基于云化QFD的采煤机服务型制造模型构建
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摘要
为更好地满足用户需求并给企业带来新的价值增长点,将"产品+服务"理念引入到采煤机概念设计中,构建基于云化质量功能配置的服务型制造模型,实现有形产品与无形服务的相互促进和影响。以顾客价值为驱动,围绕顾客价值挖掘、价值要素到方案特征的传递与分配及产品与服务方案交互配置设计3个关键问题,以采煤机为研究对象,构建基于价值域、性能域、方案域、3个域之间映射关系及产品与服务交互设计的服务型制造模型框架。首先从顾客期望系统实现的目标出发,构建包含结果层和属性层的价值域模型挖掘顾客价值,采用云模型处理具有模糊性和随机性的群决策值得到价值要素基本重要度,并利用市场竞争性分析和卡诺模型对其修正以提高其准确性。然后将云模型嵌入质量功能配置中实现价值要素到方案特征的传递分配,并充分利用以往采煤机产品设计中积累的大量数据,基于极限学习机理论进行模型推理辅助方案特征目标值分析。最后基于方案特征目标值进行产品与服务的交互配置和实例化信息交互得到服务型制造方案。该服务型制造模型能够从顾客期望系统实现的目标出发深入挖掘顾客价值,解决了群决策的模糊性和随机性问题、提高价值要素重要度的准确性并实现价值要素到产品与服务两类不同对象的传递与分配,通过产品服务交互设计获取产品与服务相互促进的整体解决方案。服务型制造方案与单一产品方案相比实现了服务对产品的促进和影响,完善了采煤机产品设计且以个性化的服务提高企业竞争力、提升用户满意度。结合采煤机典型机械产品,验证了上述方法的可行性与有效性。
In order to better meet the needs of users and bring new value growth points to enterprises,the concept of"product+service"is introduced into the conceptual design of shearer.A service-oriented manufacturing( SOM) model based on quality function deployment embedded cloud model( QFD-ECM) is constructed to realize the mutual promotion and influence between tangible products and intangible services.Driven by customer value,a shearer SOM model framework based on mapping relationship among value domain,performance domain,and scheme domain and product-service interaction design is constructed around three key issues of customer value mining,transfer and distribution of value elements to scheme features and interactive design of product and service.Firstly,according to the goal of customer expectation,the value domain model including result layer and attribute layer is constructed to mine customer value.The cloud model is used to deal with the group decision-making with fuzziness and randomness to obtain the basic importance of value element,and the basic importance is modified by the market competition analysis and Kano model to improve its accuracy.Then the cloud model is embedded in QFD to realize the transfer and distribution of value elements to scheme characteristics.Combining with the large amount of data accumulated in the past shearer design,the model reasoning based on extreme learning machine is used to assist the analysis of target value of scheme characteristics.Finally,a SOM scheme is obtained by the interactive configuration of products and services and instantiated information exchange based on the target value.This model can excavate customer value from the goal of customer expectation,solve the problem of fuzziness and randomness in group decision-making,improve the accuracy of the value elements importance,realize the transfer and distribution of value elements to two different objects of product and service,and obtain the overall solution of mutual promotion between product and service.Compared with the single product scheme,the SOM scheme achieves the promotion of service to products,improves the design of shearer,and improves the competitiveness of enterprises and customer satisfaction with personalized service.Combining with typical mechanical products of shearer,the feasibility and validity of above methods are verified.
In order to better meet the needs of users and bring new value growth points to enterprises,the concept of"product+service"is introduced into the conceptual design of shearer.A service-oriented manufacturing( SOM) model based on quality function deployment embedded cloud model( QFD-ECM) is constructed to realize the mutual promotion and influence between tangible products and intangible services.Driven by customer value,a shearer SOM model framework based on mapping relationship among value domain,performance domain,and scheme domain and product-service interaction design is constructed around three key issues of customer value mining,transfer and distribution of value elements to scheme features and interactive design of product and service.Firstly,according to the goal of customer expectation,the value domain model including result layer and attribute layer is constructed to mine customer value.The cloud model is used to deal with the group decision-making with fuzziness and randomness to obtain the basic importance of value element,and the basic importance is modified by the market competition analysis and Kano model to improve its accuracy.Then the cloud model is embedded in QFD to realize the transfer and distribution of value elements to scheme characteristics.Combining with the large amount of data accumulated in the past shearer design,the model reasoning based on extreme learning machine is used to assist the analysis of target value of scheme characteristics.Finally,a SOM scheme is obtained by the interactive configuration of products and services and instantiated information exchange based on the target value.This model can excavate customer value from the goal of customer expectation,solve the problem of fuzziness and randomness in group decision-making,improve the accuracy of the value elements importance,realize the transfer and distribution of value elements to two different objects of product and service,and obtain the overall solution of mutual promotion between product and service.Compared with the single product scheme,the SOM scheme achieves the promotion of service to products,improves the design of shearer,and improves the competitiveness of enterprises and customer satisfaction with personalized service.Combining with typical mechanical products of shearer,the feasibility and validity of above methods are verified.
