A new module partition method based on the criterion and noise functions of robust design

Against the sweeping trend of mass customization, the importance of product platform design is becoming increasingly recognized by the manufacturers. Module design is the foundation of product platform design, and module partition determines the effectiveness of module design. Traditionally, the vast majority of existing module partition methods ignored the design factor of customer preferences. This study proposes to employ the basic principles of robust design to make the module partition schemes less sensitive to the dynamically changing customer preferences by considering them as a noise factor. A criterion function and a noise function are each established based on the component-component correlation matrix and component-function contribution matrix, respectively. The criterion and noise functions, when combined, lead to a unique multi-objective optimization problem. Furthermore, an improved Pareto archive particle swarm optimization (PAPSO) algorithm is introduced to solve the multi-objective optimization problem in order to prevent the premature selections of non-optimal solutions. A case study is presented to showcase how the proposed new method is followed to conduct the module partition on an electric-traction drum shearer. The improved algorithm demonstrates highly competitive performance in comparison to the existing multi-objective optimization algorithms.