Customer requirement modeling and mapping of numerical control machine

In order to better obtain information about customer requirement and develop products meeting customer requirement, it is necessary to systematically analyze and handle the customer requirement. This article uses the product service system of numerical control machine as research objective and studies the customer requirement modeling and mapping oriented toward configuration design. It introduces the conception of requirement unit, expounds the customer requirement decomposition rules, and establishes customer requirement model; it builds the house of quality using quality function deployment and confirms the weight of technical feature of product and service; it explores the relevance rules between data using rough set theory, establishes rule database, and solves the target value of technical feature of product. Using economical turning center series numerical control machine as an example, it verifies the rationality of proposed customer requirement model.

[1]  Yi Han,et al.  Rough set-based approach for modeling relationship measures in product planning , 2012, Inf. Sci..

[2]  Bao Zhenqiang,et al.  Research on Capturing of Customer Requirements Based on Innovation Theory , 2012 .

[3]  Enrico Vezzetti,et al.  An integrated approach to support the Requirement Management (RM) tool customization for a collaborative scenario , 2017 .

[4]  Jian Jin,et al.  Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach , 2015, Eng. Appl. Artif. Intell..

[5]  Selim Zaim,et al.  Use of ANP weighted crisp and fuzzy QFD for product development , 2014, Expert Syst. Appl..

[6]  Yong Chen,et al.  A scenario-based approach for requirements management in engineering design , 2012, Concurr. Eng. Res. Appl..

[7]  Jiafu Tang,et al.  On integrating multiple type preferences into competitive analyses of customer requirements in product planning , 2012 .

[8]  Kwai-Sang Chin,et al.  A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment , 2011, Inf. Sci..

[9]  Jian Zhou,et al.  Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD , 2014, Neurocomputing.

[10]  Karthik Ramani,et al.  Ontology-based customer preference modeling for concept generation , 2011, Adv. Eng. Informatics.

[11]  Mitchell M. Tseng,et al.  Incorporating tolerances of customers’ requirements for customized products , 2014 .

[12]  Vineet R. Khare,et al.  A fuzzy logic based approach for modeling quality and reliability related customer satisfaction in the automotive domain , 2013, Expert Syst. Appl..

[13]  Yan Chen,et al.  Product Module Identification Based on Assured Customer Requirements , 2011 .

[14]  C. K. Kwong,et al.  A multi-objective genetic algorithm approach to rule mining for affective product design , 2012, Expert Syst. Appl..

[15]  Xinggang Luo,et al.  Determining the final priority ratings of customer requirements in product planning by MDBM and BSC , 2012, Expert Syst. Appl..

[16]  Niels Henrik Mortensen,et al.  Identification of a reusable requirements structure for embedded products in a dynamic market environment , 2013, Comput. Ind..

[17]  Mitchell M. Tseng,et al.  Integrating comprehensive customer requirements into product design , 2011 .

[18]  Yu-Jie Wang,et al.  A criteria weighting approach by combining fuzzy quality function deployment with relative preference relation , 2014, Appl. Soft Comput..

[19]  Enrico Vezzetti,et al.  A methodology for supporting requirement management tools (RMt) design in the PLM scenario: An user-based strategy , 2014, Comput. Ind..