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|Title:||Methodology for automatic operation sequencing for progressive die design||Authors:||Chu, Changyong||Keywords:||DRNTU::Engineering::Industrial engineering::Automation||Issue Date:||2007||Source:||Chu, C. Y. (2007). Methodology for automatic operation sequencing for progressive die design. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Sheet metal components produced by progressive dies play an important role in modern day life. Progressive die design in most industries remains an art rather than a science. The job is mainly carried out manually, based on individual designer's skills and knowledge, which are accumulated primarily through work experience. Strip layout is the most critical task in progressive die design. The optimal die operation sequence enables both the precise part production and the manufacturing cost reduction. The aim of this research is to develop a novel, rigorous approach for strip layout design in progressive die design. The proposed approach incorporates rules and heuristics used by human process planning experts and synthesizes the reasoning process using graph theoretic algorithms. Two types of constraints between stamping operations are identified in this work: cluster and precedence constraints. Operations have cluster constraints should be performed simultaneously at the same station. While a precedence constraint implies that an operation or operation cluster must be performed before another operation or cluster. The proposed approach first generates the stamping features from the sheet metal features consisting of the CAD model of stamping part to-be-manufactured. In this process, the blank orientation is determined using nesting algorithm to obtain the optimal material utilization. The sheet metal features in the CAD part model are mapped to stamping features in a strip model. Auxiliary stamping features (pilot features and scrap features) are devised by the process planner. Then, stamping features are mapped to stamping operations, followed by generating the operation precedence and adjacency graphs. Next the operation precedence graph is verified to be acyclic using a colored DFS (depth-first search). Based on the operation precedence graph, a modified topological sort algorithm is applied to cluster the operations into partially ordered sets. Finally, a graph colouring algorithm is applied to the operation adjacency graph on the partially ordered operation sets.||Description:||139 p.||URI:||https://hdl.handle.net/10356/36136||DOI:||10.32657/10356/36136||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||MAE Theses|
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