Probabilistic optimization of grinding processes in manufacturing industry

作     者：Ferhat, Hamza Liang, Gao Djeddou, Ferhat 

作者机构：Setif 1 Univ Inst Opt & Precis Mech Appl Precis Mech Lab Setif 19000 Algeria Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY》 (Int J Adv Manuf Technol)

年 卷 期：2025年第136卷第10期

页      面：4525-4534页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

基　　金：Algerian Ministry of Higher Education and Scientific Research (CNEPRU) [J0301220110033] 

主　　题：Probabilistic optimization Manufacturing Grinding operation Enriched self-adjusted mean value Grasshopper optimization algorithm 

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.