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Modular cost model for Tolerance allocation, Process selection and Inspection planning

Article dans une revue avec comité de lecture
Author
ccDANTAN, Jean-Yves
107452 Laboratoire de Conception Fabrication Commande [LCFC]
ccETIENNE, Alain
107452 Laboratoire de Conception Fabrication Commande [LCFC]
MOHAMMADI, Mehrdad
481355 IMT Atlantique [IMT Atlantique]
KHEZRI, Amirhossein
107452 Laboratoire de Conception Fabrication Commande [LCFC]
ccHOMRI, Lazhar
107452 Laboratoire de Conception Fabrication Commande [LCFC]
TAVAKKOLI-MOGHADDAM, Reza
301046 University of Tehran
ccSIADAT, Ali
107452 Laboratoire de Conception Fabrication Commande [LCFC]
185180 Centre de Recherche en Automatique de Nancy [CRAN]

URI
http://hdl.handle.net/10985/23862
DOI
10.1016/j.procir.2022.10.001
Date
2022
Journal
Procedia CIRP

Abstract

The need for highly reliable and precise products has forced industries to study potential uncertainties during designing needed parts. The reliability and acceptance of the product rely on several factors and tolerancing activity plays an important role to assure that the manufactured product meets the requirements. The importance of tolerancing activity can be noticed once designers prefer tight tolerances to ensure product performance and in contrast manufacturers want loose tolerances to reduce manufacturing and assembly complexity and then cost, to decrease the non-conformance rate. Therefore, tolerance allocation and inspection-planning design can be formalized as an optimization problem which the objective function represents the cost impacted by several aspects of the quality management: cost of failure, cost of the inspection. This paper details a modular cost model which includes four components: the manufacturing cost, the inspection cost, the scrap cost (internal failure), and the cost of external failure. Moreover, to improve the efficiency of the cost model, it integrates several factors such as frequencies of the monitoring and inspection activities, probability of conformed product, probability of non-detection of non-conformity, and probability of nondetection of confirmed. The applications of this model are illustrated and demonstrated through an industrial case study.

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