Applications of Design of Experiments (DOES) in Engineering – State of the Art

Abstract

Design of Experiments (DOE) is an essential statistical methodology for engineering, allowing the systematic analysis of processes and the optimization of products. Its use allows the controlled manipulation of variables to maximize efficiency and quality in several areas, such as chemical, mechanical and materials engineering. However, despite its effectiveness, there are still challenges in the practical implementation of DOE, especially due to the methodological complexity and the lack of training in some industries. The study includes a review of practical cases between 2009 and 2024, selected from the academic database ScienceDirect, which illustrate the applications of DOE in industrial and scientific contexts, with emphasis on the factorial and Taguchi methods. Through DOE, it is possible to identify the most influential variables in a process and determine the ideal conditions to maximize system performance. Methodologies such as the Taguchi method, analysis of variance (ANOVA) and response surfaces allow engineers to explore the simultaneous impact of multiple factors, enabling a robust approach to decision-making in a controlled and efficient manner, with significantly lower costs and efforts. The research results demonstrate applications of DOE in areas such as optimization of machining processes in mechanical engineering, improvement of the thermal efficiency of heat exchangers in chemical engineering, and the development of high-strength metal alloys in materials engineering. In addition, there was an increase in the adoption of DOE in emerging sectors, such as genetic engineering and optimization of advanced materials. It is concluded that DOE is an indispensable tool for innovation and industrial competitiveness, promoting greater efficiency and reliability in processes. However, there is still room for its evolution, mainly in the development of more accessible approaches and in professional training for its effective implementation. This study contributes to the dissemination of knowledge about DOE, encouraging its wider adoption in engineering and driving technological advances in the most diverse areas.