复杂机电产品设计质量若干关键技术研究
|
摘要
“设计是产品质量的源头”,这一观点已被越来越多的专家和制造企业所认同,企业对产品设计质量越来越重视,同时投入大量的资源努力提高产品设计质量。本论文以数控机床为例,对复杂机电产品设计质量展开研究,针对设计质量特性的多尺度性、多领域性、多学科性、演化性和耦合性等特点,将关键设计质量特性(KDQCs)的概念引入到产品设计过程中,研究了关键设计质量特性的定义、关键质量特性的提取、基于反向映射的机床精度设计、基于优化决策模型的可靠性设计、设计质量的综合评价等技术。本论文在国家自然科学基金重点项目“复杂机电产品质量特性多尺度耦合理论与预防性控制技术”(项目批准号50835008 )、国家“高档数控机床与基础制造装备”科技重大专项(2009ZX04014-016)及数字制造装备与技术国家重点实验室(华中科技大学)开放基金的资助下,进行了复杂机电产品设计质量相关理论研究,提出提高复杂机电产品设计质量的解决方案,具体内容包括以下几个方面:
(1)首先,讨论了产品设计质量特性基础理论。根据对已有文献的研究,分析了设计质量特性和关键设计质量特性的基本概念,论述了设计质量的发展历程及国内外的研究现状,说明了本论文的课题来源、主要研究内容及总体结构。然后,研究了产品关键设计质量特性的基础理论,分析了产品关键设计质量特性的形成机理,得出了产品关键设计质量特性的演变规律,并分类研究了关键设计质量特性对产品质量的影响,结合设计质量控制的技术与方法,提出了基于关键质量特性的产品设计质量控制策略。
(2)研究了复杂机电产品关键设计质量特性提取与排序。综合分析了关键设计质量特性在不同子系统、不同层次、不同功能的多元性,建立了复杂机电产品多元质量特性的功能分解树。针对关键设计质量特性提取的复杂性和不确定性,综合运用模糊理论和FAHP方法建立关键设计质量特性的提取模型,用三角模糊数表示模糊比较判断的方法计算主观权重,并使用信息熵(IE)确定质量特性指标层的客观权重。然后应用模糊Borda方法组合FAHP法和信息熵法进行综合提取,根据模糊Borda数值大小最终决定多元设计质量特性的重要度,从中提取关键设计质量特性。该综合方法可实现程序化的求解,减少人工干预并提高了提取结果的准确性。最后用数控机床实例验证了该综合提取方法的合理性和可行性。
(3)研究了机电产品精度这一关键设计质量特性的反向映射技术。针对数控机床设计过程的特殊性和复杂性,在考虑传统的质量功能配置(QFD)多源、多阶段复杂处理过程的基础上,本论文增加了产品运行阶段的反向映射,通过识别影响加工质量的关键要素,并结合用户需求,提出基于反向映射的设计关键质量特性的修正方法,综合映射成数控机床的精度设计质量特性。该方法使得产品设计、制造和使用过程的质量控制形成一个闭环系统,使产品质量信息能够及时、全面和准确地反映到产品各阶段,提高产品设计质量和用户满意度。其次,通过对加工质量要求(QRs)和关键设计质量特性的转换关系进行研究,提出了基于粗糙集(RS)和质量功能配置中质量屋(HoQ)的反向映射模型。接着,运用粗糙集理论简化和提取满足加工质量要求的要素,并提出了应用近似精确粗糙数确定加工质量要求的权重。然后,通过构建关键设计质量特性的反向映射模型,实现主要加工质量要求和关键设计质量特性之间的转换,帮助产品设计人员完成设计目标。最后,以卧式加工中心为例,验证了该模型的实用性和有效性。
(4)研究了产品可靠性这一关键设计质量特性的优化决策技术。通过分析机电产品设计方案中多元设计质量特性的特定要求,给出了可靠性的优化方案决策流程,建立了可靠性的多属性指标体系,保证产品设计质量符合预定设计质量特性要求。提出了基于改进“逼近理想值的排序方法”(TOPSIS)和ELECTRE I的综合方法进行产品设计方案的决策。建立了产品可靠性这一关键设计质量特性最优设计方案的多目标决策模型,应用改进的TOPSIS方法计算各设计质量特性的决策指标的重要性,通过综合应用ELECTRE I方法准确地确定产品可靠性设计方案的优劣程度。最后,以高档数控机床的可靠性设计方案为例,说明该方法的有效性和可行性。
(5)研究了机电产品设计质量的模糊物元综合评价技术。分析了产品设计质量评价的应用现状,给出了设计质量评价的原则及多元质量特性评价指标体系模型。针对产品质量多层次指标评价问题,提出了一种基于模糊物元(FME)的综合评价方法。该方法利用模糊物元理论分析多元质量特性及其属性特征,并建立了产品设计质量综合评价模型。针对传统决策法中权值确定的主客观评价的片面性,采用网络层次分析法(ANP)和IE技术方法综合确定评价指标权重,建立基于最小二乘法的优化组合权重模型计算组合权重,并运用模糊物元法给出评价结果。最后以数控机床的设计质量评价为例,讨论了该方法的可行性和合理性。
Design quality is a key source of product quality, and this view has been shared now by more and more experts and manufacturing enterprises. Recently, more and more enterprises pay attention to the product design quality, set the best resource configuration and give the effective way to improve product quality. In view of the Quality Characteristics (QCs) of multi-scale, multi-field, multidisciplinary, evolvability and coupling, the theory of Key Design Quality Characteristics (KDQCs) is applied to the design process of computer numerical control (CNC) machine, then, the techniques and methods of extracting, mapping, optimizing and evaluating of KDQCs are proposed in this study. This paper is supported by“Complex mechanical and electrical products quality characteristic's multi-scale coupling and preventive control”(National Natural Science Foundation, No. 50835008), the National Major Scientific and Technological Special Project for“High-grade CNC and Basic Manufacturing Equipment”(No. 2009ZX04014-016), and supported by Open Research Foundation of State Key Lab. of Digital Manufacturing Equipment & Technology in Huazhong University of Science & Technology. By the theories on KDQCs are studied under the support of these grants, we put forward the solutions for improving the design quality of mechanical and electrical products. Topics covered in this dissertation are as follows:
(1) Firstly, this paper introduced the basic theory of key design quality characteristics (QCs) of mechanical and electrical products. Secondly, the key concepts, development and technology of key QCs of mechanical and electrical products are analyzed based on the existing literature. Then, the source, the main works and frame of this treatise are illustrated here. Furthermore, the basic theory of key design QCs for mechanical and electrical products is studied.Finally, based on the research on theory about the formation and its effect on quality of key design QCs, the strategy of quality control of key design QCs are proposed.
(2) This paper introduced the multi-scale DQCs of mechanical and electrical products in different subsystem, levels and function, and built the multi-scale QCs model of mechanical and electrical products. Aiming at the complexity of ranking multiple QCs of mechanical and electrical products, the mathematical method of Fuzzy Analytical Hierarchy Process (FAHP) and Information Entropy (IE) is presented, which can effectively solve the uncertain problems such as subjectivity and ambiguity existed in the computational process of some conventional methods. Fuzzy theory and FAHP are introduced to establish the ordering model of multiple QCs. Then, the subjective weights are determined by the method of fuzzy comparison which is expressed by the triangular fuzzy number, while the objective weights of the QCs are obtained by Information Entropy. In addition, the importance of multiple QCs is sequenced by Fuzzy Borda method combining with FAHP and Information Entropy, and finally determined according to the value of fuzzy Borda. The importance sequencing problem is effectively solved by this method based on considering the subjective and the objective together. Finally, a practical example is illustrated to show the rationality and feasibility of the integrated method.
(3) Based on the particularity and complexity in the design process of mechanical and electrical products, integrated with the multi-source and multi-stage process of Quality Function Deployment (QFD), this paper increased mapping machining process to identify the elements of machining quality. Then, the user needs and quality requirements (QRs) are mapped into the QCs of mechanical and electrical products, and the product design, manufacturing and using stages are applied to form a closed loop system, so that product quality information is timely, roundly and accurately reflected in the product stages, so as to improve design quality and customer satisfaction. In order to improve the machining accuracy, the accuracy design QCs for mechanical and electrical products should be analyzed and evaluated. This paper studies the transfer relationships between QRs and accuracy design QCs, and proposes a reverse mapping model combines rough set (RS) with house of quality (HoQ) in QFD. RS theory is first applied to simplify and extract the QRs of the machining quality requirements. Then, a novel concept known as approximate accuracy rough number is proposed to determine the final importance weights of the QRs. Furthermore, a reverse mapping model is designed to translate the main QRs into accuracy design QCs, which would help product designers achieve the design goals in product development. The applicability of the proposed reverse mapping model is demonstrated by an illustrative example of CNC machine.
(4) By analyzing the specific requirements of multiple DQCs of design scheme for mechanical and electrical products, the design optimization program decision-making process are proposed to ensure the design quality based on multi-attribute index system. In this paper, we propose a novel method combined the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with Elimination and Choice Translating Reality (ELECTRE) I for reliability design scheme decision of mechanical and electrical products. Based on the improved TOPSIS method and ELECTRE I, the decision model is built to select the optimal design scheme. The improved TOPSIS method is applied to determinate the weights of reliability design factors through the decision model. ELECTRE I method is then designed to rank reliability design scheme in order of decision maker’s preference. To evaluate performance of the developed algorithm, an illustrative example of CNC machine is given. The computational results show that the proposed approach is reliable and performs well.
(5) The paper analyzed the present application and proposed the evaluation index system model for multivariate QCs of mechanical and electrical products. A comprehensive evaluation method based on fuzzy matter element (FME) analysis is proposed to address problems of multiple performance quality evaluation for mechanical and electrical products. The FME theory is utilized to analyze multi-scale QCs and attributes. Then, the comprehensive quality evaluation model for mechanical and electrical products is founded by using FME analysis method. Analytical network process (ANP) and IE are applied to obtain the objective index weights. Furthermore, the combinational weight is calculated with the optimal mathematics model based on the least square method. Finally, the FME method is used to determine the assessment result. The case study indicated that the method had a certain rationality and feasibility.
