Automated setup planning in CAPP: a modified particle swarm optimisation-based approach

Abstract

Comprehensive process planning is the key technology for linking design and the manufacturing process and is a rather complex and difficult task. Setup planning has a basic role in computer-aided process planning (CAPP) and significantly affects the overall cost and quality of machined parts. This paper presents a generative system and particle swarm optimisation algorithm (PSO) approach to the setup planning of a given part. The proposed approach and optimisation methodology analyses constraints such as the TAD (tool approach direction), the tolerance relation between features and feature precedence relations, to generate all possible process plans using the workshop resource database. Tolerance relation analysis has a significant impact on setup planning to obtain part accuracy. Based on technological constraints, the PSO algorithm approach, which adopts the feature-based representation, optimises the setup planning using cost indices. To avoid becoming trapped in local optima and to explore the search space extensively, several new operators have been developed to improve the particles’ movements, combined into a modified PSO algorithm. A practical case study is illustrated to demonstrate the effectiveness of the algorithm in optimising the setup planning.