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作 者:Jian Zhang Bingbing Li Qingjin Peng Peihua Gu
机构地区:[1]Department of Mechanical Engineering,Shantou University,Shantou,515063,China [2]Department of Mechanical Engineering,University of Manitoba,Manitoba,R3T 5V6,Canada [3]International Institute for Innovative Design and Intelligent Manufacturing of Tianjin University,Shaoxing,312000,China [4]School of Mechanical Engineering,Tianjin University,Tianjin,300350,China
出 处:《Chinese Journal of Mechanical Engineering》2023年第1期19-33,共15页中国机械工程学报(英文版)
基 金:National Key R&D Program of China(Grant No.2018YFB1701701);Sailing Talent Program;Guangdong Provincial Science and Technologies Program of China(Grant No.2017B090922008);Special Grand Grant from Tianjin City Government of China。
摘 要:Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.
关 键 词:Modular product design Customer preference Product specifications Correlation analysis Big sales data Electric vehicle
分 类 号:TB472[一般工业技术—工业设计]
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