(1)首先,讨论了产品设计质量特性基础理论。根据对已有文献的研究,分析了设计质量特性和关键设计质量特性的基本概念,论述了设计质量的发展历程及国内外的研究现状,说明了本论文的课题来源、主要研究内容及总体结构。然后,研究了产品关键设计质量特性的基础理论,分析了产品关键设计质量特性的形成机理,得出了产品关键设计质量特性的演变规律,并分类研究了关键设计质量特性对产品质量的影响,结合设计质量控制的技术与方法,提出了基于关键质量特性的产品设计质量控制策略。
(2)研究了复杂机电产品关键设计质量特性提取与排序。综合分析了关键设计质量特性在不同子系统、不同层次、不同功能的多元性,建立了复杂机电产品多元质量特性的功能分解树。针对关键设计质量特性提取的复杂性和不确定性,综合运用模糊理论和FAHP方法建立关键设计质量特性的提取模型,用三角模糊数表示模糊比较判断的方法计算主观权重,并使用信息熵(IE)确定质量特性指标层的客观权重。然后应用模糊Borda方法组合FAHP法和信息熵法进行综合提取,根据模糊Borda数值大小最终决定多元设计质量特性的重要度,从中提取关键设计质量特性。该综合方法可实现程序化的求解,减少人工干预并提高了提取结果的准确性。最后用数控机床实例验证了该综合提取方法的合理性和可行性。
(3)研究了机电产品精度这一关键设计质量特性的反向映射技术。针对数控机床设计过程的特殊性和复杂性,在考虑传统的质量功能配置(QFD)多源、多阶段复杂处理过程的基础上,本论文增加了产品运行阶段的反向映射,通过识别影响加工质量的关键要素,并结合用户需求,提出基于反向映射的设计关键质量特性的修正方法,综合映射成数控机床的精度设计质量特性。该方法使得产品设计、制造和使用过程的质量控制形成一个闭环系统,使产品质量信息能够及时、全面和准确地反映到产品各阶段,提高产品设计质量和用户满意度。其次,通过对加工质量要求(QRs)和关键设计质量特性的转换关系进行研究,提出了基于粗糙集(RS)和质量功能配置中质量屋(HoQ)的反向映射模型。接着,运用粗糙集理论简化和提取满足加工质量要求的要素,并提出了应用近似精确粗糙数确定加工质量要求的权重。然后,通过构建关键设计质量特性的反向映射模型,实现主要加工质量要求和关键设计质量特性之间的转换,帮助产品设计人员完成设计目标。最后,以卧式加工中心为例,验证了该模型的实用性和有效性。
(4)研究了产品可靠性这一关键设计质量特性的优化决策技术。通过分析机电产品设计方案中多元设计质量特性的特定要求,给出了可靠性的优化方案决策流程,建立了可靠性的多属性指标体系,保证产品设计质量符合预定设计质量特性要求。提出了基于改进“逼近理想值的排序方法”(TOPSIS)和ELECTRE I的综合方法进行产品设计方案的决策。建立了产品可靠性这一关键设计质量特性最优设计方案的多目标决策模型,应用改进的TOPSIS方法计算各设计质量特性的决策指标的重要性,通过综合应用ELECTRE I方法准确地确定产品可靠性设计方案的优劣程度。最后,以高档数控机床的可靠性设计方案为例,说明该方法的有效性和可行性。
(5)研究了机电产品设计质量的模糊物元综合评价技术。分析了产品设计质量评价的应用现状,给出了设计质量评价的原则及多元质量特性评价指标体系模型。针对产品质量多层次指标评价问题,提出了一种基于模糊物元(FME)的综合评价方法。该方法利用模糊物元理论分析多元质量特性及其属性特征,并建立了产品设计质量综合评价模型。针对传统决策法中权值确定的主客观评价的片面性,采用网络层次分析法(ANP)和IE技术方法综合确定评价指标权重,建立基于最小二乘法的优化组合权重模型计算组合权重,并运用模糊物元法给出评价结果。最后以数控机床的设计质量评价为例,讨论了该方法的可行性和合理性。
Design quality is a key source of product quality, and this view has been shared now by more and more experts and manufacturing enterprises. Recently, more and more enterprises pay attention to the product design quality, set the best resource configuration and give the effective way to improve product quality. In view of the Quality Characteristics (QCs) of multi-scale, multi-field, multidisciplinary, evolvability and coupling, the theory of Key Design Quality Characteristics (KDQCs) is applied to the design process of computer numerical control (CNC) machine, then, the techniques and methods of extracting, mapping, optimizing and evaluating of KDQCs are proposed in this study. This paper is supported by“Complex mechanical and electrical products quality characteristic's multi-scale coupling and preventive control”(National Natural Science Foundation, No. 50835008), the National Major Scientific and Technological Special Project for“High-grade CNC and Basic Manufacturing Equipment”(No. 2009ZX04014-016), and supported by Open Research Foundation of State Key Lab. of Digital Manufacturing Equipment & Technology in Huazhong University of Science & Technology. By the theories on KDQCs are studied under the support of these grants, we put forward the solutions for improving the design quality of mechanical and electrical products. Topics covered in this dissertation are as follows:
(1) Firstly, this paper introduced the basic theory of key design quality characteristics (QCs) of mechanical and electrical products. Secondly, the key concepts, development and technology of key QCs of mechanical and electrical products are analyzed based on the existing literature. Then, the source, the main works and frame of this treatise are illustrated here. Furthermore, the basic theory of key design QCs for mechanical and electrical products is studied.Finally, based on the research on theory about the formation and its effect on quality of key design QCs, the strategy of quality control of key design QCs are proposed.
(2) This paper introduced the multi-scale DQCs of mechanical and electrical products in different subsystem, levels and function, and built the multi-scale QCs model of mechanical and electrical products. Aiming at the complexity of ranking multiple QCs of mechanical and electrical products, the mathematical method of Fuzzy Analytical Hierarchy Process (FAHP) and Information Entropy (IE) is presented, which can effectively solve the uncertain problems such as subjectivity and ambiguity existed in the computational process of some conventional methods. Fuzzy theory and FAHP are introduced to establish the ordering model of multiple QCs. Then, the subjective weights are determined by the method of fuzzy comparison which is expressed by the triangular fuzzy number, while the objective weights of the QCs are obtained by Information Entropy. In addition, the importance of multiple QCs is sequenced by Fuzzy Borda method combining with FAHP and Information Entropy, and finally determined according to the value of fuzzy Borda. The importance sequencing problem is effectively solved by this method based on considering the subjective and the objective together. Finally, a practical example is illustrated to show the rationality and feasibility of the integrated method.
(3) Based on the particularity and complexity in the design process of mechanical and electrical products, integrated with the multi-source and multi-stage process of Quality Function Deployment (QFD), this paper increased mapping machining process to identify the elements of machining quality. Then, the user needs and quality requirements (QRs) are mapped into the QCs of mechanical and electrical products, and the product design, manufacturing and using stages are applied to form a closed loop system, so that product quality information is timely, roundly and accurately reflected in the product stages, so as to improve design quality and customer satisfaction. In order to improve the machining accuracy, the accuracy design QCs for mechanical and electrical products should be analyzed and evaluated. This paper studies the transfer relationships between QRs and accuracy design QCs, and proposes a reverse mapping model combines rough set (RS) with house of quality (HoQ) in QFD. RS theory is first applied to simplify and extract the QRs of the machining quality requirements. Then, a novel concept known as approximate accuracy rough number is proposed to determine the final importance weights of the QRs. Furthermore, a reverse mapping model is designed to translate the main QRs into accuracy design QCs, which would help product designers achieve the design goals in product development. The applicability of the proposed reverse mapping model is demonstrated by an illustrative example of CNC machine.
(4) By analyzing the specific requirements of multiple DQCs of design scheme for mechanical and electrical products, the design optimization program decision-making process are proposed to ensure the design quality based on multi-attribute index system. In this paper, we propose a novel method combined the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with Elimination and Choice Translating Reality (ELECTRE) I for reliability design scheme decision of mechanical and electrical products. Based on the improved TOPSIS method and ELECTRE I, the decision model is built to select the optimal design scheme. The improved TOPSIS method is applied to determinate the weights of reliability design factors through the decision model. ELECTRE I method is then designed to rank reliability design scheme in order of decision maker’s preference. To evaluate performance of the developed algorithm, an illustrative example of CNC machine is given. The computational results show that the proposed approach is reliable and performs well.
(5) The paper analyzed the present application and proposed the evaluation index system model for multivariate QCs of mechanical and electrical products. A comprehensive evaluation method based on fuzzy matter element (FME) analysis is proposed to address problems of multiple performance quality evaluation for mechanical and electrical products. The FME theory is utilized to analyze multi-scale QCs and attributes. Then, the comprehensive quality evaluation model for mechanical and electrical products is founded by using FME analysis method. Analytical network process (ANP) and IE are applied to obtain the objective index weights. Furthermore, the combinational weight is calculated with the optimal mathematics model based on the least square method. Finally, the FME method is used to determine the assessment result. The case study indicated that the method had a certain rationality and feasibility.
引文
[1]孙来燕.国防科工委领导在首届中国航天质量论坛上的讲话[J].质量与可靠性, 2006, (5):3.
[2]张根保.现代质量工程[M].北京:机械工业出版社, 2004.
[3]张公绪,孙静.新编质量管理学(第二版) [M].北京:高等教育出版社, 2003.
[4]林志航.产品设计与制造质量工程[M].北京:机械工业出版社, 2005.
[5] P. Lung-Kwang, W. Che-Chung, S. L. Wei, et al. Optimizing multiple quality characteristics via Taguchi method-based Grey analysis[J]. Journal of Materials Processing Technology. 2007, 182(1-3): 107-116.
[6] A. G.David. What Does‘Product Quality’Mean? [J]. Sloan Management Review,Fall 1984,26(1): 25–43.
