Using memetic algorithms with guided local search to solve assembly sequence planning
Expert Systems with Applications: An International Journal
Collaborative process planning and manufacturing in product lifecycle management
Computers in Industry
Symbolic OBDD representations for mechanical assembly sequences
Computer-Aided Design
A hierarchical approach on assembly sequence planning and optimal sequences analyzing
Robotics and Computer-Integrated Manufacturing
Robotics and Computer-Integrated Manufacturing
A mereotopological product relationship description approach for assembly oriented design
Robotics and Computer-Integrated Manufacturing
Disparate attributes algorithm for semantic assembly design rule management
Advanced Engineering Informatics
Mechanical assembly planning using ant colony optimization
Computer-Aided Design
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The paper describes a novel framework for an assembly-oriented design (AOD) approach as a new functional product lifecycle management (PLM) strategy, by considering product design and assembly sequence planning phases concurrently. Integration issues of product life cycle into the product development process have received much attention over the last two decades, especially at the detailed design stage. The main objective of the research is to define assembly sequence into preliminary design stages by introducing and applying assembly process knowledge in order to provide an assembly context knowledge to support life-oriented product development process, particularly for product structuring. The proposed framework highlights a novel algorithm based on a mathematical model integrating boundary conditions related to DFA rules, engineering decisions for assembly sequence and the product structure definition. This framework has been implemented in a new system called PEGASUS considered as an AOD module for a PLM system. A case study of applying the framework to a catalytic-converter and diesel particulate filter sub-system, belonging to an exhaust system from an industrial automotive supplier, is introduced to illustrate the efficiency of the proposed AOD methodology.