Minimizing assembly variation in selective assembly for auto-body parts based on IGAOT

2018 ◽  
Vol 11 (2) ◽  
pp. 254-268 ◽  
Author(s):  
Yanfeng Xing ◽  
Yansong Wang
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Rigid Bodies ◽  
Influence Coefficient ◽  
Body Parts ◽  
Selective Assembly ◽  
Improved Genetic Algorithm ◽  
Content Type ◽  
Sheet Metal Parts ◽  
Assembly Tolerance

PurposeDimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.Design/methodology/approachThe method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.FindingsThe cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.Originality/valueThe research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

A new assembly variation analysis model based on the method of power balance for auto-body parts

2014 ◽  
Vol 34 (3) ◽  
pp. 296-302 ◽  
Author(s):  
Yanfeng Xing ◽  
Yansong Wang
Keyword(s):  
Sheet Metal ◽  
Power Balance ◽  
Influence Coefficient ◽  
Body Parts ◽  
Variation Analysis ◽  
Analysis Model ◽  
Content Type ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Auto Body

Purpose – The purpose of this paper is to propose a new assembly variation analysis model to analyze assembly variation for sheet metal parts. The main focus is to analyze assembly processes based on the method of power balance. Design/methodology/approach – Starting with issues in assembly variation analysis, the review shows the critical aspects of tolerance analysis. The method of influence coefficient (MIC) cannot accurately analyze the relationship between part variations and assembly variations, as the welding point is not a point but a small area. Therefore, new sensitivity matrices are generated based on the method of power balance. Findings – Here two cases illustrate the processes of assembly variation analysis, and the results indicate that new method has higher accuracy than the MIC. Research limitations/implications – This study is limited to assembly variation analysis for sheet metal parts, which can be used in auto-body and airplane body. Originality/value – This paper provides a new assembly variation analysis based on the method of power balance.

Path optimization of CNC PCB drilling using hybrid Taguchi genetic algorithm

Kybernetes ◽  
2016 ◽  
Vol 45 (1) ◽  
pp. 107-125 ◽  
Author(s):  
Dony Hidayat Al-Janan ◽  
Tung-Kuan Liu
Keyword(s):  
Genetic Algorithm ◽  
Printed Circuit Boards ◽  
Numerical Control ◽  
Circuit Boards ◽  
Improved Genetic Algorithm ◽  
Solution Quality ◽  
Content Type ◽  
Neighbor Search ◽  
Feasible Solutions ◽  
The Traveling Salesman Problem

Purpose – In this study, the hybrid Taguchi genetic algorithm (HTGA) was used to optimize the computer numerical control-printed circuit boards drilling path. The optimization was performed by searching for the shortest route for the drilling path. The number of feasible solutions is exponentially related to the number of hole positions. The paper aims to discuss these issues. Design/methodology/approach – Therefore, a traveling cutting tool problem (TCP), which is similar to the traveling salesman problem, was used to evaluate the drilling path; this evaluation is considered an NP-hard problem. In this paper, an improved genetic algorithm embedded in the Taguchi method and a neighbor search method are proposed for improving the solution quality. The classical TCP problems proposed by Lim et al. (2014) were used for validating the performance of the proposed algorithm. Findings – Results showed that the proposed algorithm outperforms a previous study in robustness and convergence speed. Originality/value – The HTGA has not been used for optimizing the drilling path. This study shows that the HTGA can be applied to complex problems.