[7] JB/T5058-2006,中华人民共和国机械行业标准?机械工业产品质量特性重要度分级导则[S].北京:机械工业出版社, 2006:1-3..
[8] Y.H. He, W. Chang. Systematical design quality control model based on key quality characteristics [C]. 2009 IEEE 16th International Conference on Industrial Engineering and Engineering Management, Beijing, China, 2009, 1208-1212.
[9]何益海,常文兵.基于关键质量特性的产品设计质量保证方法研究[C].第7届国际可靠性、维修性、安全性学术会议, 2007.
[10]孙伟,刘杰,李小彭,等.机械产品设计质量评价及评价系统的开发[J].机械设计, 2007, 24(6):5-7.
[11]荆洪英,张利,闻邦椿.基于层次分析法的产品设计质量权重分配[J].东北大学学报(自然科学版), 2009, 5(30):712-715.
[12] Y. Reich, E. Levy. Managing product design quality under resource constraints[J]. International Journal of Production Research. 2004, (42): 2555-2572.
[13] M. L. Swink, R. Calantone. Design-manufacturing integration as a mediator of antecedents to new product design quality[J]. IEEE Transactions on Engineering Management. 2004, (51): 472-482.
[14] S. K. Mukhopadhyay, R. Setaputra. A dynamic model for optimal design quality and return policies[J]. European Journal of Operational Research. 2007, (180): 1144-1154.
[15] J. Freiesleben. Proposing a new approach to discussing economic effects of design quality[J]. International Journal of Production Economics. 2010, (124): 348-359.
[16]丁莲,吴锡英,汤文成.产品设计质量评价指标体系的研究[J].机械工业自动化, 1997, 19(4):5-8.
[17]袁清珂,王宏,赵汝嘉.产品设计质量综合评价的理论研究[J].组合机床与自动化加工技术, 1998, (5):35-38.
[18]商建东,陈康宁.质量驱动的产品设计质量模糊评价及方案决策方法研究[J].中国机械工程, 2000, 11(12):1394-1398.
[19]王洪,唐晓青.面向现代产品设计的设计质量评估方法[J].中国质量. 2001, (12): 9-13.
[20]杨明顺,李言,林志航.产品总体设计方案评价与优选的组合决策[J].计算机集成制造系统, 2006, 4(10): 540-545.
[21]孙伟,李小彭,李朝峰,等.面向机械产品设计的多层次规划研究[J].计算机集成制造系统, 2009, 15(3) : 431-439.
[22] D. M. Osborne, R. L. Armacost. Review of techniques for optimizing multiple quality characteristics in product development[J]. Computers and Industrial Engineering. 1996, 31(1-2): 107-110.
[23] J. H. Byun, K. J. Kim. On robust design for multiple quality characteristics[J]. Quality Improvement through Statistical Methods. 1998: 289-297.
[24] I. G. Ali, Y. T. Chen. Design quality and robustness with neural networks[J]. IEEE Trans Neural Netw. 1999, 10(6): 1518-1527.
[25] W. Ful-Chiang, C. Chiuh-Cheng. Optimization of robust design for multiple quality characteristics[J]. International Journal of Production Research. 2004, 42(2): 337-354.
[26]胡金龙.试探“质量特性分级”的应用[J].企业标准化, 1994, (1):40-42.
[27]王宗君.机械零件最佳选定质量特性重要度是实行质量重点控制的重要举措[J].机械工业标准化与质量, 1999, (1):28-29.
[28]刘宏.产品质量特性重要度分级在质量链中的重要作用[J].电子质量, 2007, (1):45-47.
[29]王颖.基于TOPSIS法的多元质量特性优化方法研究[D].博士学位论文,天津大学, 2007.
[30]张根保,庞继红,任显林,等.机械产品多元质量特性重要度排序方法研究[J].计算机集成制造系统, 2011, 17(1):151-158.
[31] R. Krishnapillai, A. Zeid. Mapping product design specification for mass customization[J]. Journal of Intelligent Manufacturing. 2006, (17): 29-43.
[32] Chu Xue-Zheng, Gao Liang, Qiu Hao-Bo, et al., An expert system using rough sets theory and self-organizing maps to design space exploration of complex products[J]. Expert Systems with Applications. 2010, 37(11):7364-7372.
[33]许多,严洪森.基于模糊度量质量屋的产品特征映射及分析[J].计算机集成制造系统, 2004, 10(6):693-698.
[34]车君华,谭建荣,冯毅雄,等.产品配置设计的多层次映射求解模型研究[J].中国机械工程, 2006, 17(8): 849-853.
[35]崔剑,祁国宁,纪杨建等.基于需求流动链的映射机理[J].机械工程学报, 2008, 7(44): 93-100.
[36]张根保,纪富义,任显林,等.复杂机电产品关键质量特性提取模型[J].重庆大学学报(自然科学版), 2010, 33(2):8-14.
[37]邓军,余忠华,吴昭同.产品质量与过程质量的映射研究[J].中国机械工程, 2010, (17):58-62.
[38]雷鸣,朱祖平,姚立纲.质量功能展开中顾客需求自动映射[J].计算机集成制造系统, 2009, 15(3):?592-597.
[39] K. C. Kapur, B. R. Cho. Economic design of the specification region for multiple quality characteristics[J]. Iie Transactions. 1996, 28(3): 237-248.
[40] D. C. Kim, D. L. Jones. Multiple parameter optimization incorporating mixed quality characteristics[J]. Computer Aided Optimum Design of Structures Vii. 2001, (10): 309-326.
[41] J. Teeravaraprug, B. R. Cho. Designing the optimal process target levels for multiple quality characteristics[J]. International Journal of Production Research. 2002, (40): 37-54.
[42] H. Yi-hai, T. Xiao-qing, C. Wen-bing. Technical Decomposition Approach of Critical to Quality Characteristics for Product Design for Six Sigma[J]. Quality and Reliability Engineering International. 2010: 325-339.
[43] G. F. Zhai, H. M. Liang, F. Xu, et al. New method of tolerance design of electromagnetic relay reliability[J]. Journal of Engineering Design. 2004, 15(5): 425-431.
[44]余忠华.保质设计中的质量评价研究[J].计算机集成制造系统, 2001, 7(6):65-67.
[45]邓军,余忠华,吴昭同.基于信息熵的概念设计的质量评价[J].浙江大学学报(工学版), 2009, 43(8):1480-1483.
[46]钟晓芳,韩之俊.利用主成分分析对多质量特性的优化设计[J].南京理工大学学报:自然科学版, 2003, 27(3): 301-304.
[47]王洪,唐晓青.面向质量设计的质量评估集成模型[J].计算机集成制造系统, 2002, 8 (2):122-125.
[48]何桢,吕海利,多元质量特性稳健性设计方法的优化研究[J].管理科学, 2007, 20(1): 2-7.
[49]魏喆,谭建荣,冯毅雄.广义性能驱动的机械产品方案设计方法[J].机械工程学报, 2008, 44(5):?1-10.
[50] J. Wu, C. Deng, X. Y. Shao, et al. A reliability assessment method based on support vector machines for CNC equipment[J]. Science In China Series E-technological Sciences. 2009, 52(7): 1849-1857.
[51] G. K. Adil. Concurrent consideration of product design, process planning and production planning activities[J]. Production Planning and Control. 1998, (9): 167-175.
[52] Y. Han, H. Zhu. Bayesian mean vector control chart for multiple quality characteristics[J]. Journal of Nanjing University of Science and Technology. 2003, 27(5): 561-566.
[53] F.C. Wu, C.H. Yeh. Robust design of multiple dynamic quality characteristics[J]. International Journal of Advanced Manufacturing Technology. 2005, (25): 579-588.
[54] [美]约瑟夫·M·朱兰主编,焦叔斌等译. (Juran’s Quality Handbook)《朱兰质量手册》(第一版),北京:中国人民大学出版社, 2003.
[55]何益海,唐晓青,王美清.产品设计质量数据与管理模型研究[J].计算机集成制造系统, 2006, 12(8):1161-1166
[56] O. Feyzioglu, G. Buyukozkan. An integrated group decision-making approach for new product development[J]. International Journal of Computer Integrated Manufacturing. 2008, (21): 366-375.
[57]肖人彬,蔡政英.不确定质量水平下闭环质量链的成本模糊控制[J].计算机集成制造系统, 2009, 15(6):?1207-1214.
[58]金国强,刘恒江.质量链管理理论研究综述[J].世界标准化与质量管理, 2006, 3(3):21-24.
[59]肖人彬,蔡政英.应用关联规则构建质量活动链模型[J].北京航空航天大学学报, 2004, 30(5):?448-451.
[60]龚益鸣.质量管理学[M].上海:复旦大学出版社, 2004.
[61]张根保,何桢,刘英.质量管理与可靠性[M].北京:中国科学技术出版社, 2005.
[62] [美]克劳斯比,零缺点的质量管理[M],北京:中信出版社, 2000.
[63] F.C. Wu, C.C. Chyu. Optimization of correlated multiple quality characteristics robust design using principal component analysis[J]. Journal of Manufacturing Systems, 2004, 23 (2) :134-143.
[64]戴伟,唐晓青.基于质量特性的产品质量策划模型及应用[J].计算机集成制造系统, 2009, 15(10):1938-1945.
[65]何桢,赵有,马彦辉.模糊QFD中技术特性重要度排序方法[J].天津大学学报, 2008, 41(5): 631-634.
[66]何益海,唐晓青.基于关键质量特性的产品保质设计[J].航空学报, 2007, 28 (6):1468-1481.
[67]郭惠昕,任丕顺,张国平.多质量指标的解耦与稳健优化设计[J].农业机械学报, 2009, 40(1): 203-207.
[68]马义中,赵逢禹.多元质量特性的稳健设计及其实现[J].系统工程与电子技术, 2005, 27(9):1580-1582.
[69] S.M. Govindaluri, B.R. Cho. Robust design modeling with correlated quality characteristics using a multicriteria decision framework[J]. International Journal of Advanced Manufacturing Technology, 2007, 32(5) :423-433.
