Process and machine selection in sampling-based tolerance-cost optimisation for dimensional tolerancing

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.

KEYWORDS:

• Tolerance-cost optimisation
• optimal tolerance allocation
• alternative machine/process selection
• mixed-integer optimisation
• statistical tolerance analysis
• process planning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The selection process in tolerance-cost optimisation is methodically the same if it has to be chosen between different machine alternatives, which can handle one or several different processes, or between different process alternatives themselves. Since both terms are often used synonymously in literature, it is called machine/process selection in this article.

Additional information

Funding

This work was supported by the Deutsche Forschungsgemeinschaft [German Research Foundation (DFG)] under the grant number WA 2913/25-1: ‘Tolerance optimisation of statically under- and over-constrained assemblies’.

Notes on contributors

Martin Hallmann

Martin Hallmann is a research Associate and PhD student in the research group ‘dimensional management’ at the Institute of Engineering Design of the Friedrich-Alexander-Universität Erlangen-Nürnberg. In his current research, he focuses on virtual product development and computer-aided tolerancing. His area of expertise is the usage of optimisation techniques for least-cost tolerance design, which is also known under the term tolerance-cost optimisation.

Benjamin Schleich

Benjamin Schleich is a Senior Engineer at the Institute of Engineering Design of the Friedrich-Alexander-Universität Erlangen-Nürnberg and leader of the research groups ‘dimensional management’ and ‘digital engineering’. His research mainly focuses on computer-aided tolerancing, geometrical variations management, and digitalisation in virtual and digital product development.

Sandro Wartzack

Sandro Wartzack is Full Professor at the Friedrich-Alexander-Universität of Erlangen-Nürnberg. After completing his studies in production engineering in 1994, he carried out research as a research assistant in virtual product development and was awarded a doctorate in 2000 with summa cum laude. In his subsequent industrial activity in the automotive industry between 2001 and 2009, he held various management positions, most recently as head of simulation and knowledge management. In 2009 he was appointed professor at the FAU Erlangen-Nürnberg for Engineering Design and was subsequently chaired Full Professor. His research focuses on the large field of Digital Product Development with emphasis on artificial intelligence in product development, design assistance systems, tolerance simulation and analysis with digital human models.