Deformable Sheet Metal Fixturing: Principles, Algorithms, and Simulations

1996 ◽  
Vol 118 (3) ◽  
pp. 318-324 ◽  
Author(s):  
W. Cai ◽  
S. J. Hu ◽  
J. X. Yuan
Keyword(s):  
Sheet Metal ◽  
Rigid Bodies ◽  
Fixture Design ◽  
Total Deformation ◽  
Element Analysis ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Manufacturing Operations ◽  
Nonlinear Programming Methods ◽  
Simulation Package

Fixture design is an important consideration in all manufacturing operations. Central to this design is selecting and positioning the locating points. While substantial literature exists in this area, most of it is for prismatic or solid workpieces. This paper deals with sheet metal fixture design. An “N-2-1” locating principle has been proposed and verified to be valid for deformable sheet metal parts as compared to the widely accepted “3-2-1” principle for rigid bodies. Based on the “N-2-1” principle algorithms for optimal fixture design are presented using finite element analysis and nonlinear programming methods to find the best “N” locating points such that total deformation of the deformable sheet metal is minimized. A simulation package called OFixDesign is introduced and numerical examples are presented to validate the “N-2-1” principle and optimal sheet metal fixture design approach.

An improved sexual genetic algorithm for solving 0/1 multidimensional knapsack problem

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soukaina Laabadi ◽  
Mohamed Naimi ◽  
Hassan El Amri ◽  
Boujemâa Achchab
Keyword(s):  
Genetic Algorithm ◽  
Sexual Selection ◽  
Combinatorial Optimization ◽  
Knapsack Problem ◽  
Search Space ◽  
Multidimensional Knapsack Problem ◽  
Improved Genetic Algorithm ◽  
Two Stage ◽  
Content Type ◽  
Multidimensional Knapsack

Purpose The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover operators that cooperate to explore the search space. Design/methodology/approach The authors first present a new sexual selection strategy that significantly improves the one proposed by (Varnamkhasti and Lee, 2012), while working in phenotype space. Then they propose two variants of the two-stage recombination operator of (Aghezzaf and Naimi, 2009), while they adapt the latter in the context of 0/1 MKP. The authors evaluate the efficiency of both proposed operators on a large set of 0/1 MKP benchmark instances. The obtained results are compared against that of conventional selection and crossover operators, in terms of solution quality and computing time. Findings The paper shows that the proposed selection respects the two major factors of any metaheuristic: exploration and exploitation aspects. Furthermore, the first variant of the two-stage recombination operator pushes the search space towards exploitation, while the second variant increases the genetic diversity. The paper then demonstrates that the improved genetic algorithm combining the two proposed operators is a competitive method for solving the 0/1 MKP. Practical implications Although only 0/1 MKP standard instances were tested in the empirical experiments in this paper, the improved genetic algorithm can be used as a powerful tool to solve many real-world applications of 0/1 MKP, as the latter models several industrial and investment issues. Moreover, the proposed selection and crossover operators can be incorporated into other bio-inspired algorithms to improve their performance. Furthermore, the two proposed operators can be adapted to solve other binary combinatorial optimization problems. Originality/value This research study provides an effective solution for a well-known non-deterministic polynomial-time (NP)-hard combinatorial optimization problem; that is 0/1 MKP, by tackling it with an improved genetic algorithm. The proposed evolutionary mechanism is based on two new genetic operators. The first proposed operator is a new and deeply different variant of the so-called sexual selection that has been rarely addressed in the literature. The second proposed operator is an adaptation of the two-stage recombination operator in the 0/1 MKP context. This adaptation results in two variants of the two-stage recombination operator that aim to improve the quality of encountered solutions, while taking advantage of the sexual selection criteria to prevent the classical issue of genetic algorithm that is premature convergence.

Variation Analysis of Sheet Metal Assemblies with Fixture Configuration

2012 ◽  
Vol 170-173 ◽  
pp. 3283-3287
Author(s):  
Yan Feng Xing ◽  
Yan Song Wang ◽  
Xiao Yu Zhao
Keyword(s):  
Sheet Metal ◽  
Influence Coefficient ◽  
Variation Analysis ◽  
Wind Noise ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Linear Relationships ◽  
Deviation Propagation ◽  
Fixture Configuration ◽  
Assembly Variation Modeling