[70] E.E. Karsak, S. Sozer, S.E. Alptekin. Product planning in quality function development using a combined analytic network process and goal programming approach[J]. Computer & Industrial Engineering, 2002, (44):171-190.
[71] Yang Chingchow, Chen Baisheng. Key quality performance evaluation using fuzzy AHP[J]. Journal of the Chinese Institute of Industrial Engineers. 2004, 21(6):543-550.
[72] F.C. Wu. Robust design of nonlinear multiple dynamic quality characteristics[J]. Computers and Industrial Engineering. 2009, (56): 1328-1332.
[73] Jia Yue, Song Baowei, Zhao Xiangtao, et al. Method of combat programs optimization by fuzzy AHP based on entropy weight[J]. Journal of System Simulation. 2008, 20(11):2965-2968.
[74]朱松岭,周平,韩毅.基于模糊层次分析法的风险量化研究[J].计算机集成制造系统, 2004, 10(8):980-984.
[75]王美乂,张凤鸣,刘智.模糊信息的熵权多属性决策方案评估方法[J].系统工程与电子技术, 2006, 28(10):1523-1525.
[76]李柏年.模糊数学及其应用[M].合肥:合肥工业大学出版社, 2007.
[77] E. Levner, D. Alcaide. SICILIA J. Multi-attribute text classification using the fuzzy Borda method and semantic grades[C]// Conference of the 7th International Workshop on Fuzzy Logic and Applications, Berlin, Germany:Springer, 2007:422-429.
[78] T. L. Saaty. The Analytic Hierarchy Process[J]. New York:McGraw Hill. 1980.
[79] S. Parthasarathy, N. Anbazhagan. Evaluating ERP implementation choices using AHP[J]. International Journal of Enterprise Information Systems. 2007: 52-65.
[80] T. Soota, H. Singh, R. Mishra. Defining characteristics for product development using quality function deployment: a case study on Indian bikes[J]. Quality Engineering. 2008: 195-208.
[81] S. F. Ding, Z. Z. Shi. Studies on incidence pattern recognition based on information entropy[J]. Journal of Information Science. 2005, 31(6): 497-502.
[82] C. M. Sarris, A. N. Proto. Information entropy and nonlinear semiquantum dynamics[J]. International Journal of Bifurcation and Chaos. 2009, 19(10): 3473-3484.
[83] L. S. Zhou, B. Ding. Using Information Entropy to Analyze Dynamic Reserves of Emergency Material[C]. International Joint Conference on Computational Sciences and Optimization. 2009, (12): 609-612.
[84] S. Naimuddin, S.B. Warkad, G.M. Dhole, et al. Enhancing the operational availability of CNC machine using fuzzy logic controller[J]. Journal of Theoretical and Applied Information Technology. 2009, 6(2) : 145-154.
[85] H. Raharjo, A.C. Brombacher, T.N. Goh, et al. On integrating kano's model dynamics into QFD for multiple product design[J]. Quality and Reliability Engineering International. 2010, 26(4): 351-363.
[86] J.R. Sharma, A.M. Rawani. Ranking customer requirements in QFD by factoring in their interrelationship values[J]. Quality Management Journal. 2007,14 (4):53-60.
[87] F. Jariri, S.H. Zegordi. Quality function deployment planning for platform design[J]. International Journal of Advanced Manufacturing Technology. 2008, 36(5-6): 419-430.
[88] K. Delice e, Z. Gungor. A new mixed integer linear programming model for product development using quality function deployment[J]. Computers & Industrial Engineering. 2009, 57(3): 906-912.
[89]王美清,唐晓青.产品设计中的用户需求与产品质量特征映射方法研究[J].机械工程学报, 2004, 40(5): 136-140.
[90]杨明顺,李言,林志航.顾客需求向技术特征映射的产品设计规划求解[J].计算机集成制造系统, 2006, 16(6): 853-856.
[91]李延来,姚建明,焦明海.基于BSC的质量屋中工程特性最终重要度确定方法[J].机械工程学报, 2010, 46(4): 177-185.
[92] M. Celik, S. Cebi, C. Kahraman, et al. An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market[J]. Expert Systems With Applications. 2009, 36(3): 6227-6235.
[93] T. Sakao. A QFD-centred design methodology for environmentally conscious product design[J]. International Journal of Production Research. 2007, 45(18-19): 4143-4162.
[94] T. Matulja, N. Fafandjel, M. Hadjina. Small craft basic characteristics determination applying QFD technique[J]. Engineering Review. 2008, 28(2): 11-21.
[95] Z. Pawlak. Rough sets[J]. International Journal of Computer and Information Science. 1982, 11(2): 341-356.
[96] M. J. Beynon, M.J. Peel. Variable precision rough set theory and data discretisation: an application to corporate failure prediction[J]. Omega-international Journal of Management Science. 2001, 29(6): 561-576.
[97] Y. Hassan, E.Tazaki. Induction of knowledge using evolutionary rough set theory[J]. Cybernetics and Systems. 2003, 34(8): 617-643.
[98] T. L. Tseng, Y. Kwon, Y.M. Ertekin. Feature-based rule induction in machining operation using rough set theory for quality assurance[J]. Robotics and Computer-integrated Manufacturing. 2005, 21(6): 559-567.
[99] D.P. Soetanto, M. Van-Geenhuizen. Technology incubators and knowledge networks: a rough set approach in comparative project analysis[J]. Environment and Planning B-Planning & Design. 2007, 34(6): 1011-1029.
[100]郑联语,唐晓青,汪叔淳.基于QFD的数控加工工艺质量优化规划方法[J].机械工程学报, 2001,37(8): 38-42.
[101] L. Jinghong, Z. P. Mourelatos, T. Jian. A single-loop method for reliability-based design optimisation[J]. International Journal of Product Development. 2008, 5(1): 76-92.
[102] Mohsine, G. Kharmanda, A. El-Hami. Improved hybrid method as a robust tool for reliability-based design optimization[J]. Structural and Multidisciplinary Optimization. 2006, 32(3): 203-213.
[103] H. Shimizu, Y. Otsuka, H. Noguchi. Design review based on failure mode to visualise reliability problems in the development stage of mechanical products[J]. International Journal of Vehicle Design. 2010, 53(3): 149-165.
[104] Z. Li, Q. Gao, D. Zhang. Product design on the basis of fuzzy quality function deployment[J]. Journal of Systems Engineering and Electronics. 2008, 19(6): 1165-1170.
[105] R. Byung, M. D. Phillips, J. Kovach. Designing the optimum configurations of circular and spherical product specifications for multiple quality characteristics[J]. Journal of Systems Science and Systems Engineering. 2005, 14(4): 385-399.
[106] H. Raharjo, A. C. Brombacher, M. Xie. Dealing with subjectivity in early product design phase: A systematic approach to exploit Quality Function Deployment potentials[J]. Computers & Industrial Engineering. 2008, 55(1): 253-278.
[107] K. Zhou, S. Feng. Evaluating the conceptual design schemes of complex products based on FAHP using fuzzy number[J]. Journal of Systems Engineering and Electronics. 2005, 16(3): 574-578.
[108] X. F. Zha, R. D. Sriram, W. F. Lu. Evaluation and selection in product design for mass customization: A knowledge decision support approach[J]. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM. 2004, (18): 87-109.
[109] Y. Cui, F. Sun. Fuzzy design scheme selection model based on quality function deployment[J]. Computer Integrated Manufacturing Systems. 2006, 12(2): 220-224.
[110] F. Shi, Z. L. Lou, J. G. Lu, et al. An improved rough set approach to design of gating scheme for injection moulding[J]. International Journal of Advanced Manufacturing Technology.2003, (21): 662-668.
[111] F. I. Khan, R. Sadiq, B. Veitch. Life cycle iNdeX (LInX): A new indexing procedure for process and product design and decision-making[J]. Journal of Cleaner Production. 2004, (12): 59-76.
[112] R. T. D. Prabhakaran, B. J. C. Babu, V. P. Agrawal. Optimum selection of a composite product system using MADM approach[J]. Materials and Manufacturing Processes. 2006, (21): 883-891.
[113] E. E. Karsak, S. Sozer, S. E. Alptekin. Product planning in quality function deployment using a combined analytic network process and goal programming approach[J]. Computers & Industrial Engineering. 2003, 44(1): 171-190.
[114] Z. Zhang, X. Chu. A new integrated decision-making approach for design alternative selection for supporting complex product development[J]. International Journal of Computer Integrated Manufacturing. 2009, (22): 179-198.
[115] K. Tahera, R. N. Ibrahim, P. B. Lochert. Determination of the optimal production run and the optimal initial means of a process with dependent multiple quality characteristics subject to a random deterioration[J]. International Journal of Advanced Manufacturing Technology. 2008, 39(5-6): 623-632.
[116] W. M. Chan, R. N. Ibrahim. Evaluating the quality level of a product with multiple quality characterisitcs[J]. International Journal of Advanced Manufacturing Technology. 2004, 24(9-10): 738-742.
[117]龚庆详.型号可靠性工程手册[M].北京:国防工业出版社, 2007.
[118]赵东元,樊虎,任志久.可靠性工程与应用[M].北京:国防工业出版社, 2009.
[119]乔永辉.一种基于TOPSIS的多属性决策方法研究[J].企业技术开发, 2006,25(9): 89-91.
[120] M. A. Abo-Sinna, A. H. Amer. Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems[J]. Applied Mathematics and Computation. 2005, 162(1): 243-256.
[121] M. Azzam, A. A. Mousa. Using genetic algorithm and TOPSIS technique for multiobjective reactive power compensation[J]. Electric Power Systems Research. 2010, 80(6): 675-681.
[122] E. Bottani, A. Rizzi. A fuzzy TOPSIS methodology to support outsourcing of logistics services[J]. Supply Chain Management-an International Journal. 2006, 11(4): 294-308.
[123] N. D. Chakladar, S. Chakraborty. A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection[J]. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture. 2008, 222(12): 1613-1623.
