Process and machine selection in sampling-based tolerance-cost optimisation for dimensional tolerancing. Issue 17 (2nd September 2022)

• Record Type: Journal Article
• Authors: Hallmann, Martin; Schleich, Benjamin; Wartzack, Sandro
• Is Part Of: International journal of production research. Volume 60:Issue 17(2022)
• Page Start: 5201
• Page End: 5216
• Publication Date: 2022-09-02
• Subjects: Tolerance-cost optimisation -- optimal tolerance allocation -- alternative machine/process selection -- mixed-integer optimisation -- statistical tolerance analysis -- process planning; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2021.1951867
• Languages: English
• ISSNs: 0020-7543

Abstract

ABSTRACT: Tolerance-cost optimisation, i.e. using optimisation techniques for tolerance allocation, is frequently used to determine a cost-efficient tolerance design that can meet the stringent requirements on high-quality products. Besides various manufacturing aspects, the selection of available alternative machines and processes hold great potential for an early optimal process planning by identifying their best combination. Although machine/process selection by minimum cost and mixed-integer optimisation is often applied in theory and practice, their proper implementation in tolerance-cost optimisation based on sampling techniques for tolerance analysis, which can statistically consider various individual part tolerance distributions, has not been studied so far. With the aim to overcome this drawback, this article focuses on machine/process selection in sampling-based tolerance-cost optimisation for dimensional tolerances considering the respective machine characteristics of several machine options, e.g. process capabilities and manufacturing distributions. A comparative study proves that machine/process selection by mixed-integer optimisation leads to minimum total manufacturing costs since it covers the whole search space, including all technically feasible machine combinations and thus identifies the global cost minimum.