Optimization of robotic assembly sequence

Abstract

The assembly process is combination of several products into a single product. The assembly process affects manufacturing processes very great extent because it is very time consuming and expensive process. The cost of assembly can reach up to 30% of the manufacturing cost. Instability and direction change in assembly process increases the cost of assembly thus the total cost of product is increased very great extent. The production rate decreases with increase in time in assembly process, so the correct assembly sequence is needed to reduce the time and cost of assembly. For the given product assembly model, the sequences and paths of parts is determined by assembly sequence planning (ASP) to obtain the assembly with minimum costs and shortest time. Industries are taking interest in automated assembly system; robotic assembly system comes under category of this assembly system which uses robots for performing the required assembly tasks. This system is one of the most flexible assembly systems to assemble various parts into desired assembly. Robotic assembly systems can handle a wide range of styles and products, so that same product can be assembled different ways, and to recover from errors. Robotic assembly has the ability to switch to different products and styles because robotic assembly is programmable assembly and it has advantage of greater process capability. Robotic assembly is faster, more efficient and precise than any conventional process. It is very important to determine the feasible,stable and optimal assembly sequence for an assembly system. An assembly sequence plan is a high level plan for constructing a product from its component parts. It specifies which sets of parts form subassemblies, the order in which parts and subassemblies are to be inserted into each subassembly,are to be performed. The aim of the present work is to determine stable, feasible and optimal robotic assembly sequence which follows the assembly constraints and reduces the assembly cost.An important feature of this developing process is epresented by the need to automatically determine the assembly plan by recognizing the optimum sequence iv of operations based upon cost and accuracy. Products with large number of parts have several alternative feasible sequences among which optimal assembly sequence is generated. Traditional methods often generate combinatorial explosions of alternatives, with intolerable computational times. A new methodology has been developed to find out the best robotic assembly sequence among the feasible robotic sequences. The feasible robotic assembly sequences have been generated based on the assembly constraints and later, Artificial Immune System (AIS) and particle swarm optimization with mutation operation has been applied to generate feasible and optimal assembly sequences and result is compared with the previous technique. In AIS Clonal selection and Affinity maturation have been implemented to determine the optimal assembly sequence. During the implementation, each assembly sequence and its energy value have been considered as antibody and the antibody affinity respectively. In PSO, each part of the assembled product is considered as the particle (bird) and mutation operation is performed for selected assembly sequence in each iteration to update the position and velocity of each particle. To generate optimal assembly sequence, a fitness function is generated, which is based on the energy function associated with assembly sequence. The sequence which is having the best fitness value followed by all assembly constraints is treated as the optimal robotic assembly sequence. Present research work has been divided into six chapters. The introduction of the topic and the related matters including the objectives of the work are presented in Chapter 1.The literature reviews on different issues of the topic in Chapter 2. In Chapter 3 Steps of assembly sequence generation,assembly constraints, instability is presented Chapter 4 presents generation of stable assembly sequences using Novel immune approach method and Particle swarm optimization with mutation operation for the generation of robotic assembly sequence. In Chapter 5, Result and discussion obtained from different methods are presented. Finally, Chapter 6 presents the conclusion and future work.