[124] Z. Gligoric, C. Beljic, V. Simeunovic. Shaft location selection at deep multiple orebody deposit by using fuzzy TOPSIS method and network optimization[J]. Expert Systems With Applications. 2010: 1408-1418.
[125] C. Kahraman, N. Y. Ates, S. Cevik, et al. Hierarchical fuzzy TOPSIS model for selection among logistics information technologies[J]. Journal of Enterprise Information Management. 2007, 20(2): 143-168.
[126] G. Kannan, S. Pokharel, P. S. Kumar. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider[J]. Resources Conservation and Recycling. 2009, 54(1): 28-36.
[127]余雁,梁墚.多指标决策TOPSIS方法的进一步探讨[J].系统工程, 2003, 21(2): 98-101.
[128]陈伟.关于TOPSIS法应用中的逆序问题及消除的方法[J].运筹与管理, 2005, 14(3): 39-43.
[129]余雁,梁樑,蒋跃进,等.一种新的基于模糊偏好的TOPSIS改进方法[J].系统工程, 2004, 22(8): 87-90.
[130]彭勇行.管理决策分析[M].北京:科学出版社, 2000.
[131] H. C. Gea, K. Oza. Two-level approximation method for reliability-based design optimisation[J]. International Journal of Materials & Product Technology. 2006, 25(1-3): 99-111.
[132] K. Chwail, K. K. Choi. Reliability-based design optimization using response surface method with prediction interval estimation[J]. Journal of Mechanical Design. 2008, 130(12): 121401-121412.
[133] B. D. Youn, K. K. Choi, Y. H. Park. Hybrid analysis method for reliability-based design optimization[J]. Transactions of the ASME. Journal of Mechanical Design. 2003, 125(2): 221-232.
[134] X. P. Du, W. Chen. Sequential optimization and reliability assessment method for efficient probabilistic design[J]. Journal of Mechanical Design. 2004, 126(2): 225-233.
[135] I. Lee, K. K. Choi, L. Du, et al. Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems[J]. Computer Methods In Applied Mechanics and Engineering. 2008, 198(1): 14-27.
[136] Z. Qiang, L. Shouju, T. Ying. Multi-objective Incomplete Probability Information Optimization Reliability Design Based on Ant Colony Algorithm[J]. Journal of Software Engineering and Applications. 2009: 350-353.
[137] K. Injoong. Reliability object model tree (ROM-Tree): a system design-for-reliability method[J]. Microelectronics Reliability. 2010: 438-446.
[138] R. Benayoun, B. Roy, N. Sussman. Manual de reference du programme electre[J]. Note De Synthese et Formaton. 1966, 25.
[139] B. Roy. Classement et choix en presence de points de vue multiples:La methode ELECTRE[J]. R.I.R.O. 1968, (8): 57-75.
[140] A. Teixeira De Almeida. Multicriteria modelling for a repair contract problem based on utility and the ELECTRE I method[J]. IMA Journal of Management Mathematics. 2002, 13(1): 29-37.
[141] A. Shanian, O. Savadogo. ELECTRE I decision support model for material selection of bipolar plates for Polymer Electrolyte Fuel Cells applications[J]. Journal of New Materials For Electrochemical Systems. 2006, 9(3): 191-199.
[142] A. T. de Almeida. Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method[J]. Computers & Operations Research. 2007, 34(12): 3569-3574.
[143] M. Amiri, N. E. Nosratian, A. Jamshidi, et al. Developing a new ELECTRE method with interval data in multiple attribute decision making problems[J]. Journal of Applied Sciences. 2008: 4017-4028.
[144] J. R. Figueira, S. Greco, B. Roy. ELECTRE methods with interaction between criteria: An extension of the concordance index[J]. European Journal of Operational Research. 2009, 199(2): 478-495.
[145]陈雪峰,曹宏瑞,何正嘉.数字化制造装备的主轴服役性能监测与诊断[J].机械工程学报, 2009, 45(5): 140-146.
[146]周祖德,谭跃刚,王汉熙.探讨数字制造科学内涵,展望制造全球化未来[J].国际学术动态, 2007, (4): 17-20.
[147]刘志峰,王淑旺,万举勇,等.基于模糊物元的绿色产品评价方法[J].中国机械工程, 2007, 18(2): 166-170.
[148]张志强,徐斌,何勇灵,等.基于AHP评价方法的发动机性能评价[J].兵工学报, 2008, 29(5): 625-628.
[149] A. Azadeh, S.F. Ghaderi, M. F. Ahmadabad. Multi criteria quality assessment of products by integrated DEA-PCA approach[J]. International Journal of Reliability, Quality and Safety Engineering, 2007,14(3): 201-218.
[150] R. Pal, J. Mitra, M.N. Pal. Evaluation of relative performance of product designs: a fuzzy DEA approach to quality function deployment[J]. Opsearch, 2007, 44(4): 322-336.
[151]李菲,沈虹.面向再制造的产品质量特性评价方法[J].现代制造工程, 2008, (11): 99-102.
[152] E. Bashkansky, T. Gadrich. Evaluating quality measured on a ternary ordinal scale[J]. Quality and Reliability Engineering International, 2008, 24(8): 957-971.
[153] Y. W. Bai, Z. N. Chen, H. Z. Bin, et al. Collaborative design in product development based on product layout model[J]. Robotics and Computer-integrated Manufacturing. 2005, (21): 55-65.
[154] R. R. Inman, D. E. Blumenfeld, N. Huang, et al. Designing production systems for quality: Research opportunities from an automotive industry perspective[J]. International Journal of Production Research. 2003, (41): 1953-1971.
[155] C. Liu, X. Li, W. Sun, et al., Fuzzy synthesized evaluation of product quality of machine tool based on harmonious design method[C]. The 7th International Conference on e-Engineering and Digital Enterprise Technology, Shenyang, China, 2009, 1382-1386.
[156] J. Kovach, B.R. Cho, J. Antony. Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming[J]. International Journal of Advanced Manufacturing Technology, 2008, 35(11/12) :1100-1112.
[157] F.C. Wu, C.C. Chyu. Optimization of correlated multiple quality characteristics robust design using principal component analysis[J]. Journal of Manufacturing Systems, 2004, 23(2) :134-143.
[158] A. Aggarwal, H. Singh, P. Kumar, et al. Optimization of multiple quality characteristics for CNC turning under cryogenic cutting environment using desirability function[J]. Journal of Materials Processing Technology, 2008, 205(1/3): 42-50.
[159] Y. Q. Wang, G. X. Shen, Y. Z. Jia. Multidimensional force spectra of CNC machine tools and their applications, part two: reliability design of elements[J]. International Journal of Fatigue. 2003, 25(5): 447-452.
[160]黄剑,周林,栗秋华,等.基于物元分析理论的电能质量综合评估[J].重庆大学学报(自然科学版) , 2007, 30(6): 25-29.
[161] Y.W. Zhao, G.X. Zhang. A new integrated design method based on fuzzy matter-element optimization[J]. Journal of Materials Processing Technology, 2002, 129(1/3): 612-618.
[162] Y.C. Yin, L.F. Sun, C. Guo. A policy of conflict negotiation based on fuzzy matter element particle swarm optimization in distributed collaborative creative design[J]. Computer-Aided Design, 2008, 40(10/11): 1009-1014.
[163] Y.W. Zhao, F.L. Huang, Z.F. Li, et al. Research in method of reliability optimization based-on multi-objective fuzzy matter-element with fuzzy chance constraint[J]. Key Engineering Materials, 2006, 315/316: 430-435.
[164] M. Saleh. Estimating market shares in each market segment using the information entropy concept[J]. Applied Mathematics and Computation, 2006, 190(2): 1735-1739.
[165]袁杰,史海波,刘昶.基于最小二乘拟合的模糊隶属函数构建方法[J].控制与决策, 2008, 23(11): 1263-1266.
[166] D. Tesfamariam, B. Lindberg. Aggregate analysis of manufacturing systems using system dynamics and ANP[J]. Computers and Industrial Engineering. 2005, (49): 98-117.
[167] C. Garuti, I. Spencer. Parallels between the analytic hierarchy and network processes (AHP/ANP) and fractal geometry[J]. Mathematical and Computer Modelling. 2007, (46): 926-934.
[168] S.H. Chung, A. H. I. Lee, W. L. Pearn. Analytic network process (ANP) approach for product mix planning in semiconductor fabricator[J]. International Journal of Production Economics. 2005, (96): 15-36.
[169] R. P. Mohanty, R. Agarwal, A. K. Choudhury, et al. A fuzzy ANP-based approach to RD project selection: A case study[J]. International Journal of Production Research. 2005, (43): 5199-5216.
[170] J. P. Pastor-Ferrando, P. Aragones-Beltran, A. Hospitaler-Perez, et al. An ANP and AHP-based approach for weighting criteria in public works bidding[J]. Journal of The Operational Research Society. 2010, (61): 905-916.
[171] H. Lee, C. Kim, H. Cho, et al. An ANP-based technology network for identification of core technologies: A case of telecommunication technologies[J]. Expert Systems With Applications. 2009, (36): 894-908.
[172]陈永国.偏最小二乘法在公共部门绩效多元评估中的应用[J].系统工程理论与实践, 2009, 29(1): 89-96.
[173]刘志平,石林英.最小二乘法原理及其MATLAB实现[J].中国西部科技, 2005, 7(17): 33-34.
[174]张赤斌,史金飞,易红.基于偏最小二乘法回归的工序质量建模[J].东南大学学报(自然科学版), 2005, 35(5): 702-705.
[175] T. Yuo-Tern, C. Yu-Min. Function-based cost-estimation integrating quality function deployment to support system design[J]. International Journal of Advanced Manufacturing Technology. 2004, 23(7-8): 514-522.
[2]张根保.现代质量工程[M].北京:机械工业出版社, 2004.
[3]张公绪,孙静.新编质量管理学(第二版) [M].北京:高等教育出版社, 2003.
[4]林志航.产品设计与制造质量工程[M].北京:机械工业出版社, 2005.
