Hybridizing Lion and Dragonfly Algorithm for Robotic Assembly subsets recognition model

Abstract

Nowadays, a lot of interrelated shape products have been proposed by manufacturing industries for diverse applications in diverse fields like aerospace, defense, as well as space centers. Because of assembly operation, 30 percent of time utilization occurs in manufacturing while comparing with the residual processes in manufacturing. Attaining the optimal sequence is highly complex due to the assembly sequence planning which is a multi-model optimization issue. The probable number of sequences increases exponentially as the number of parts in the assembly raises consequently attaining the optimal assembly sequences which are very complex and have time utilization. To attain the assembly sequences, there subsist numerous mathematical approaches. Nevertheless, current researches affirm that they carry out poorly when it comes to multi-objective optimal assembly sequence. Nowadays, numerous studies have worked on various soft computingbased approaches to solve assembly sequence issues. Here, the assembly subset recognition model is developed. The developed model is used initially to resolve the assembly sequence issues. This approach eradicates aforesaid assembly sets which have numerous directional changes and need high energy. Here, the Lion Mutated and Updated Dragon Algorithm (LMU-DA) is proposed, which is the theoretical hybridization of the Lion Algorithm (LA) and Dragonfly Algorithm (DA). This technique is evaluated with the conventional approaches and it is attained to be efficient in obtaining the optimal assembly sequence for an industrial product.