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.

Strategies for Optimization of Spot Welding Sequence With Respect to Geometrical Variation in Sheet Metal Assemblies

2010 ◽  
Author(s):  
Kristina Wa¨rmefjord ◽  
Rikard So¨derberg ◽  
Lars Lindkvist
Keyword(s):  
Case Studies ◽  
Sheet Metal ◽  
Spot Welding ◽  
Optimal Sequence ◽  
Factors Affecting ◽  
Critical Dimensions ◽  
Sheet Metal Parts ◽  
Geometrical Quality ◽  
Welding Sequence ◽  
Geometrical Variation

During the assembly process of sheet metal parts, a lot of factors affect the final geometrical quality. It is important to have knowledge about the characteristics of as many as possible of those factors, not only to be able to reduce their effect, but also to be able to include those factors in variation simulations. Those tolerance simulations are crucial tools in early stages in automotive industry in order to predict the outcome in critical dimensions and it is of course important to have as good accuracy as possible in the simulations. One of the factors affecting the final geometry is the spot welding sequence. In this paper it is shown how the spot welding sequence affects the amount of geometrical variation in a sheet metal assembly. A method for including the welding sequence in tolerance simulations is described. Of course, it is desirable to find an optimal sequence, i.e. a sequence that minimizes the geometrical variation in the final assembly. Since this is a fast growing problem — the number of possible sequences for N welding points is N!, it is not practicable to test all possible sequences. In this work some different strategies for finding an optimal sequence are tested on several industrial case studies. The tested strategies are based on general guidelines, on minimizing variation in each welding step respectively calculations of the movements in unwelded points in each step. The strategies based on general guidelines was not successful, neither was the one based on minimization of the variation in each step. The strategy based on movements in the unwelded points seems however promising. It resulted in the best or one of the better sequences for all of the eight tested industrial case studies.

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.

GA BASED HYBRID FUZZY RULE OPTIMIZATION APPROACH FOR ELEVATOR GROUP CONTROL SYSTEM

2013 ◽  
Vol 37 (3) ◽  
pp. 937-947 ◽  
Author(s):  
Ta-Cheng Chen ◽  
Yuan-Yong Hsu ◽  
An-Chen Lee ◽  
Shiang-Yu Wang
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Service Quality ◽  
Fuzzy Rule ◽  
Optimization Approach ◽  
Rule Based ◽  
Group Control ◽  
Elevator Group Control ◽  
Traditional Approaches ◽  
Rule Optimization

Elevators are the essential transportation tools in high buildings so that elevator group control system (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rule based intelligent EGCS optimized by genetic algorithm has been proposed where the rules with the corresponding parameters are generated optimally so as to maximize service quality. The experimental results show that the performance of our approach is superior to these of traditional approaches in the literature.

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.

scholarly journals Efficient Spot Welding Sequence Simulation in Compliant Variation Simulation

2020 ◽  
Author(s):  
Roham Sadeghi Tabar ◽  
Samuel Lorin ◽  
Christoffer Cromvik ◽  
Lars Lindkvist ◽  
Kristina Wärmefjord ◽  
...  
Keyword(s):  
Sheet Metal ◽  
Spot Welding ◽  
Traditional Approach ◽  
Optimal Sequence ◽  
Sequence Optimization ◽  
Welding Operation ◽  
Welding Sequence ◽  
Geometric Variation ◽  
Inspection Fixture ◽  
Assembly Fixture

Abstract Geometric variation is one of the sources of quality issues in a product. Spot welding is an operation that impacts the final geometric variation of a sheet metal assembly considerably. Evaluating the outcome of the assembly, considering the existing geometrical variation between the components can be achieved using the Method of Influence Coefficients (MIC), based on the Finite Element Method (FEM). The sequence, with which the spot welding operation is performed, influences the final geometric deformations of the assembly. Finding the optimal sequence that results in the minimum geometric deformation is a combinatorial problem that is experimentally and computationally expensive. For an assembly with N number of welds, there are N! possible sequences to perform the spot welding operation. Traditionally, spot welding optimization strategies have been to simulate the geometric variation of the spot-welded assembly after the assembly has been positioned in an inspection fixture, using an appropriate measure of variation. In this approach, the calculation of deformation after springback is one of the most time-consuming steps. In this paper, the cause of variation in the deformations after the springback, between different sequences is identified. The relative displacements of the weld points in the assembly fixture, when welded in a sequence, is the source of such behavior. Capturing these displacements leads to large time savings during sequence optimization. Moreover, this approach is independent of the inspection fixture. The relative weld displacements have been evaluated on two sheet metal assemblies. The sequence optimization problem has been solved for the two assemblies using this approach. The optimal sequence, the corresponding final assembly deformations, and the time-consumption have been compared to the traditional approach. The results show a significant correlation between the weld relative displacements in the assembly fixture, and the assembly deformation in the inspection fixture. Considering the relative weld displacement makes each assembly evaluation less time-consuming, and thereby, sequence optimization time can be reduced up to 30%, compared to the traditional approach.

Rule-Based and Genetic Algorithm for Automatic Gamelan Music Composition

2017 ◽  
Vol 10 (3) ◽  
pp. 202
Author(s):  
Khafiizh Hastuti ◽  
Azhari Azhari ◽  
Aina Musdholifah ◽  
Rahayu Supanggah
Keyword(s):  
Genetic Algorithm ◽  
Music Composition ◽  
Rule Based

Rule Based Network Security Using Genetic Algorithm

2007 ◽  
Vol 1 (4) ◽  
pp. 85-91
Author(s):  
Jeya S ◽  
◽  
Ramar K ◽  
Keyword(s):  
Genetic Algorithm ◽  
Network Security ◽  
Rule Based

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

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