Robust Product Design: A Modern View of Quality Engineering in Manufacturing Systems

Abstract

One of the main technological and economic challenges for an engineer is designing high-quality products in manufacturing processes. Most of these processes involve a large number of variables included the setting of controllable (design) and uncontrollable (noise) variables. Robust Design (RD) method uses a collection of mathematical and statistical tools to study a large number of variables in the process with a minimum value of computational cost. Robust design method tries to make high-quality products according to customers’ viewpoints with an acceptable profit margin. This paper aims to provide a brief up-to-date review of the latest development of RD method particularly applied in manufacturing systems. The basic concepts of the quality loss function, orthogonal array, and crossed array design are explained. According to robust design approach, two classifications are presented, first for different types of factors, and second for different types of data. This classification plays an important role in determining the number of necessity replications for experiments and choose the best method for analyzing data. In addition, the combination of RD method with some other optimization methods applied in designing and optimizing of processes are discussed.

Optimization of Machining Parameters on the Surface Roughness of Aluminum in CNC Turning Process Using Taguchi Method

“…The experimental method used in this research is the Taguchi Method. The Taguchi Method employs a special orthogonal array (OA), signal-to-noise ratio (S/N), main effects, and analysis of variance (ANOVA) [17]. The signal-to-noise (S/N) ratio can be calculated in three ways: the nominal is the best, the larger is the better, and the smaller is the better.…”