Genetic algorithm optimization based on manufacturing prediction for an efficient tolerance allocation approach

Maroua Ghali (), Sami Elghali () and Nizar Aifaoui ()
Additional contact information
Maroua Ghali: University of Monastir, National School of Engineers of Monastir (LGM_ENIM)
Sami Elghali: University of Sousse, National Engineering School of Sousse (ENISO)
Nizar Aifaoui: University of Monastir, National School of Engineers of Monastir (LGM_ENIM)

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 4, No 12, 1649-1670

Abstract: Abstract The tolerance allocation is an extremely sensitive task due to the complex effects on quality, product, and cost. Thus, tolerance allocation optimization covers design and manufacturing aspects and can help to bridge the gap between tolerance design and manufacturing process. Consequently, the objective of this paper is to establish a tolerance optimization method based on manufacturing difficulty computation using the genetic algorithm method with optimum parameters. To do this, the objective function of the proposed GA algorithm is to minimize the total cost. The proposed GA constraints are the tolerance equations of functional requirements considering difficulty coefficients. The manufacturing difficulty computation is based on tools for the study and analysis of reliability of the design or the process, as the Failure Mode, Effects and Criticality Analysis (FMECA) and Ishikawa diagram. The proposed approach, based on combining the Difficulty Coefficient Computation (DCC) and the GA optimization method produces the GADCC tool. This model is applied on mechanical assemblies taken from the literature and compared to previous methods regarding tolerance values and computed total cost. This comparative study highlights the benefits of the accomplished GADCC optimization method. The results lead to obtain optimal tolerances that minimize the total cost and respect the functional, quality and manufacturing requirements.

Keywords: Tolerance allocation; Tolerance optimization; Genetic algorithm; Manufacturing difficulty; Total cost computation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02132-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02132-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02132-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().