A new algorithm for minimizing surplus parts in selective assembly by using genetic algorithm

Abstract

Precision assemblies are produced from low precision subcomponents by partitioning and assembling them randomly from their corresponding groups. Surplus part is one of the important issues, which reduces the implementation of selective assembly in real situations. A new algorithm is introduced in this present paper to reduce surplus parts almost to zero and it is achieved in two stages by using a genetic algorithm. For demonstrating the proposed algorithm, a gearbox shaft assembly is considered as an example problem in which the shaft and pulley are manufactured in wider tolerance and partitioned in three to nine bins. The surplus parts are divided into three bins equally and a best combination of groups is obtained for both cases. It is observed that nearly 995 assemblies are produced out of one thousand subcomponents with the manufacturing cost savings of 19.5% for T _max and 992 assemblies are produced with 13.5% saving in manufacturing cost for 0.9T _max.

Keywords:

• Selective assembly
• Tolerance synthesis
• Manufacturing tolerance
• Manufacturing cost
• Lagrange's multiplier optimization

Notes

† Dimensions and tolerances—inches, cost—American dollars.