A Hybrid DMAIC Framework for Integrating Response Surface Methodology and Multi-Objective Optimization Methods

Abstract In many practical situations, it is important to evaluate the relationships between the factors that compose an industrial process and their effects on one or more response variables that are of interest to an enterprise. The main contribution of this present study is to propose a new conceptual hybrid framework based on the DMAIC (Define, Measure, Analyze, Im- prove, and Control) methodological structure, to optimize complex experimental problems with multiple responses. This procedure combines Response Surface Methodology, with the Desirability (D), Modified Desirability (MD), Compromise Programming (CP) functions, with Generalized Reduced Gradient (GRG) and Evolutionary Algorithms (EA). We made real application to a glass lamination process case study to describe how to use the proposed framework. The procedure allowed several configurations to be tested involving the D, MD and CP functions, adopting the GRD and EV, to optimize the studied industrial process. The best configuration was defined by a practical confirmation experiment and validated by company engineers and experts. As examples of the advantages of adopting the proposed framework in the glass lamination problems, the best solutions resulted in a 49,86% increase in grinding wheel shelf life, corresponding to a 927[kg] reduction of steel-use per year, and a 41.7% reduction in dressing stone consumption, saving 17,200 stones per year.