Assembly planning using a novel immune approach

Robotic Assembly Sequence Planning and Optimization by Cuckoo Search Algorithm

“…The optimum set of parameters found to be able to provide the optimal/near optimal assembly sequence in reasonable time is population size of 10, fraction of worst eggs that are thrown out 10%, probability used in generating new eggs to replace those that are thrown out 90%. After the CS algorithm is run using the above set of parameters, the optimal assembly sequence obtained is [1,2,3,4,5,6,7,8,9,10,11,12,13,14] with fitness value of 1.5269, requiring 7 tool changes, 4 reorientation changes, stability index of 9, having the base component at the first location of the assembly sequence, and most importantly the sequence was found to have no feasibility (precedence) violations. In order to evaluate the effectiveness of the proposed CS algorithm, it is compared with Improved Harmony Search (IHS) algorithm [12].…”

“…In order to evaluate the effectiveness of the proposed CS algorithm, it is compared with Improved Harmony Search (IHS) algorithm [12]. The best sequence generated by the IHS algorithm is found to be [ 6,11,12,13,14,7,8,9,10] with fitness value of 1.5077, requiring 7 tool changes, 5 reorientation changes, stability index of 9, having base component at the first location of the assembly sequence, no feasibility violations but is clearly sub-optimal when compared to the sequence generated by CS algorithm. Figure 8 shows comparison between the convergence graphs of the CS and IHS algorithms from which it can be concluded that the proposed CS algorithm clearly outperforms IHS in terms of convergence speed as it is able to reach the global best fitness value in lesser number of iterations.…”

“…Components [2,5,6] are missing and components [3,4] 3 Robot task level planning for implementation of generated assembly sequence To implement the generated sequence for robotic assembly, it must be converted into a task level plan and executable robot level program. A task level planning strategy for robotic assembly has been developed by a knowledge-based system implemented in Expert System shell, CLIPS [19].…”

“…Mishra and Deb [5] proposed a GA based parameter meta-optimization of ACO algorithm for solving assembly sequence optimization. Cao and Xiao [6] developed Immune Optimization Algorithm (IOA) based on bionic principles of Artificial Immune Systems (AIS). Assembly sequences were evaluated based on number of components, assembly directions, tools changes and base component location.…”

Survey on assembly sequencing: a combinatorial and geometrical perspective

“…Closely related to GA, another population-based optimization technique, called Immune Optimization Approach (IOA) uses the bionic principles of Artificial Immune Systems (AIS) Cao and Xiao (2007). The assembly sequencing problems are represented as antigens, and antibodies represent the assembly sequences of the product, encoded in their genes as component numbers.…”