Robust Design of Families of Products With Production Modeling and Evaluation

Abstract

The effectiveness of manufacturing enterprises that compete with product families can be leveraged through an appropriate standardization of components. In this paper we examine how a robust standardization of components can be implemented in the early stages of design with an explicit evaluation of the production system. The approach is based on (1) a mathematical formulation of design decisions using the Compromise Decision Support Problem (DSP), which includes robustness considerations, and (2) modeling production systems as networks of response surfaces. This modeling method facilitates evaluation of the impact of product design changes on the performance of the production system, thus enabling concurrent product-process design exploration. We demonstrate the approach with a case study, namely, the design of an absorber-evaporator module for a family of absorption chillers.

A Single-Stage Gradient-Based Approach for Solving the Joint Product Family Platform Selection and Design Problem Using Decomposition

“…In the application we choose the normalized l 1 norm and divide by the number of variables to measure the average deviation in each component. For example, ∆x 1 is defined as follows: (12) where x imax and x imin represent the upper and lower bounds for the design variables respectively. Performance targets for individual products are listed in Table 3.…”

“…Simpson et al [1] reviews and compares forty approaches addressing the product family optimization problem. According to this classification, some methods limit scope in order to reduce complexity by assuming that design variables defining product platforms are known a priori and are not treated as variables in the optimization process (Allada and Jiang [2]; Blackenfelt [3], D'souza and Simpson [4], Dai and Scott [5], Farrell and Simpson [6], Fellini et al [7],;Gonzales-Zugasti et al [10], [11], Hernandez et al [12], Kokkolaras et al [13], Kumar et al [14], Li and Azarm [15], Messac et al [16], Nelson et al [17], Ortega et al [18], Seepersad et al [19], [20], Simpson et al [21], [22], Willcox and Wakayama [23]). However, other approaches optimize for the platform selection and product family design simultaneously; that is, platforms are specified a posteriori (Akundi et al [24], Cetin and Saitou [25], de Weck et al [26], Fellini et al, [27], [28], Fujita and Yoshida [29], Gonzales-Zugasti and Otto [30], Hernandez et al [31], [32], Messac et al [33], Nayak et al [34], Rai and Allada [35], Hassan et al [36], Simpson and D'souza [37], Fujita et al [38], Khire and Messac [39], Khajavirad et al [40]).…”

Online Case Studies and a Process Description for Product Platform Planning

“…Additionally, a multi-criteria framework has been developed to evaluate preliminary product platform concepts as the platform elements and characteristics require continuous measurement and refinement in the conceptual design phase [1,27,49]. A technique that is gaining ground for product family specifications is that of compromise Decision Support Problem (DSP) [38,11,24,37]. Here, the platform and variant requirements and targets are formulated as a multi-objective program in order to optimize conflicting targets.…”

Robust design optimization with bivariate quality characteristics

“…Robust design can be performed through either statistical experiments (Phadke 1995) or model-based optimization (Gu et al 2000;Chen et al 1996Chen et al , 1997Allen et al 2006;Hernandez et al 2001;McAllister and Simpson 2003;Mourelatos and Liang 2006;Youn and Xi 2009;Giassi et al 2004;Han and Kwak 2004;Lee et al 2009). Instead of experimentally estimating the quality characteristics, the latter method computationally evaluates quality characteristics with computational models.…”