electromagnetics: a review , 1997 .

[17]  Martin Damsbo,et al.  An Evolutionary Algorithm for Welding Task Sequence Ordering , 1998, AISC.

[18]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[19]  Rikard Söderberg,et al.  Strategies for Optimization of Spot Welding Sequence With Respect to Geometrical Variation in Sheet Metal Assemblies , 2010 .

[20]  Darrell Whitley,et al.  The Travelling Salesman and Sequence Scheduling: Quality Solutions using Genetic Edge Recombination , 1990 .

[21]  Ching Hsieh,et al.  Clamping and welding sequence optimisation for minimising cycle time and assembly deformation , 2002 .

[22]  Sun Jin,et al.  Assembly sequence planning of automobile body components based on liaison graph , 2007 .

[23]  Milena Lazarova Efficiency of parallel genetic algorithm for solving N-queens problem on multicomputer platform , 2008 .

[24]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[25]  V. Chaudron,et al.  Assembly sequences: planning and simulating assembly operations , 2005, (ISATP 2005). The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, 2005..

[26]  Kyoung-Yun Kim,et al.  Heuristics for single-pass welding task sequencing , 2002 .

[27]  Lars Lindkvist,et al.  Variation Simulation of Sheet Metal Assemblies Using the Method of Influence Coefficients With Contact Modeling , 2007 .

[28]  Andy J. Keane,et al.  Weld sequence optimization: The use of surrogate models for solving sequential combinatorial problems , 2005 .

[29]  Jin Sun,et al.  Optimisation of assembly sequences for compliant body assemblies , 2009 .

[30]  Mauricio G. C. Resende,et al.  Biased random-key genetic algorithms for combinatorial optimization , 2011, J. Heuristics.

[31]  Y. Gene Liao Optimal design of weld pattern in sheet metal assembly based on a genetic algorithm , 2005 .

[32]  Jasbir S. Arora,et al.  A genetic algorithm for sequencing type problems in engineering design , 1997 .

[33]  Garret N. Vanderplaats,et al.  Numerical Optimization Techniques for Engineering Design: With Applications , 1984 .

[34]  Etienne Bonnaud Mitigation of weld residual deformations by weld sequence optimization: limitations and enhancements of surrogate models , 2017 .

[35]  Darek Ceglarek,et al.  Quality-driven Sequence Planning and Line Configuration Selection for Compliant Structure Assemblies , 2005 .

[36]  Rikard Söderberg,et al.  Evaluating evolutionary algorithms on spot welding sequence optimization with respect to geometrical variation , 2018 .