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

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 p...

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Veröffentlicht in:International journal of production research Jg. 50; H. 15; S. 4127 - 4140
Hauptverfasser: Kafashi, Sajad, Shakeri, Mohsen, Abedini, Vahid
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis Group 01.08.2012
Taylor & Francis
Taylor & Francis LLC
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Automation
CAD/CAM
CAM
CAPP
CIM
Component parts
Computer aided design
Computer aided manufacturing
Computer science; control theory; systems
Exact sciences and technology
Mechanical engineering. Machine design
operation sequence
Optimization
Optimization algorithms
particle swarm optimisation
Process planning
Searching
setup planning
Software
Studies
Task planning (robotics)
Theoretical computing
tolerance relation analysis
Tolerances
CAPP
Costs
particle swarm optimisation
Evolutionary algorithm
Machining
CAD/CAM
Quality assurance
setup planning
Efficiency
operation sequence
tolerance relation analysis
Particle system
Database
Swarm intelligence
Computer aided design
Quality management
Production process
Local search
Assisted planning
Computer integrated manufacturing
Process planning
CIM
Tolerance analysis
Particle swarm optimization
Constrained optimization
Engineering design
Case study
Particle method
Trapping
Imperfect information
Dimensional tolerance
ISSN:0020-7543, 1366-588X
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Zusammenfassung: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.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2011.592157