The dimensional quality of auto-body relates to the whole external appearance and wind noise, the effect of closing the door and even driving smoothness of vehicles. The deviation propagation can be analyzed through the method of influence coefficient (MIC). However, fixture deviation usually impacts on the assembly variation than part variation. A variation analysis modeling with fixture configuration is presented to improve the current variation analysis efficiency of sheet metal parts. The variation change process of part, fixture and assembly was analyzed by researching the locating and assembly process of sheet metal parts. The linear relationships among variations of parts, fixtures and assemblies are established with two locating principles of “N-2-1” and “3-2-1”. Moreover, in accordance with the different release modes of the fixture locating points after the assembly, the assembly variation modeling is established in the two modes of over-constrained release and full release. Finally, a case of sheet metal assembly is illustrated to show the effectiveness of the assembly variation analysis modeling.

Evolutionary Grasp Planning for Sheet Metal Parts With Multi-Type Grippers

2021 ◽  
Author(s):  
Jicmat Ali Tribaldos ◽  
Chiradeep Sen
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Sensitivity Analyses ◽  
Grasp Planning ◽  
Sheet Metal Parts ◽  
Manufacturing Operations ◽  
Custom Made ◽  
Suction Cups ◽  
End Effectors ◽  
Gripping Force

Abstract Grasping sheet metal objects for manufacturing operations requires custom-made robot-mounted end-effectors to grip the parts. Modern end-effectors use multi-type grasp where a combination of gripper types such as suction cups, magnets, and fingers may be used. This paper presents a genetic algorithm-based approach of grasp design automation. The algorithm first generates an option space of possible grasping locations by analyzing the geometry of the sheet metal part and then uses a genetic algorithm to optimize the grasp using up to five magnets and suction cups. The algorithm includes as fitness criteria the factor of safety of the total gripping force against part weight, the unbalanced moment created by the gripping forces and part weight, the cost of the grasp, and three combinations of these parameters. The GA features asexual reproduction, mutation, and elitism. The algorithm is implemented in the Siemens NX™ Knowledge Fusion language and on Microsoft VBA code. The paper presents detailed test results and sensitivity analyses that indicate that genetic algorithms can produce viable solutions for multi-type grasp configurations and that the algorithm behaves in response to varying its control parameters in ways that are logically anticipated.

Modeling and predicting of aeronautical thin-walled sheet metal parts riveting deformation

2016 ◽  
Vol 36 (3) ◽  
pp. 295-307 ◽  
Author(s):  
Zhengping Chang ◽  
Zhongqi Wang ◽  
Bo Jiang ◽  
Jinming Zhang ◽  
Feiyan Guo ◽  
...  
Keyword(s):  
Sheet Metal ◽  
Element Model ◽  
Local Deformation ◽  
Calculation Model ◽  
Content Type ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Thin Walled ◽  
The Impact ◽  
Successive Calculation

Purpose Riveting deformation is inevitable because of local relatively large material flows and typical compliant parts assembly, which affect the final product dimensional quality and fatigue durability. However, traditional approaches are concentrated on elastic assembly variation simulation and do not consider the impact of local plastic deformation. This paper aims to present a successive calculation model to study the riveting deformation where local deformation is taken into consideration. Design/methodology/approach Based on the material constitutive model and friction coefficient obtained by experiments, an accurate three-dimensional finite element model was built primarily using ABAQUS and was verified by experiments. A successive calculation model of predicting riveting deformation was implemented by the Python and Matlab and was solved by the ABAQUS. Finally, three configuration experiments were conducted to evaluate the effectiveness of the model. Findings The model predicting results, obtained from two simple coupons and a wing panel, showed that it was a good compliant with the experimental results, and the riveting sequences had a significant effect on the distribution and magnitude of deformation. Practical implications The proposed model of predicting the deformation from riveting process was available in the early design stages, and some efficient suggestions for controlling deformation could be obtained. Originality/value A new predicting model of thin-walled sheet metal parts riveting deformation was presented to help the engineers to predict and control the assembly deformation more exactly.

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