[5] P. Lung-Kwang, W. Che-Chung, S. L. Wei, et al. Optimizing multiple quality characteristics via Taguchi method-based Grey analysis[J]. Journal of Materials Processing Technology. 2007, 182(1-3): 107-116.
[6] A. G.David. What Does‘Product Quality’Mean? [J]. Sloan Management Review,Fall 1984,26(1): 25–43.
[7] JB/T5058-2006,中华人民共和国机械行业标准?机械工业产品质量特性重要度分级导则[S].北京:机械工业出版社, 2006:1-3..
[8] Y.H. He, W. Chang. Systematical design quality control model based on key quality characteristics [C]. 2009 IEEE 16th International Conference on Industrial Engineering and Engineering Management, Beijing, China, 2009, 1208-1212.
[9]何益海,常文兵.基于关键质量特性的产品设计质量保证方法研究[C].第7届国际可靠性、维修性、安全性学术会议, 2007.
[10]孙伟,刘杰,李小彭,等.机械产品设计质量评价及评价系统的开发[J].机械设计, 2007, 24(6):5-7.
[11]荆洪英,张利,闻邦椿.基于层次分析法的产品设计质量权重分配[J].东北大学学报(自然科学版), 2009, 5(30):712-715.
[12] Y. Reich, E. Levy. Managing product design quality under resource constraints[J]. International Journal of Production Research. 2004, (42): 2555-2572.
[13] M. L. Swink, R. Calantone. Design-manufacturing integration as a mediator of antecedents to new product design quality[J]. IEEE Transactions on Engineering Management. 2004, (51): 472-482.
[14] S. K. Mukhopadhyay, R. Setaputra. A dynamic model for optimal design quality and return policies[J]. European Journal of Operational Research. 2007, (180): 1144-1154.
[15] J. Freiesleben. Proposing a new approach to discussing economic effects of design quality[J]. International Journal of Production Economics. 2010, (124): 348-359.
[16]丁莲,吴锡英,汤文成.产品设计质量评价指标体系的研究[J].机械工业自动化, 1997, 19(4):5-8.
[17]袁清珂,王宏,赵汝嘉.产品设计质量综合评价的理论研究[J].组合机床与自动化加工技术, 1998, (5):35-38.
[18]商建东,陈康宁.质量驱动的产品设计质量模糊评价及方案决策方法研究[J].中国机械工程, 2000, 11(12):1394-1398.
[19]王洪,唐晓青.面向现代产品设计的设计质量评估方法[J].中国质量. 2001, (12): 9-13.
[20]杨明顺,李言,林志航.产品总体设计方案评价与优选的组合决策[J].计算机集成制造系统, 2006, 4(10): 540-545.
[21]孙伟,李小彭,李朝峰,等.面向机械产品设计的多层次规划研究[J].计算机集成制造系统, 2009, 15(3) : 431-439.
[22] D. M. Osborne, R. L. Armacost. Review of techniques for optimizing multiple quality characteristics in product development[J]. Computers and Industrial Engineering. 1996, 31(1-2): 107-110.
[23] J. H. Byun, K. J. Kim. On robust design for multiple quality characteristics[J]. Quality Improvement through Statistical Methods. 1998: 289-297.
[24] I. G. Ali, Y. T. Chen. Design quality and robustness with neural networks[J]. IEEE Trans Neural Netw. 1999, 10(6): 1518-1527.
[25] W. Ful-Chiang, C. Chiuh-Cheng. Optimization of robust design for multiple quality characteristics[J]. International Journal of Production Research. 2004, 42(2): 337-354.
[26]胡金龙.试探“质量特性分级”的应用[J].企业标准化, 1994, (1):40-42.
[27]王宗君.机械零件最佳选定质量特性重要度是实行质量重点控制的重要举措[J].机械工业标准化与质量, 1999, (1):28-29.
[28]刘宏.产品质量特性重要度分级在质量链中的重要作用[J].电子质量, 2007, (1):45-47.
[29]王颖.基于TOPSIS法的多元质量特性优化方法研究[D].博士学位论文,天津大学, 2007.
[30]张根保,庞继红,任显林,等.机械产品多元质量特性重要度排序方法研究[J].计算机集成制造系统, 2011, 17(1):151-158.
[31] R. Krishnapillai, A. Zeid. Mapping product design specification for mass customization[J]. Journal of Intelligent Manufacturing. 2006, (17): 29-43.
[32] Chu Xue-Zheng, Gao Liang, Qiu Hao-Bo, et al., An expert system using rough sets theory and self-organizing maps to design space exploration of complex products[J]. Expert Systems with Applications. 2010, 37(11):7364-7372.
[33]许多,严洪森.基于模糊度量质量屋的产品特征映射及分析[J].计算机集成制造系统, 2004, 10(6):693-698.
[34]车君华,谭建荣,冯毅雄,等.产品配置设计的多层次映射求解模型研究[J].中国机械工程, 2006, 17(8): 849-853.
[35]崔剑,祁国宁,纪杨建等.基于需求流动链的映射机理[J].机械工程学报, 2008, 7(44): 93-100.
[36]张根保,纪富义,任显林,等.复杂机电产品关键质量特性提取模型[J].重庆大学学报(自然科学版), 2010, 33(2):8-14.
[37]邓军,余忠华,吴昭同.产品质量与过程质量的映射研究[J].中国机械工程, 2010, (17):58-62.
[38]雷鸣,朱祖平,姚立纲.质量功能展开中顾客需求自动映射[J].计算机集成制造系统, 2009, 15(3):?592-597.
[39] K. C. Kapur, B. R. Cho. Economic design of the specification region for multiple quality characteristics[J]. Iie Transactions. 1996, 28(3): 237-248.
[40] D. C. Kim, D. L. Jones. Multiple parameter optimization incorporating mixed quality characteristics[J]. Computer Aided Optimum Design of Structures Vii. 2001, (10): 309-326.
[41] J. Teeravaraprug, B. R. Cho. Designing the optimal process target levels for multiple quality characteristics[J]. International Journal of Production Research. 2002, (40): 37-54.
[42] H. Yi-hai, T. Xiao-qing, C. Wen-bing. Technical Decomposition Approach of Critical to Quality Characteristics for Product Design for Six Sigma[J]. Quality and Reliability Engineering International. 2010: 325-339.
[43] G. F. Zhai, H. M. Liang, F. Xu, et al. New method of tolerance design of electromagnetic relay reliability[J]. Journal of Engineering Design. 2004, 15(5): 425-431.
[44]余忠华.保质设计中的质量评价研究[J].计算机集成制造系统, 2001, 7(6):65-67.
[45]邓军,余忠华,吴昭同.基于信息熵的概念设计的质量评价[J].浙江大学学报(工学版), 2009, 43(8):1480-1483.
[46]钟晓芳,韩之俊.利用主成分分析对多质量特性的优化设计[J].南京理工大学学报:自然科学版, 2003, 27(3): 301-304.
[47]王洪,唐晓青.面向质量设计的质量评估集成模型[J].计算机集成制造系统, 2002, 8 (2):122-125.
[48]何桢,吕海利,多元质量特性稳健性设计方法的优化研究[J].管理科学, 2007, 20(1): 2-7.
[49]魏喆,谭建荣,冯毅雄.广义性能驱动的机械产品方案设计方法[J].机械工程学报, 2008, 44(5):?1-10.
[50] J. Wu, C. Deng, X. Y. Shao, et al. A reliability assessment method based on support vector machines for CNC equipment[J]. Science In China Series E-technological Sciences. 2009, 52(7): 1849-1857.
[51] G. K. Adil. Concurrent consideration of product design, process planning and production planning activities[J]. Production Planning and Control. 1998, (9): 167-175.
[52] Y. Han, H. Zhu. Bayesian mean vector control chart for multiple quality characteristics[J]. Journal of Nanjing University of Science and Technology. 2003, 27(5): 561-566.
[53] F.C. Wu, C.H. Yeh. Robust design of multiple dynamic quality characteristics[J]. International Journal of Advanced Manufacturing Technology. 2005, (25): 579-588.
[54] [美]约瑟夫·M·朱兰主编,焦叔斌等译. (Juran’s Quality Handbook)《朱兰质量手册》(第一版),北京:中国人民大学出版社, 2003.
[55]何益海,唐晓青,王美清.产品设计质量数据与管理模型研究[J].计算机集成制造系统, 2006, 12(8):1161-1166
[56] O. Feyzioglu, G. Buyukozkan. An integrated group decision-making approach for new product development[J]. International Journal of Computer Integrated Manufacturing. 2008, (21): 366-375.
[57]肖人彬,蔡政英.不确定质量水平下闭环质量链的成本模糊控制[J].计算机集成制造系统, 2009, 15(6):?1207-1214.
[58]金国强,刘恒江.质量链管理理论研究综述[J].世界标准化与质量管理, 2006, 3(3):21-24.
[59]肖人彬,蔡政英.应用关联规则构建质量活动链模型[J].北京航空航天大学学报, 2004, 30(5):?448-451.
[60]龚益鸣.质量管理学[M].上海:复旦大学出版社, 2004.
[61]张根保,何桢,刘英.质量管理与可靠性[M].北京:中国科学技术出版社, 2005.
[62] [美]克劳斯比,零缺点的质量管理[M],北京:中信出版社, 2000.
[63] F.C. Wu, C.C. Chyu. Optimization of correlated multiple quality characteristics robust design using principal component analysis[J]. Journal of Manufacturing Systems, 2004, 23 (2) :134-143.
[64]戴伟,唐晓青.基于质量特性的产品质量策划模型及应用[J].计算机集成制造系统, 2009, 15(10):1938-1945.
[65]何桢,赵有,马彦辉.模糊QFD中技术特性重要度排序方法[J].天津大学学报, 2008, 41(5): 631-634.
[66]何益海,唐晓青.基于关键质量特性的产品保质设计[J].航空学报, 2007, 28 (6):1468-1481.
[67]郭惠昕,任丕顺,张国平.多质量指标的解耦与稳健优化设计[J].农业机械学报, 2009, 40(1): 203-207.
