Abstract
Most of the engineering products are made with multiple components. The products with multiple subassemblies offer greater flexibility for parallel assembly operation and also disassembly operation during its end of life. Assembly cost and time can be minimized by reducing the number of assembly levels. In this paper, Elephant search algorithm is used to perform Optimal Assembly Sequence Planning (OASP) in order to minimize the number of assembly levels. Subassembly identification technique is used as an integral part of algorithm to identify the parallel assembly possibilities. The proposed method is implemented on industrial products and a detailed comparative assessment has been made with suitable product illustrations to corroborate the efficiency.
Highlights
There is an ever increasing demand for producing new products and supplying into market on-time towards fulfilling the customer needs, which made the industries to look towards new fabrication techniques and assembling methods
Dini has proposed a solution while implementing the Genetic Algorithm (GA), the solution has 12 total assembly levels with 4 identified stable subassemblies (Dini and Santochi, 1992)
Trigui solved the product with disassembly sequence planning approach and obtained a feasible solution with 7 assembly levels (Trigui et al, 2017)
Summary
There is an ever increasing demand for producing new products and supplying into market on-time towards fulfilling the customer needs, which made the industries to look towards new fabrication techniques and assembling methods. The journey of Artificial Intelligence (AI) based Optimal Assembly Sequence Planning (OASP) started with Genetic Algorithms (GA) and Simulated Annealing (SA) in the early 90’s.
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