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<p>Deterministic optimization of machining processes in the manufacturing industry usually leads to suboptimal results with a high failure probability. This is due to the uncertainty and random variation of the input data which can be derived from diverse sources. Therefore, the purpose of this research work is to introduce a probabilistic optimization (PO) for handling manufacturing processes in the presence of uncertainties. First, a new PO approach (ESMV-GOA) is developed based on integrating the strategy of enriched self-adjusted mean value (ESMV) into the grasshopper optimization algorithm (GOA). Then, the proposed approach is applied to select the optimal machining parameters of a well-known grinding optimization problem. The obtained results indicate that the ESMV-GOA is a competent tool for optimizing manufacturing problems while guaranteeing the desired reliability level.</p>

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Probabilistic optimization of grinding processes in manufacturing industry

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Hamza Ferhat,
  • Gao Liang,
  • Ferhat Djeddou

摘要

Deterministic optimization of machining processes in the manufacturing industry usually leads to suboptimal results with a high failure probability. This is due to the uncertainty and random variation of the input data which can be derived from diverse sources. Therefore, the purpose of this research work is to introduce a probabilistic optimization (PO) for handling manufacturing processes in the presence of uncertainties. First, a new PO approach (ESMV-GOA) is developed based on integrating the strategy of enriched self-adjusted mean value (ESMV) into the grasshopper optimization algorithm (GOA). Then, the proposed approach is applied to select the optimal machining parameters of a well-known grinding optimization problem. The obtained results indicate that the ESMV-GOA is a competent tool for optimizing manufacturing problems while guaranteeing the desired reliability level.