scholarly journals Efficient Spot Welding Sequence Simulation in Compliant Variation Simulation

2021 ◽  
Author(s):  
Roham Sadeghi Tabar ◽  
Samuel Lorin ◽  
Christoffer Cromvik ◽  
Lars Lindkvist ◽  
Kristina W\xe4rmefjord ◽  
...  
Keyword(s):  
Spot Welding ◽  
Welding Sequence ◽  
Sequence Simulation ◽  
Variation Simulation

Path planning of spot welding robot based on multi-objective grey wolf algorithm

2021 ◽  
pp. 1-9
Author(s):  
Yun-Tao Zhao ◽  
Lei Gan ◽  
Wei-Gang Li ◽  
Ao Liu
Keyword(s):  
Path Planning ◽  
Density Estimation ◽  
Spot Welding ◽  
Teaching Method ◽  
Grey Wolf ◽  
Pareto Solution ◽  
Multi Objective ◽  
Operation Rules ◽  
Welding Sequence ◽  
Body In White

The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces.

Evaluating Genetic Algorithms on Welding Sequence Optimization With Respect to Dimensional Variation and Cycle Time

2011 ◽  
Author(s):  
Johan Segeborn ◽  
Johan S. Carlson ◽  
Kristina Wa¨rmefjord ◽  
Rikard So¨derberg
Keyword(s):  
Genetic Algorithms ◽  
Significant Influence ◽  
Cycle Time ◽  
Spot Welding ◽  
Fitness Function ◽  
Welding Robot ◽  
Single Criterion ◽  
Welding Sequence ◽  
Dimensional Variation ◽  
Welding Sequences

Spot welding is the predominant joining method in car body assembly. Spot welding sequences have a significant influence on the dimensional variation of resulting assemblies and ultimately on overall product quality. It also has a significant influence on welding robot cycle time and thus ultimately on manufacturing cost. In this work we evaluate the performance of Genetic Algorithms, GAs, on multi-criteria optimization of welding sequence with respect to dimensional assembly variation and welding robot cycle time. Reference assemblies are fully modelled in 3D including detailed fixtures, welding robots and weld guns. Dimensional variation is obtained using variation simulation and part measurement data. Cycle time is obtained using automatic robot path planning. GAs are not guaranteed to find the global optimum. Besides exhaustive calculations, there is no way to determine how close to the actual optimum a GA trial has reached. Furthermore, sequence fitness evaluations constitute the absolute majority of optimization computation running time and do thus need to be kept to a minimum. Therefore, for two industrial reference assemblies we investigate the number of fitness evaluations that is required to find a sequence that is optimal or a near-optimal with respect to the fitness function. The fitness function in this work is a single criterion based on a weighted and normalized combination of dimensional variation and cycle time. Both reference assemblies involves 7 spot welds which entails 7!=5040 possible welding sequences. For both reference assemblies, dimensional variation and cycle time is exhaustively calculated for all 5040 possible sequences, determining the optimal sequence, with respect to the fitness function, for a fact. Then a GA that utilizes Random Key Encoding is applied on both cases and the performance is recorded. It is found that in searching through about 1% of the possible sequences, optimum is reached in about half of the trials and 80–90% of the trials reach the ten best sequences. Furthermore the optimum of the single criterion fitness function entails dimensional variation and cycle time fairly close to their respective optimum. In conclusion, this work indicates that genetic algorithms are highly effective in optimizing welding sequence with respect to dimensional variation and cycle time.

A Novel Rule-Based Method for Individualized Spot Welding Sequence Optimization With Respect to Geometrical Quality

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Roham Sadeghi Tabar ◽  
Kristina Wärmefjord ◽  
Rikard Söderberg ◽  
Lars Lindkvist
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Spot Welding ◽  
Search Time ◽  
Search Problem ◽  
Optimization Approach ◽  
Hamiltonian Graph ◽  
Rule Based ◽  
Geometrical Quality ◽  
Welding Sequence

Abstract Spot welding is the predominant joining process for the sheet metal assemblies. The assemblies, during this process, are mainly bent and deformed. These deformations, along with the single part variations, are the primary sources of the aesthetic and functional geometrical problems in an assembly. The sequence of welding has a considerable effect on the geometrical variation of the final assembly. Finding the optimal weld sequence for the geometrical quality can be categorized as a combinatorial Hamiltonian graph search problem. Exhaustive search to find the optimum, using the finite element method simulations in the computer-aided tolerancing tools, is a time-consuming and thereby infeasible task. Applying the genetic algorithm to this problem can considerably reduce the search time, but finding the global optimum is not guaranteed, and still, a large number of sequences need to be evaluated. The effectiveness of these types of algorithms is dependent on the quality of the initial solutions. Previous studies have attempted to solve this problem by random initiation of the population in the genetic algorithm. In this paper, a rule-based approach for initiating the genetic algorithm for spot weld sequencing is introduced. The optimization approach is applied to three automotive sheet metal assemblies for evaluation. The results show that the proposed method improves the computation time and effectiveness of the genetic algorithm.

