A Genetic Algorithm Approach to Weld Pattern Optimization in Sheet Metal Assembly

2003 ◽  
Author(s):  
Gene Y. Liao
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Optimization Method ◽  
Structural Deformation ◽  
Assembly Process ◽  
Sheet Metal Parts ◽  
Final Assembly ◽  
Welding Operation ◽  
Genetic Algorithm Approach ◽  
Sheet Metal Assembly

In sheet metal assembly process, welding operation joins two or more sheet metal parts together. Since sheet metals are subject to dimensional variation resulted from manufacturing randomness, gap may be generated at each weld pair prior to welding. These gaps are forced to close during a welding operation and accordingly undesirable structural deformation results. Optimizing the welding pattern (the number and locations of weld pairs) of an assembly process was proven to significantly improve the quality of final assembly. This paper presents a Genetic Algorithm (GA)-based optimization method to automatically search for the optimal weld pattern so that the assembly deformation is minimized. Application result of a real industrial part demonstrated that the proposed algorithm effectively achieve the objective.

A genetic algorithm-based fixture locating positions and clamping schemes optimization

2003 ◽  
Vol 217 (8) ◽  
pp. 1075-1083 ◽  
Author(s):  
Y G Liao
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Optimization Method ◽  
Form Accuracy ◽  
Dimensional Variation ◽  
Stationary Set ◽  
Machining Fixtures ◽  
Sheet Metal Assembly ◽  
Industrial Part ◽  
Selection Of

Optimizing the locator positions and clamping schemes of a fixture was proven to improve the dimensional and form accuracy of a workpiece significantly. A number of approaches have been developed to optimize the designs of sheet-metal assembly fixtures and machining fixtures. However, in these previous works, the optimal selection of the positions of locators and clamps were based on a stationary set of locator and clamp conditions; i.e. the numbers of the locators and clamps were fixed during optimization. This paper proposes a genetic algorithm (GA)-based optimization method to select automatically the optimal numbers of locators and clamps as well as their optimal positions in sheet-metal assembly fixtures, such that the workpiece deformation due to the gravity effect and resulting variation due to part dimensional variation are simultaneously minimized. The application result of a real industrial part demonstrated that the proposed algorithm effectively achieves the objective.

The Simple Genetic Algorithm Approach for Optimization of Nesting of Sheet Metal Parts in Blanking Operation

2015 ◽  
Vol 14 (01) ◽  
pp. 41-53 ◽  
Author(s):  
K. Ramesh ◽  
N. Baskar
Keyword(s):  
Genetic Algorithm ◽  
Sheet Metal ◽  
Optimization Problem ◽  
Cutting Stock ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard Problems ◽  
Maximum Utilization

The two-dimensional (2D) cutting stock is a common problem arising in the sheet metal industries, lock industries, textile industries, etc. Here, the problem is to reduce the wastage in order to increase the profit. This problem is also called as the general 2D problem or NP hard problems. The choice of chromosome representation in genetic algorithm (GA) depends on the variables of the optimization problem being solved. The main objectives of the work are the maximum utilization of part in the sheet and also minimizing the wastage.

Optimization of Fixture and Joint Positions in Sheet Metal Assembly: The Effect of Fixture Numbers and Constraints

2011 ◽  
Author(s):  
Kambiz Haji Hajikolaei ◽  
G. Gary Wang
Keyword(s):  
Sheet Metal ◽  
Metal Forming ◽  
Optimization Method ◽  
Stochastic Methods ◽  
Objective Functions ◽  
Variation Analysis ◽  
Spring Back ◽  
Final Assembly ◽  
Deterministic Methods ◽  
Sheet Metal Assembly

Assembly process is widely used in the manufacturing processes. Fabrication processes such as machining, casting and metal forming are not perfect and introduce variation in the components. Variations of components and tools accumulate and cause the assembly variation. In this paper, after reviewing the literature and presenting sheet metal assembly variation analysis, an optimization method is used to minimize the assembly variation by optimizing the location of joints and fixtures. The model is constructed in ANSYS with three fixtures and two joints. When a black-box function calculated numerically in software is used as the objective function, using deterministic methods for optimization is not suitable because the deterministic methods need knowledge of the objective functions. Also, using stochastic methods such as genetic algorithm is not suitable because of the large number of function evaluations they normally need. In this paper, an optimization algorithm based on mode-pursuing sampling (MPS) method is used to minimize the assembly variation. The optimization method is explained and after implementing the method, results are presented. It is learned that, in addition to the number of fixtures, the constraints on neighboring fixture locations also affect the optimal fixture layout, as well as the final assembly stiffness and spring-back.