[68]马义中,赵逢禹.多元质量特性的稳健设计及其实现[J].系统工程与电子技术, 2005, 27(9):1580-1582.
[69] S.M. Govindaluri, B.R. Cho. Robust design modeling with correlated quality characteristics using a multicriteria decision framework[J]. International Journal of Advanced Manufacturing Technology, 2007, 32(5) :423-433.
[70] E.E. Karsak, S. Sozer, S.E. Alptekin. Product planning in quality function development using a combined analytic network process and goal programming approach[J]. Computer & Industrial Engineering, 2002, (44):171-190.
[71] Yang Chingchow, Chen Baisheng. Key quality performance evaluation using fuzzy AHP[J]. Journal of the Chinese Institute of Industrial Engineers. 2004, 21(6):543-550.
[72] F.C. Wu. Robust design of nonlinear multiple dynamic quality characteristics[J]. Computers and Industrial Engineering. 2009, (56): 1328-1332.
[73] Jia Yue, Song Baowei, Zhao Xiangtao, et al. Method of combat programs optimization by fuzzy AHP based on entropy weight[J]. Journal of System Simulation. 2008, 20(11):2965-2968.
[74]朱松岭,周平,韩毅.基于模糊层次分析法的风险量化研究[J].计算机集成制造系统, 2004, 10(8):980-984.
[75]王美乂,张凤鸣,刘智.模糊信息的熵权多属性决策方案评估方法[J].系统工程与电子技术, 2006, 28(10):1523-1525.
[76]李柏年.模糊数学及其应用[M].合肥:合肥工业大学出版社, 2007.
[77] E. Levner, D. Alcaide. SICILIA J. Multi-attribute text classification using the fuzzy Borda method and semantic grades[C]// Conference of the 7th International Workshop on Fuzzy Logic and Applications, Berlin, Germany:Springer, 2007:422-429.
[78] T. L. Saaty. The Analytic Hierarchy Process[J]. New York:McGraw Hill. 1980.
[79] S. Parthasarathy, N. Anbazhagan. Evaluating ERP implementation choices using AHP[J]. International Journal of Enterprise Information Systems. 2007: 52-65.
[80] T. Soota, H. Singh, R. Mishra. Defining characteristics for product development using quality function deployment: a case study on Indian bikes[J]. Quality Engineering. 2008: 195-208.
[81] S. F. Ding, Z. Z. Shi. Studies on incidence pattern recognition based on information entropy[J]. Journal of Information Science. 2005, 31(6): 497-502.
[82] C. M. Sarris, A. N. Proto. Information entropy and nonlinear semiquantum dynamics[J]. International Journal of Bifurcation and Chaos. 2009, 19(10): 3473-3484.
[83] L. S. Zhou, B. Ding. Using Information Entropy to Analyze Dynamic Reserves of Emergency Material[C]. International Joint Conference on Computational Sciences and Optimization. 2009, (12): 609-612.
[84] S. Naimuddin, S.B. Warkad, G.M. Dhole, et al. Enhancing the operational availability of CNC machine using fuzzy logic controller[J]. Journal of Theoretical and Applied Information Technology. 2009, 6(2) : 145-154.
[85] H. Raharjo, A.C. Brombacher, T.N. Goh, et al. On integrating kano's model dynamics into QFD for multiple product design[J]. Quality and Reliability Engineering International. 2010, 26(4): 351-363.
[86] J.R. Sharma, A.M. Rawani. Ranking customer requirements in QFD by factoring in their interrelationship values[J]. Quality Management Journal. 2007,14 (4):53-60.
[87] F. Jariri, S.H. Zegordi. Quality function deployment planning for platform design[J]. International Journal of Advanced Manufacturing Technology. 2008, 36(5-6): 419-430.
[88] K. Delice e, Z. Gungor. A new mixed integer linear programming model for product development using quality function deployment[J]. Computers & Industrial Engineering. 2009, 57(3): 906-912.
[89]王美清,唐晓青.产品设计中的用户需求与产品质量特征映射方法研究[J].机械工程学报, 2004, 40(5): 136-140.
[90]杨明顺,李言,林志航.顾客需求向技术特征映射的产品设计规划求解[J].计算机集成制造系统, 2006, 16(6): 853-856.
[91]李延来,姚建明,焦明海.基于BSC的质量屋中工程特性最终重要度确定方法[J].机械工程学报, 2010, 46(4): 177-185.
[92] M. Celik, S. Cebi, C. Kahraman, et al. An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market[J]. Expert Systems With Applications. 2009, 36(3): 6227-6235.
[93] T. Sakao. A QFD-centred design methodology for environmentally conscious product design[J]. International Journal of Production Research. 2007, 45(18-19): 4143-4162.
[94] T. Matulja, N. Fafandjel, M. Hadjina. Small craft basic characteristics determination applying QFD technique[J]. Engineering Review. 2008, 28(2): 11-21.
[95] Z. Pawlak. Rough sets[J]. International Journal of Computer and Information Science. 1982, 11(2): 341-356.
[96] M. J. Beynon, M.J. Peel. Variable precision rough set theory and data discretisation: an application to corporate failure prediction[J]. Omega-international Journal of Management Science. 2001, 29(6): 561-576.
[97] Y. Hassan, E.Tazaki. Induction of knowledge using evolutionary rough set theory[J]. Cybernetics and Systems. 2003, 34(8): 617-643.
[98] T. L. Tseng, Y. Kwon, Y.M. Ertekin. Feature-based rule induction in machining operation using rough set theory for quality assurance[J]. Robotics and Computer-integrated Manufacturing. 2005, 21(6): 559-567.
[99] D.P. Soetanto, M. Van-Geenhuizen. Technology incubators and knowledge networks: a rough set approach in comparative project analysis[J]. Environment and Planning B-Planning & Design. 2007, 34(6): 1011-1029.
[100]郑联语,唐晓青,汪叔淳.基于QFD的数控加工工艺质量优化规划方法[J].机械工程学报, 2001,37(8): 38-42.
[101] L. Jinghong, Z. P. Mourelatos, T. Jian. A single-loop method for reliability-based design optimisation[J]. International Journal of Product Development. 2008, 5(1): 76-92.
[102] Mohsine, G. Kharmanda, A. El-Hami. Improved hybrid method as a robust tool for reliability-based design optimization[J]. Structural and Multidisciplinary Optimization. 2006, 32(3): 203-213.
[103] H. Shimizu, Y. Otsuka, H. Noguchi. Design review based on failure mode to visualise reliability problems in the development stage of mechanical products[J]. International Journal of Vehicle Design. 2010, 53(3): 149-165.
[104] Z. Li, Q. Gao, D. Zhang. Product design on the basis of fuzzy quality function deployment[J]. Journal of Systems Engineering and Electronics. 2008, 19(6): 1165-1170.
[105] R. Byung, M. D. Phillips, J. Kovach. Designing the optimum configurations of circular and spherical product specifications for multiple quality characteristics[J]. Journal of Systems Science and Systems Engineering. 2005, 14(4): 385-399.
[106] H. Raharjo, A. C. Brombacher, M. Xie. Dealing with subjectivity in early product design phase: A systematic approach to exploit Quality Function Deployment potentials[J]. Computers & Industrial Engineering. 2008, 55(1): 253-278.
[107] K. Zhou, S. Feng. Evaluating the conceptual design schemes of complex products based on FAHP using fuzzy number[J]. Journal of Systems Engineering and Electronics. 2005, 16(3): 574-578.
[108] X. F. Zha, R. D. Sriram, W. F. Lu. Evaluation and selection in product design for mass customization: A knowledge decision support approach[J]. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM. 2004, (18): 87-109.
[109] Y. Cui, F. Sun. Fuzzy design scheme selection model based on quality function deployment[J]. Computer Integrated Manufacturing Systems. 2006, 12(2): 220-224.
[110] F. Shi, Z. L. Lou, J. G. Lu, et al. An improved rough set approach to design of gating scheme for injection moulding[J]. International Journal of Advanced Manufacturing Technology.2003, (21): 662-668.
[111] F. I. Khan, R. Sadiq, B. Veitch. Life cycle iNdeX (LInX): A new indexing procedure for process and product design and decision-making[J]. Journal of Cleaner Production. 2004, (12): 59-76.
[112] R. T. D. Prabhakaran, B. J. C. Babu, V. P. Agrawal. Optimum selection of a composite product system using MADM approach[J]. Materials and Manufacturing Processes. 2006, (21): 883-891.
[113] E. E. Karsak, S. Sozer, S. E. Alptekin. Product planning in quality function deployment using a combined analytic network process and goal programming approach[J]. Computers & Industrial Engineering. 2003, 44(1): 171-190.
[114] Z. Zhang, X. Chu. A new integrated decision-making approach for design alternative selection for supporting complex product development[J]. International Journal of Computer Integrated Manufacturing. 2009, (22): 179-198.
[115] K. Tahera, R. N. Ibrahim, P. B. Lochert. Determination of the optimal production run and the optimal initial means of a process with dependent multiple quality characteristics subject to a random deterioration[J]. International Journal of Advanced Manufacturing Technology. 2008, 39(5-6): 623-632.
[116] W. M. Chan, R. N. Ibrahim. Evaluating the quality level of a product with multiple quality characterisitcs[J]. International Journal of Advanced Manufacturing Technology. 2004, 24(9-10): 738-742.
[117]龚庆详.型号可靠性工程手册[M].北京:国防工业出版社, 2007.
[118]赵东元,樊虎,任志久.可靠性工程与应用[M].北京:国防工业出版社, 2009.
[119]乔永辉.一种基于TOPSIS的多属性决策方法研究[J].企业技术开发, 2006,25(9): 89-91.
[120] M. A. Abo-Sinna, A. H. Amer. Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems[J]. Applied Mathematics and Computation. 2005, 162(1): 243-256.
[121] M. Azzam, A. A. Mousa. Using genetic algorithm and TOPSIS technique for multiobjective reactive power compensation[J]. Electric Power Systems Research. 2010, 80(6): 675-681.
