Robotic Optimal Assembly Sequence Using Improved Cuckoo Search Algorithm

Abstract The demand for manufacturing newer products are increasing day-by-day, keeping this demand in mind many modern manufacturing processes have been evolved to meet the demand and supply the product in time. Even though many modern methods have been evolved, still there is lack in time to meet the consumer’s requirements. This is due to assembly, which takes 20% of cost in manufacturing. To do effective assembly, optimal sequence is required; achieving the optimal assembly sequence is a difficulty process because it is one of them Non Probabilistic (NP) hard combinatorial problems. Achieving an effective optimal assembly sequence involves more than one objective function to develop the fitness equation (number of directional changes, gripper changes, time of assembly etc.), which converts the problem into discrete optimization problem. At the starting stages of assembly planning, researchers implemented mathematical models to achieve the feasible solution. These methods performs very poorly when comes to large part assemblies. Meanwhile, Artificial Intelligence (AI) techniques are evolved to solve the Assembly Sequence Planning (ASP) Problems. Performances of these methods are quite impressive in solving ASP problems, but most of these algorithms fall in local optimal during execution. More over these methods consumes more time for getting optimal solution especially for the more part assemblies. Keeping the above difficulties in mind, in this paper an Improved Cuckoo Search (ICS) algorithm is implemented to obtain the optimal solution. The proposed algorithm is compared by considering two assemblies (wall rack assembly and eccentric milling machine) with the algorithms like Genetic Algorithm (GA), Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO) algorithm and Hybrid Ant Wolf Algorithm (HAWA). The results of the different algorithms are compared in terms of number of iterations and fitness values with the proposed algorithm. The results show that the proposed algorithm performs better than the compared algorithms.