Title: Simulated annealing algorithm for minimising assembly variation in nonlinear assembly

Authors: R. Sivasubramanian; G. Venkatesan; M. Sivakumar; R. Sivasankaran

Addresses: Mangayarkarasi College of Engineering, Mangayarkarasi Nagar, Paravai, Madurai – 625-402, Tamil Nadu, India ' Department of Mechanical Engineering, Sethu Institute of Technology, Pulloor, Kariapatti Virudhunagar Dist., 626-115, Tamil Nadu, India ' Sree Sowdambika College of Engineering, Chettikurichi, Aruppukkottai, Virudhunagar Dist., 626-101, Tamil Nadu, India ' Department of Mechanical Engineering, Thiagarajar College of Engineering, Madurai – 625-015, Tamil Nadu, India

Abstract: Precision assemblies need close tolerance components. A close tolerance component requires secondary operations, which increase the manufacturing cost considerably. Selective assembly (SA) methods like uniform grouping, equal probability, uniform tolerance etc., was discussed in the literature focus generally on reducing surplus parts or minimising clearance variation in the linear assembly. Moreover, the existing techniques use component's tolerance for obtaining the best bin combinations rather than using the component's dimension. The present work aims to obtain maximum number of products with closer assembly specification from wider tolerance sub components of a nonlinear assembly by mating their component's dimensions based on the best bin combinations. Overrunning clutch assembly (OCA) has been considered as an example problem, in which the sub components are manufactured with wide tolerance and partitioned into three to ten bins. Combinations of best bins have been obtained for various assembly specifications by implementing simulated annealing algorithm (SAA). The proposed method has proved its effectiveness by showing 24.9% of cost saving in making OCA.

Keywords: tolerance; clearance variation; nonlinear assembly; over running clutch assembly; simulated annealing algorithm; SAA.

DOI: 10.1504/IJMTM.2021.121571

International Journal of Manufacturing Technology and Management, 2021 Vol.35 No.5, pp.422 - 442

Received: 05 Dec 2016
Accepted: 05 Nov 2017

Published online: 21 Mar 2022 *

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