A Multistage Approach to the Selective Assembly of Components Without Dimensional Distribution Assumptions

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Abolfazl Rezaei Aderiani ◽  
Kristina Wärmefjord ◽  
Rikard Söderberg
Keyword(s):  
Genetic Algorithm ◽  
Past Research ◽  
Selective Assembly ◽  
Quality Product ◽  
Dimensional Distribution ◽  
Surplus Parts

Selective assembly is a means of obtaining higher quality product assemblies by using relatively low-quality components. Components are selected and classified according to their dimensions and then assembled. Past research has often focused on components that have normal dimensional distributions to try to find assemblies with minimal variation and surplus parts. This paper presents a multistage approach to selective assembly for all distributions of components and with no surplus, thus offering less variation compared to similar approaches. The problem is divided into different stages and a genetic algorithm (GA) is used to find the best combination of groups of parts in each stage. This approach is applied to two available cases from the literature. The results show improvement of up to 20% in variation compared to past approaches.

A discrete fireworks optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution

2019 ◽  
Vol 39 (2) ◽  
pp. 323-344 ◽  
Author(s):  
Zhenyu Liu ◽  
Zhang Nan ◽  
Chan Qiu ◽  
Jianrong Tan ◽  
Jingsong Zhou ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Selection Strategy ◽  
Selective Assembly ◽  
Content Type ◽  
Assembly Method ◽  
Combination Scheme ◽  
Dimensional Distribution ◽  
Assembly Problem ◽  
Specific Mapping ◽  
Surplus Parts

Purpose The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution. Design/methodology/approach In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm. Findings The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods. Practical implications The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts. Originality/value A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

Minimizing Surplus Parts in Selective Assembly Using GA

2012 ◽  
Vol 215-216 ◽  
pp. 178-181 ◽  
Author(s):  
Jun Feng Fei ◽  
Cong Lu ◽  
Song Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
High Precision ◽  
Chromosome Structure ◽  
Selective Assembly ◽  
Number Of Components ◽  
Grouping Method ◽  
Standard Deviations ◽  
The Difference ◽  
Surplus Parts

Selective assembly is a method of obtaining high-precision assemblies from relatively low-precision components. In selective assembly, the mating parts are manufactured with wide tolerances. It is impossible that the number of components in the selective group will be the same, and a large number of surplus parts exist according to the difference in the standard deviations of the mating parts. A method with new grouping method and chromosome structure is proposed to minimize surplus parts by using genetic algorithm.

The method of selective assembly for the RV reducer based on genetic algorithm

2017 ◽  
Vol 232 (6) ◽  
pp. 921-929 ◽  
Author(s):  
Xuyang Chu ◽  
Huihuang Xu ◽  
Xiaomin Wu ◽  
Jiping Tao ◽  
Guifang Shao
Keyword(s):  
Genetic Algorithm ◽  
Complex Structure ◽  
Selective Assembly ◽  
Matching Algorithm ◽  
Assembly Method ◽  
Assembly Accuracy ◽  
Manufacturing Errors ◽  
Rv Reducer ◽  
Assembly Precision ◽  
Very High

As a precision gear reducer, the RV reducer has a low-transmission backlash (very high assembly accuracy). Therefore, the selective assembly method is the only assembly method which can guarantee the assembly precision of the RV reducer. However, the RV reducer has a complex structure; it consists of a high number of parts whose machining tolerance cannot be very low. Furthermore, there are numerous parts, the tolerances of which influence the RV reducer transmission backlash. Therefore, it is difficult to achieve high assembly accuracy by using the traditional selective assembly method. In this paper, a method of selective assembly is proposed to make the backlash of the RV reducer meet the requirements through the analysis of the characteristics of the RV reducer structure, the processing and assembly process of the parts, and the influence of manufacturing errors on the backlash. Then, a mathematical model was established for the RV reducer assembly issue. And a matching algorithm based on a genetic algorithm was developed. Finally, the algorithm was applied to the selective assembly of the RV reducer for verifying the feasibility and validity of the proposed matching method.

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 approach to nullify surplus parts in selective assembly

2011 ◽  
Vol 3 (5/6) ◽  
pp. 492
Author(s):  
S. Saravana Sankar ◽  
S.G. Ponnambalam ◽  
M. Victor Raj
Keyword(s):  
Selective Assembly ◽  
New Approach ◽  
Surplus Parts

scholarly journals Moisture Estimation in Cabinet Dryer with Thin-Layer Relationships Using Genetic Algorithm and Neural Network

2019 ◽  
Author(s):  
Behzad Maleki ◽  
Mahyar Ghazvini ◽  
Mohammad Hossein Ahmadi ◽  
Heydar Maddah ◽  
Shahab Shamshirband
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Thin Layer ◽  
Experimental Models ◽  
Moisture Loss ◽  
Squared Error ◽  
The Neural Network ◽  
Quality Product ◽  
Neural Network Structure ◽  
Moisture Estimation

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

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