Variation Analysis for Compliant Assembly

2000 ◽  
Author(s):  
S. Jack Hu ◽  
Yufeng Long ◽  
Jaime Camelio
Keyword(s):  
Sheet Metal ◽  
Assembly Line ◽  
Assembly Process ◽  
Variation Analysis ◽  
Assembly Lines ◽  
Compliant Assembly ◽  
Single Station ◽  
Dimensional Variation ◽  
Sheet Metal Assembly

Abstract Assembly processes for compliant non-rigid parts are widely used in manufacturing automobiles, furniture, and electronic appliances. One of the major issues in the sheet metal assembly process is to control the dimensional variation of assemblies throughout the assembly line. This paper provides an overview of the recent development in variation analysis for compliant assembly. First, the unique characteristics of compliant assemblies are discussed. Then, various approaches to variation modeling for compliant assemblies are presented for single station and multi-station assembly lines. Finally, examples are given to demonstrate the applications of compliant assembly variation models.

Analysis of Nonlinear Variation Accumulation in Auto-Body Assembly Process

Manufacturing ◽  
2002 ◽  
Author(s):  
Jun Lian ◽  
Zhongqin Lin ◽  
Fusheng Yao ◽  
Xinmin Lai
Keyword(s):  
Sheet Metal ◽  
State Equation ◽  
Assembly Process ◽  
Transformation Mechanism ◽  
Metal Parts ◽  
Sheet Metal Parts ◽  
Accumulation Mechanism ◽  
Auto Body ◽  
Non Gaussian ◽  
Geometrical Dimensions

In the assembly process of auto-body, variations in the geometrical dimensions of sheet metal parts and fixtures are inevitable. These variations accumulate through the multi-station assembly process to form the dimensional variations of the final products. Compared with the assembly of rigid parts, the assembly process of the elastic parts is more complex because the variation accumulation patterns rely much on the variations of fixture, jointing methods and mechanical deformation. This paper aims at analyzing the variation transformation mechanism and accumulation characteristics for the assembly of sheet metal parts based on the analysis of dimensional coordination relations among parts and fixtures. Finite element method (FEM) and Monte-Carlo Simulation (MCS) were used to analyze the effect of jointing contact on variation transformation, while a state equation was developed to describe the variation accumulation mechanism. The result of the analysis indicates that the main characteristics of elastic assembly jointing are the overlap jointing methods and elastic contacts action. The fact that the variation transform coefficients (VTC) are variable makes the assembly variation distribution Non-Gaussian even if the dimension variation of parts is Gaussian distribution. The analysis conclusions have potential value for more reasonable tolerance synthesis of elastic parts assembly.

Genetic Algorithm Approach to Optimize Biodiesel Production by Ultrasonic System

2014 ◽  
Vol 9 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Ebrahim Fayyazi ◽  
Barat Ghobadian ◽  
Gholamhassan Najafi ◽  
Bahram Hosseinzadeh
Keyword(s):  
Genetic Algorithm ◽  
Catalyst Concentration ◽  
Ultrasonic Method ◽  
Research Work ◽  
Optimization Method ◽  
Waste Cooking Oil ◽  
Biodiesel Production ◽  
Molar Ratio ◽  
Genetic Algorithm Approach ◽  
Mixing Method

Abstract Ultrasonic processing is an effective tool to attain required mixing while providing the necessary activation energy in the field of biofuels. In this regard, optimization of fast transesterification of waste cooking oil is very important. The goal of this research paper is therefore to determine the effect of important parameters such as methanol to oil molar ratio, catalyst concentration (potassium hydroxide), temperature, and horn position on oil conversion to methyl ester in ultrasonic mixing method. Result of experiments showed that the optimum conditions for the transesterification process have been obtained as molar ratio of alcohol to oil as 6:1, catalyst concentration of 1 wt.%, temperature as 45°C, and horn position at the interface of methanol to oil. The results show that the ultrasonic method decreases the reaction time as much as up to eight times compare to the conventional stirring. For practically evaluating the theoretical optimum point using genetic algorithm, the obtained values were verified experimentally. In order to perform this, the catalyst concentration, temperature, and the time of reaction were determined, and the values are 1%, 48°C, and 449s, respectively. For the obtained values, the biodiesel conversion was 93.2%, so that the experimental optimum value is closed to that of the theoretical values. As a result, experimental data confirmed the obtained values from optimization method in this research work.

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.

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