[122] E. Bottani, A. Rizzi. A fuzzy TOPSIS methodology to support outsourcing of logistics services[J]. Supply Chain Management-an International Journal. 2006, 11(4): 294-308.
[123] N. D. Chakladar, S. Chakraborty. A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection[J]. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture. 2008, 222(12): 1613-1623.
[124] Z. Gligoric, C. Beljic, V. Simeunovic. Shaft location selection at deep multiple orebody deposit by using fuzzy TOPSIS method and network optimization[J]. Expert Systems With Applications. 2010: 1408-1418.
[125] C. Kahraman, N. Y. Ates, S. Cevik, et al. Hierarchical fuzzy TOPSIS model for selection among logistics information technologies[J]. Journal of Enterprise Information Management. 2007, 20(2): 143-168.
[126] G. Kannan, S. Pokharel, P. S. Kumar. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider[J]. Resources Conservation and Recycling. 2009, 54(1): 28-36.
[127]余雁,梁墚.多指标决策TOPSIS方法的进一步探讨[J].系统工程, 2003, 21(2): 98-101.
[128]陈伟.关于TOPSIS法应用中的逆序问题及消除的方法[J].运筹与管理, 2005, 14(3): 39-43.
[129]余雁,梁樑,蒋跃进,等.一种新的基于模糊偏好的TOPSIS改进方法[J].系统工程, 2004, 22(8): 87-90.
[130]彭勇行.管理决策分析[M].北京:科学出版社, 2000.
[131] H. C. Gea, K. Oza. Two-level approximation method for reliability-based design optimisation[J]. International Journal of Materials & Product Technology. 2006, 25(1-3): 99-111.
[132] K. Chwail, K. K. Choi. Reliability-based design optimization using response surface method with prediction interval estimation[J]. Journal of Mechanical Design. 2008, 130(12): 121401-121412.
[133] B. D. Youn, K. K. Choi, Y. H. Park. Hybrid analysis method for reliability-based design optimization[J]. Transactions of the ASME. Journal of Mechanical Design. 2003, 125(2): 221-232.
[134] X. P. Du, W. Chen. Sequential optimization and reliability assessment method for efficient probabilistic design[J]. Journal of Mechanical Design. 2004, 126(2): 225-233.
[135] I. Lee, K. K. Choi, L. Du, et al. Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems[J]. Computer Methods In Applied Mechanics and Engineering. 2008, 198(1): 14-27.
[136] Z. Qiang, L. Shouju, T. Ying. Multi-objective Incomplete Probability Information Optimization Reliability Design Based on Ant Colony Algorithm[J]. Journal of Software Engineering and Applications. 2009: 350-353.
[137] K. Injoong. Reliability object model tree (ROM-Tree): a system design-for-reliability method[J]. Microelectronics Reliability. 2010: 438-446.
[138] R. Benayoun, B. Roy, N. Sussman. Manual de reference du programme electre[J]. Note De Synthese et Formaton. 1966, 25.
[139] B. Roy. Classement et choix en presence de points de vue multiples:La methode ELECTRE[J]. R.I.R.O. 1968, (8): 57-75.
[140] A. Teixeira De Almeida. Multicriteria modelling for a repair contract problem based on utility and the ELECTRE I method[J]. IMA Journal of Management Mathematics. 2002, 13(1): 29-37.
[141] A. Shanian, O. Savadogo. ELECTRE I decision support model for material selection of bipolar plates for Polymer Electrolyte Fuel Cells applications[J]. Journal of New Materials For Electrochemical Systems. 2006, 9(3): 191-199.
[142] A. T. de Almeida. Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method[J]. Computers & Operations Research. 2007, 34(12): 3569-3574.
[143] M. Amiri, N. E. Nosratian, A. Jamshidi, et al. Developing a new ELECTRE method with interval data in multiple attribute decision making problems[J]. Journal of Applied Sciences. 2008: 4017-4028.
[144] J. R. Figueira, S. Greco, B. Roy. ELECTRE methods with interaction between criteria: An extension of the concordance index[J]. European Journal of Operational Research. 2009, 199(2): 478-495.
[145]陈雪峰,曹宏瑞,何正嘉.数字化制造装备的主轴服役性能监测与诊断[J].机械工程学报, 2009, 45(5): 140-146.
[146]周祖德,谭跃刚,王汉熙.探讨数字制造科学内涵,展望制造全球化未来[J].国际学术动态, 2007, (4): 17-20.
[147]刘志峰,王淑旺,万举勇,等.基于模糊物元的绿色产品评价方法[J].中国机械工程, 2007, 18(2): 166-170.
[148]张志强,徐斌,何勇灵,等.基于AHP评价方法的发动机性能评价[J].兵工学报, 2008, 29(5): 625-628.
[149] A. Azadeh, S.F. Ghaderi, M. F. Ahmadabad. Multi criteria quality assessment of products by integrated DEA-PCA approach[J]. International Journal of Reliability, Quality and Safety Engineering, 2007,14(3): 201-218.
[150] R. Pal, J. Mitra, M.N. Pal. Evaluation of relative performance of product designs: a fuzzy DEA approach to quality function deployment[J]. Opsearch, 2007, 44(4): 322-336.
[151]李菲,沈虹.面向再制造的产品质量特性评价方法[J].现代制造工程, 2008, (11): 99-102.
[152] E. Bashkansky, T. Gadrich. Evaluating quality measured on a ternary ordinal scale[J]. Quality and Reliability Engineering International, 2008, 24(8): 957-971.
[153] Y. W. Bai, Z. N. Chen, H. Z. Bin, et al. Collaborative design in product development based on product layout model[J]. Robotics and Computer-integrated Manufacturing. 2005, (21): 55-65.
[154] R. R. Inman, D. E. Blumenfeld, N. Huang, et al. Designing production systems for quality: Research opportunities from an automotive industry perspective[J]. International Journal of Production Research. 2003, (41): 1953-1971.
[155] C. Liu, X. Li, W. Sun, et al., Fuzzy synthesized evaluation of product quality of machine tool based on harmonious design method[C]. The 7th International Conference on e-Engineering and Digital Enterprise Technology, Shenyang, China, 2009, 1382-1386.
[156] J. Kovach, B.R. Cho, J. Antony. Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming[J]. International Journal of Advanced Manufacturing Technology, 2008, 35(11/12) :1100-1112.
[157] F.C. Wu, C.C. Chyu. Optimization of correlated multiple quality characteristics robust design using principal component analysis[J]. Journal of Manufacturing Systems, 2004, 23(2) :134-143.
[158] A. Aggarwal, H. Singh, P. Kumar, et al. Optimization of multiple quality characteristics for CNC turning under cryogenic cutting environment using desirability function[J]. Journal of Materials Processing Technology, 2008, 205(1/3): 42-50.
[159] Y. Q. Wang, G. X. Shen, Y. Z. Jia. Multidimensional force spectra of CNC machine tools and their applications, part two: reliability design of elements[J]. International Journal of Fatigue. 2003, 25(5): 447-452.
[160]黄剑,周林,栗秋华,等.基于物元分析理论的电能质量综合评估[J].重庆大学学报(自然科学版) , 2007, 30(6): 25-29.
[161] Y.W. Zhao, G.X. Zhang. A new integrated design method based on fuzzy matter-element optimization[J]. Journal of Materials Processing Technology, 2002, 129(1/3): 612-618.
[162] Y.C. Yin, L.F. Sun, C. Guo. A policy of conflict negotiation based on fuzzy matter element particle swarm optimization in distributed collaborative creative design[J]. Computer-Aided Design, 2008, 40(10/11): 1009-1014.
[163] Y.W. Zhao, F.L. Huang, Z.F. Li, et al. Research in method of reliability optimization based-on multi-objective fuzzy matter-element with fuzzy chance constraint[J]. Key Engineering Materials, 2006, 315/316: 430-435.
[164] M. Saleh. Estimating market shares in each market segment using the information entropy concept[J]. Applied Mathematics and Computation, 2006, 190(2): 1735-1739.
[165]袁杰,史海波,刘昶.基于最小二乘拟合的模糊隶属函数构建方法[J].控制与决策, 2008, 23(11): 1263-1266.
[166] D. Tesfamariam, B. Lindberg. Aggregate analysis of manufacturing systems using system dynamics and ANP[J]. Computers and Industrial Engineering. 2005, (49): 98-117.
[167] C. Garuti, I. Spencer. Parallels between the analytic hierarchy and network processes (AHP/ANP) and fractal geometry[J]. Mathematical and Computer Modelling. 2007, (46): 926-934.
[168] S.H. Chung, A. H. I. Lee, W. L. Pearn. Analytic network process (ANP) approach for product mix planning in semiconductor fabricator[J]. International Journal of Production Economics. 2005, (96): 15-36.
[169] R. P. Mohanty, R. Agarwal, A. K. Choudhury, et al. A fuzzy ANP-based approach to RD project selection: A case study[J]. International Journal of Production Research. 2005, (43): 5199-5216.
[170] J. P. Pastor-Ferrando, P. Aragones-Beltran, A. Hospitaler-Perez, et al. An ANP and AHP-based approach for weighting criteria in public works bidding[J]. Journal of The Operational Research Society. 2010, (61): 905-916.
[171] H. Lee, C. Kim, H. Cho, et al. An ANP-based technology network for identification of core technologies: A case of telecommunication technologies[J]. Expert Systems With Applications. 2009, (36): 894-908.
[172]陈永国.偏最小二乘法在公共部门绩效多元评估中的应用[J].系统工程理论与实践, 2009, 29(1): 89-96.
[173]刘志平,石林英.最小二乘法原理及其MATLAB实现[J].中国西部科技, 2005, 7(17): 33-34.
[174]张赤斌,史金飞,易红.基于偏最小二乘法回归的工序质量建模[J].东南大学学报(自然科学版), 2005, 35(5): 702-705.
[175] T. Yuo-Tern, C. Yu-Min. Function-based cost-estimation integrating quality function deployment to support system design[J]. International Journal of Advanced Manufacturing Technology. 2004, 23(7-8): 514-522.