Stainless steel 316 has wide applications in different industries due to its distinct mechanical and corrosion resistance properties. In this paper, a new adaptive design approach is presented and discussed to optimize the machining process when face milling of Stainless steel 316 under different cooling and lubrication strategies. The proposed approach considers three process designs, namely: balanced, cost, and quality-oriented designs. Experimental runs were modeled using a support vector regression model, which was later used as objective functions for the particle swarm optimization algorithm. Finally, the technique of order preference similarity to the ideal solution (TOPSIS) was adopted with different weightings for different design criteria. In general, MQL and higher cutting speed are favorable in all designs. In contrast, lower feed rates are recommended in the quality-oriented design, compared to the cost-oriented one. This analysis does not only provide an adaptive design, but it also utilizes this approach to come up with different designs, each biased toward one of the responses. The approach and methodology are described in a straightforward, intuitive manner, which makes it replicable in different machining operations and even in other engineering optimization problems sharing the same nature of the present problem of interest.
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