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

Author

Listed:
  • 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))

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.

Suggested Citation

  • Maroua Ghali & Sami Elghali & Nizar Aifaoui, 2024. "Genetic algorithm optimization based on manufacturing prediction for an efficient tolerance allocation approach," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1649-1670, April.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:4:d:10.1007_s10845-023-02132-1
    DOI: 10.1007/s10845-023-02132-1
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    References listed on IDEAS

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    1. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.
    2. Ghaderi, A. & Hassani, H. & Khodaygan, S., 2021. "A Bayesian-reliability based multi-objective optimization for tolerance design of mechanical assemblies," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    3. Yueyi Zhang & Lixiang Li & Mingshun Song & Ronghua Yi, 2019. "Optimal tolerance design of hierarchical products based on quality loss function," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 185-192, January.
    4. Lu-jun Cui & Man-ying Sun & Yan-long Cao & Qi-jian Zhao & Wen-han Zeng & Shi-rui Guo, 2021. "A novel tolerance geometric method based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 799-821, March.
    5. Eduardo Umaras & Ahmad Barari & Marcos Sales Guerra Tsuzuki, 2021. "Tolerance analysis based on Monte Carlo simulation: a case of an automotive water pump design optimization," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1883-1897, October.
    6. A. Sanz-Lobera & Emilio Gómez & Jesús Pérez & Lorenzo Sevilla, 2016. "A proposal of cost-tolerance models directly collected from the manufacturing process," International Journal of Production Research, Taylor & Francis Journals, vol. 54(15), pages 4584-4598, August.
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