An intelligent estimation method for product design time
The planning and control of product development is based on the pre-estimation of product design time (PDT). In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in sy...
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Veröffentlicht in: | International journal of advanced manufacturing technology 2006-10, Vol.30 (7-8), p.601-613 |
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description | The planning and control of product development is based on the pre-estimation of product design time (PDT). In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in systemic analytical methods for PDT this is somewhat lacking. This paper explores an intelligent method to evaluate the PDT regarding this problem. At the early development stage, designers lack sufficient product information and have difficulty in determining PDT via subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of “function→principle→structure” to extract product characteristics from customer demands. Then, a fuzzy neural network (FNN) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. In a word, the whole estimation method consists of four steps: time factors identification, product characteristics extraction by QFD and function mapping pattern, FNN learning, and PDT estimation. Finally, to illustrate the procedure of the estimation method, the case of injection mold design is studied. The results of experiments show that the intelligent estimation method is feasible and effective. This paper is developed to provide designers with PDT information to help them in optimizing PDP. |
doi_str_mv | 10.1007/s00170-005-0098-6 |
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In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in systemic analytical methods for PDT this is somewhat lacking. This paper explores an intelligent method to evaluate the PDT regarding this problem. At the early development stage, designers lack sufficient product information and have difficulty in determining PDT via subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of “function→principle→structure” to extract product characteristics from customer demands. Then, a fuzzy neural network (FNN) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. In a word, the whole estimation method consists of four steps: time factors identification, product characteristics extraction by QFD and function mapping pattern, FNN learning, and PDT estimation. Finally, to illustrate the procedure of the estimation method, the case of injection mold design is studied. The results of experiments show that the intelligent estimation method is feasible and effective. This paper is developed to provide designers with PDT information to help them in optimizing PDP.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-005-0098-6</identifier><language>eng</language><publisher>Heidelberg: Springer Nature B.V</publisher><subject>Artificial neural networks ; Design optimization ; Designers ; Fuzzy logic ; Injection molding ; Mapping ; Neural networks ; Product design ; Product development ; Product information ; Quality function deployment</subject><ispartof>International journal of advanced manufacturing technology, 2006-10, Vol.30 (7-8), p.601-613</ispartof><rights>The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2005). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c273t-d565c267121294fde35699d118be68ac03b4c222f613dd358bc0eac5fbffd1ce3</citedby><cites>FETCH-LOGICAL-c273t-d565c267121294fde35699d118be68ac03b4c222f613dd358bc0eac5fbffd1ce3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27907,27908</link.rule.ids></links><search><creatorcontrib>Xu, D.</creatorcontrib><creatorcontrib>Yan, H. -S.</creatorcontrib><title>An intelligent estimation method for product design time</title><title>International journal of advanced manufacturing technology</title><description>The planning and control of product development is based on the pre-estimation of product design time (PDT). In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in systemic analytical methods for PDT this is somewhat lacking. This paper explores an intelligent method to evaluate the PDT regarding this problem. At the early development stage, designers lack sufficient product information and have difficulty in determining PDT via subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of “function→principle→structure” to extract product characteristics from customer demands. Then, a fuzzy neural network (FNN) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. In a word, the whole estimation method consists of four steps: time factors identification, product characteristics extraction by QFD and function mapping pattern, FNN learning, and PDT estimation. Finally, to illustrate the procedure of the estimation method, the case of injection mold design is studied. The results of experiments show that the intelligent estimation method is feasible and effective. This paper is developed to provide designers with PDT information to help them in optimizing PDP.</description><subject>Artificial neural networks</subject><subject>Design optimization</subject><subject>Designers</subject><subject>Fuzzy logic</subject><subject>Injection molding</subject><subject>Mapping</subject><subject>Neural networks</subject><subject>Product design</subject><subject>Product development</subject><subject>Product information</subject><subject>Quality function deployment</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNotkLtOAzEQRS0EEkvgA-gsURtm7PVjyyjiJUWigdra9SNslOwG2yn4exyFYjTN0Z07h5B7hEcE0E8ZADUwAFmnM0xdkAZbIZgAlJekAa4ME1qZa3KT87bSCpVpiFlOdJxK2O3GTZgKDbmM-76M80T3oXzPnsY50UOa_dEV6kMeNxOtSLglV7Hf5XD3vxfk6-X5c_XG1h-v76vlmjmuRWFeKum40siRd230QUjVdR7RDEGZ3oEYWsc5jwqF90KawUHonYxDjB5dEAvycM6tHX6OtZ7dzsc01ZOWc8VbbYxuK4VnyqU55xSiPaT6R_q1CPYkyJ4F2SrIngRZJf4AHBdYbQ</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Xu, D.</creator><creator>Yan, H. -S.</creator><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>200610</creationdate><title>An intelligent estimation method for product design time</title><author>Xu, D. ; Yan, H. -S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c273t-d565c267121294fde35699d118be68ac03b4c222f613dd358bc0eac5fbffd1ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Artificial neural networks</topic><topic>Design optimization</topic><topic>Designers</topic><topic>Fuzzy logic</topic><topic>Injection molding</topic><topic>Mapping</topic><topic>Neural networks</topic><topic>Product design</topic><topic>Product development</topic><topic>Product information</topic><topic>Quality function deployment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, D.</creatorcontrib><creatorcontrib>Yan, H. -S.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, D.</au><au>Yan, H. -S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An intelligent estimation method for product design time</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><date>2006-10</date><risdate>2006</risdate><volume>30</volume><issue>7-8</issue><spage>601</spage><epage>613</epage><pages>601-613</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>The planning and control of product development is based on the pre-estimation of product design time (PDT). In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in systemic analytical methods for PDT this is somewhat lacking. This paper explores an intelligent method to evaluate the PDT regarding this problem. At the early development stage, designers lack sufficient product information and have difficulty in determining PDT via subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of “function→principle→structure” to extract product characteristics from customer demands. Then, a fuzzy neural network (FNN) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. 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subjects | Artificial neural networks Design optimization Designers Fuzzy logic Injection molding Mapping Neural networks Product design Product development Product information Quality function deployment |
title | An intelligent estimation method for product design time |
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