Efficient Spot Welding Sequence Optimization in a Geometry Assurance Digital Twin

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Roham Sadeghi Tabar ◽  
Kristina Wärmefjord ◽  
Rikard Söderberg ◽  
Lars Lindkvist
Keyword(s):  
Spot Welding ◽  
Welding Process ◽  
Combinatorial Problems ◽  
Optimal Sequence ◽  
Sequence Optimization ◽  
Digital Twin ◽  
Final Assembly ◽  
Geometrical Quality ◽  
Welding Sequence ◽  
The Individual

Abstract A digital twin for geometry assurance contains a set of analyses that are performed to steer the real production for securing the geometry of the final assembly. In sheet metal assemblies, spot welding is performed to join the parts together. The sequence of the welding has a considerable influence on the geometrical outcome of the final assembly. In industry, the sequence of welding to secure the geometry is mainly derived by tacit manufacturing knowledge. Including such knowledge to mimic the production process requires extensive knowledge management, and the result might be just a good enough solution. Theoretically, spot welding sequence optimization for the optimal geometrical quality is among NP-hard combinatorial problems. In a geometry assurance digital twin, where assembly parameters are selected for the individual assemblies, time constraints define the quality of the optimal sequence. In this paper, an efficient method for spot welding sequence optimization with regards to the geometrical quality is introduced. The results indicate that the proposed method reduces 60–80% of the time for the sequencing of the spot welding process to achieve the optimal geometrical quality.

Welding sequence optimisation in the automotive industry: A review

2019 ◽  
Vol 233 (17) ◽  
pp. 5945-5952
Author(s):  
Varshan Beik ◽  
Hormoz Marzbani ◽  
Reza Jazar
Keyword(s):  
Automotive Industry ◽  
Spot Welding ◽  
Fast Rate ◽  
Welding Process ◽  
Future Research ◽  
Automotive Body ◽  
Welding Sequence ◽  
Common Technique ◽  
The Common

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.

Efficient Variation Simulation of Spot-Welded Assemblies

2018 ◽  
Author(s):  
Samuel Lorin ◽  
Björn Lindau ◽  
Roham Sadeghi Tabar ◽  
Lars Lindkvist ◽  
Kristina Wärmefjord ◽  
...  
Keyword(s):  
Spot Welding ◽  
The Finite Element Method ◽  
Influence Coefficients ◽  
Industrial Use ◽  
The Aesthetic ◽  
New Formulation ◽  
Variation Simulation ◽  
Simulation Time ◽  
Important Activity ◽  
Spot Welded

Variation simulation for assembled products is one important activity during product development. Variation simulation enables the designer to understand not only the features of the nominal product but also how uncertainty will affect production, functions and the aesthetic properties of the final product. For parts that are able to deform during assembly, compliant variation simulation is needed for accurate prediction. For this the Finite Element Method (FEM) is used. Despite many effective efforts to decrease simulation times for compliant variation simulation, simulation time is still considered an obstacle for full scale industrial use. In this paper, a new formulation for compliant variation simulation of assemblies that are joined in sequential spot-welding will be presented. In this formulation the deformation in the intermediate springback steps during the simulation of a spot-weld sequence do not have to be calculated. This is one of the most time consuming steps in sequential spot-welding simulation. Furthermore, avoiding the intermediate springback calculation will reduce the size of memory of the computer models since the number of sensitivity matrices is reduced. The formulation is implemented using the latest developments in compliant variation simulation, that is the Method of Influence Coefficients (MIC) where the Sherman-Morrison-Woodbury-formula is used to update the resulting sensitivity matrices and the contact- and weld forces are solved using a Quadratic Programme (QP). Industrial cases are used to demonstrate the reduced simulation time. It is believed that the reduction in simulation times will have future implications on sequence optimization for spot-welded assemblies.

scholarly journals A new surrogate model–based method for individualized spot welding sequence optimization with respect to geometrical quality

2019 ◽  
Vol 106 (5-6) ◽  
pp. 2333-2346 ◽  
Author(s):  
Roham Sadeghi Tabar ◽  
Kristina Wärmefjord ◽  
Rikard Söderberg
Keyword(s):  
Surrogate Model ◽  
Spot Welding ◽  
Fem Simulation ◽  
Global Optimum ◽  
Neural Network Approach ◽  
Model Based ◽  
Heuristic Search Algorithms ◽  
Geometrical Quality ◽  
Functional Quality ◽  
Welding Sequence

AbstractIn an individualized shee metal assembly line, form and dimensional variation of the in-going parts and different disturbances from the assembly process result in the final geometrical deviations. Securing the final geometrical requirements in the sheet metal assemblies is of importance for achieving aesthetic and functional quality. Spot welding sequence is one of the influential contributors to the final geometrical deviation. Evaluating spot welding sequences to retrieve lower geometrical deviations is computationally expensive. In a geometry assurance digital twin, where assembly parameters are set to reach an optimal geometrical outcome, a limited time is available for performing this computation. Building a surrogate model based on the physical experiment data for each assembly is time-consuming. Performing heuristic search algorithms, together with the FEM simulation, requires extensive evaluations times. In this paper, a neural network approach is introduced for building surrogate models of the individual assemblies. The surrogate model builds the relationship between the spot welding sequence and geometrical deviation. The approach results in a drastic reduction in evaluation time, up to 90%, compared to the genetic algorithm, while reaching a geometrical deviation with marginal error from the global optimum after welding in a sequence.

scholarly journals Optimization of Spot Welding of An Assembly Like B-pillar of a Car For Minimum Distortion, By Sequencing Technique

2013 ◽  
pp. 295-300
Author(s):  
K. D. Hardikar ◽  
K. H. Inamdar ◽  
D. J. Nidgalkar
Keyword(s):  
Sheet Metal ◽  
Spot Welding ◽  
Spot Welds ◽  
Sequence Change ◽  
Welding Sequence ◽  
Sequencing Technique ◽  
Sheet Metal Assembly ◽  
Minimum Distortion

The aim of this paper is to achieve optimisation of spot welding sequence to minimise the distortion of a sheet metal assembly. The distortion of the assembly involving number of spot welds is different for different sequences of welding The assembly consists of sheet metal components which are joined by using various welding sequence schemes. The components are manufactured in quantity and welding with various sequences. After welding the distortions in an assembly due to welding sequence change are worked out and compaired. The sequence with minimum distortion is suggested a solution for the quality manufacturing with minimum distortion induced in it.

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