Optimization of Taguchi’s on-line quality feedback control system

Abstract

A feedback control system for quality characteristics is one of the most crucial components in Taguchi’s on-line quality management. In this article, three problems in Taguchi’s on-line quality feedback control system are discussed. Furthermore, countermeasures for improving the system are proposed. First, the quality loss of products that are out of control is reestimated by approximating the probability density of their quality characteristic value to a linear distribution. Second, the quality loss of products that are under control is reestimated by approximating the probability density of their quality characteristic value to a normal distribution. Third, the decreased profit resulting from a process shutdown, which is excluded in Taguchi’s on-line quality feedback control system, is considered in calculating the adjustment cost. The management cost and quality loss constitute the total quality cost. In the improved on-line quality feedback control system, the optimal measurement interval and optimal administrative boundary are calculated by minimizing the total quality cost using an iterative method. An example of the adjustment of the administrative boundary and the measurement interval for manufacturing automobile parts is presented to illustrate the effectiveness of the improved on-line system.

Taguchi–gray relational analysis of eccentricity and tilt of multilens module mount fabricated by injection compression molding

“…where y i is the roundness, flatness, or gray relational grade (see Section 3.3); y is the mean of y i , and n is the number of measured points in each trial. [47][48][49][50][51][52] Table 3 shows the 25 experimental runs in the L 25 (5 6 ) orthogonal array (gray region). The S/N ratios obtained for the roundness and flatness in the corresponding single-objective optimization trials are listed in the two right-most columns of the table.…”

“…Thus, the Taguchi trials were configured in an L 25 (5 6 ) orthogonal array (OA). For each optimization objective (i.e., hole roundness or mount flatness), the quality of the experimental outcome for each run in the Taguchi OA was evaluated using the following smaller‐the‐better signal‐to‐noise ratio ( S / N ) characteristic: $$$$ S/N=-10\log \left({\overline{y}}^2+{S}_n^2\right) $$$$ $$$$ {S}_n=\sqrt{\frac{\sum \limits_{i=1}^n{\left({y}_i-\overline{y}\right)}^2}{n}} $$$$ where $$$ {y}_i $$$ is the roundness, flatness, or gray relational grade (see Section 3.3); $$$ \overline{\mathrm{y}} $$$ is the mean of $$$ {y}_i $$$, and n is the number of measured points in each trial 47–52 …”

Optimization of Injection-Compression Molding Processing Conditions for Fresnel Lens Based on Optical Performance and Geometry Deformation Considerations

“…Thus, in the present study, a simulation approach based on Moldex3D mold flow analysis software [21] is used to determine the optimal settings of the ICM processing conditions which minimize the OPD and OAD of a molded plastic Fresnel lens. The simulations consider three main optimization methods, including the Taguchi analysis method, grey relational analysis (GRA), and a fixed-factor (FF) Taguchi analysis method, where these methods are used either alone, or in conjunction with one another [22,23].…”