TY - GEN
T1 - Multidisciplinary analysis and product family optimization of front-loading washing machines
AU - Jung, Sangjin
AU - Simpson, Timothy W.
N1 - Funding Information:
The authors gratefully acknowledge funding from the National Science Foundation under Grant CMMI-1436236. Any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the National Science Foundation.
PY - 2016
Y1 - 2016
N2 - In the past decade, the market share of front-loading washing machines has seen explosive growth in the United States. As a result, many companies are now offering families of front-loading washing machines with a variety of features and options. Understanding the tradeoffs within these product families is challenging as existing research has focused primarily on a single disciplinary analysis (e.g., dynamic analysis, strength analysis); few models exist for cleanliness, reliability, energy efficiency, etc. In this paper, we introduce a new integrated multidisciplinary analysis using simulations, mathematical models, and response surface models based on the ratings of product attributes. In order to determine feasible design solutions for a front-loading washer family, we formulate a product family design problem using deviation functions and a product family penalty function. A multi-objective genetic algorithm is applied to solve the proposed formulation, and the results indicate that designers can successfully determine solutions for the best performance, most common, and compromise families of front-loading washers.
AB - In the past decade, the market share of front-loading washing machines has seen explosive growth in the United States. As a result, many companies are now offering families of front-loading washing machines with a variety of features and options. Understanding the tradeoffs within these product families is challenging as existing research has focused primarily on a single disciplinary analysis (e.g., dynamic analysis, strength analysis); few models exist for cleanliness, reliability, energy efficiency, etc. In this paper, we introduce a new integrated multidisciplinary analysis using simulations, mathematical models, and response surface models based on the ratings of product attributes. In order to determine feasible design solutions for a front-loading washer family, we formulate a product family design problem using deviation functions and a product family penalty function. A multi-objective genetic algorithm is applied to solve the proposed formulation, and the results indicate that designers can successfully determine solutions for the best performance, most common, and compromise families of front-loading washers.
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U2 - 10.1115/DETC2016-59520
DO - 10.1115/DETC2016-59520
M3 - Conference contribution
AN - SCOPUS:85007560395
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 42nd Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2016
Y2 - 21 August 2016 through 24 August 2016
ER -
