ABSTRACT Based on general principles of robust design and axiomatic design, relationship among robustness, structural parameters, design parameters and uncontrollable factors has been established. Various factors that affect system robustness were analyzed mathematically to determine the relationship between robustness and structural characteristics of the linear system. The relations among functional requirements were also explored. Accordingly, an optimization model was established to determine design parameters. This new robust design approach can be used for linear mechanical system analysis. Keywords: Quality, Robust, Sensitivity. 1 INTRODUCTION Quality is a primary factor in determining whether a product is successful in the market place. It can be evaluated if the product performs the intended functions. The intended functionality may be deviated by variations resulted from raw materials, manufacturing processes, and/or operational environments. To minimize the effects of the variations on functions, the functions pf product and system should be made insensitive to those variations. Dr.Taguchi has proposed robust design in 1970s' and this method has been widely used in industry Analytical robust design approach is a new method for design of robust mechanical systems. Through mapping from design parameters to functional requirements of system, this method aims to analyze the intrinsic relationships among structural characteristics, design parameters, uncontrollable factors and robustness of linear system. Based on Suh's Axiomatic Design and robust analysis of traditional robust design approach [3], this paper reports on studies of models of linear mechanical system for robust analysis, sensitivity index of system, and describes a optimal model of analytical robust design, reveals the primary factors on robustness. Examples were included to demonstrate this new approach. ROBUST ANALYSIS Models The factors that influence functions of linear system could be divided into controllable factors and uncontrollable factors. It is important to distinguish these factors appropriately and establish analytical models accordingly
