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The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> = 0.9893 and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.<\/jats:p>","DOI":"10.1017\/s0890060422000087","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T09:11:40Z","timestamp":1663578700000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":14,"title":["A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning 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