引文
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[16] KANG H,KANG M,YOON S,et al. A consumer value analysis of mobile internet protocol television based on a means-end chain theory[J].Service Business,2014,8(4):587-613.
[17]谢友柏.现代设计理论中的若干基本概念[J].机械工程学报,2007,43(11):7-16.XIE Youbai.Some basic concepts in modern design theory[J].Journal of Mechanical Engineering,2007,43(11):7-16.
[18]陈昊,李兵,刘常昱.一种无确定度的逆向云算法[J].小型微型计算机系统,2015,36(3):544-549.CHEN Hao,LI Bing,LIU Changyu.An algorithm of backward cloud without certainty degree[J].Journal of Chinese Computer Systems,2015,36(3):544-549.
[19] NAHM YE,ISHIKAWA H,INOUE M.New rating methods to prioritize customer requirements in QFD with incomplete customer preferences[J].International Journal of Advanced Manufacturing Technology,2013,65(9-12):1587-1604.
[20]丁华,常琦,杨兆建,等.基于极限学习机的采煤机功率预测算法研究[J].煤炭学报,2016,41(3):794-800.DING Hua,CHANG Qi,YANG Zhaojian,et al.Research on the algorithm of shearer power prediction based on extreme learning machine[J].Journal of China Coal Society,2016,41(3):794-800.
[2]张凯,赵武,王杰,等.面向服务型制造的产品设计信息集成方法[J].工程科学与技术,2018,50(2):204-211.ZHANG Kai,ZHAO Wu,WANG Jie,et al. Information integration method of product design for service-embedded manufacturing[J].Advanced Engineering Science,2018,50(2):204-211.
[3] LEE S,HAN W,PARK Y. Measuring the functional dynamics of product-service system:A system dynamics approach[J].Computers&Industrial Engineering,2015(80):159-170.
[4] KANG Yuyun,TANG Dunbing.Matrix-based computational conceptual design with ant colony optimization[J]. Journal of Engineering Design,2013,24(6):429-452.
[5]杨波,高常青,尹晓玲,等.面向概念设计的扩展功能建模方法[J].机械工程学报,2013,49(15):153-162.YANG Bo,GAO Changqing,YIN Xiaoling,et al. Extended product function modeling for conceptual design[J]. Journal of Mechanical Engineering,2013,49(15):153-162.
[6]刘伟,曹国忠,檀润华,等.基于产品基因与物理表达的变型设计[J].计算机集成制造系统,2015,21(2):381-391.LIU Wei,CAO Guozhong,TAN Runhua,et al. Variant design based on products genes and physical description[J]. Computer Integrated Manufacturing Systems,2015,21(2):381-391.
[7]丁华,杨兆建.采煤机概念设计融合推理模型研究与实践[J].煤炭学报,2010,35(10):1748-1753.DING Hua,YANG Zhaojian. Research and practice of shearer conceptual design fusion reasoning model[J].Journal of China Coal Society,2010,35(10):1748-1753.
[8]丁华,邓金涛,王义亮,等.采煤机概念设计生态学模型构建与实现[J].煤炭学报,2018,43(7):2068-2076.DING Hua,DENG Jintao,WANG Yiliang,et al.Construction and implementation of ecological model for conceptual design of shearer[J].Journal of China Coal Society,2018,43(7):2068-2076.
[9] ZHANG Dongmin,HU Dachao,XU Yuchun,et al. A framework for design knowledge management and reuse for Product-Service Systems in construction machinery industry[J]. Computers in Industry,2012,63(4):328-337.
[10]朱琦琦,江平宇,张朋,等.数控加工装备的产品服务系统配置与运行体系结构研究[J].计算机集成制造系统,2009,15(6):1140-1147.ZHU Qiqi,JIANG Pingyu,ZHANG Peng,et al. Configuration and operation architecture for product service systems of CNC machine tools[J].Computer Integrated Manufacturing Systems,2009,15(6):1140-1147.
[11] MANNWEILER C,AURICH J C. Functional Thinking for Value Creation[M].Berlin,Heidelberg:Springer,2011:81-86.
[12] SHENG Zhongqi,LIU Changsai,SONG Junyou,et al.Module division and configuration modeling of CNC product-service system[J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science 2017; 231:494-506.
[13] LONG H J,WANG L Y,SHEN J,et al.Product service system configuration based on support vector machine considering customer perception[J].International Journal of Production Research,2013,51(18):5450-5468.
[14]沈瑾,王丽亚,隆惠君,等.服务型制造中基于知识的产品服务配置[J].计算机集成制造系统,2013,19(10):2633-2643.SHEN Jin,WANG Liya,LONG Huijun,et al. Knowledge-based product service configuration in servitization[J].Computer Integrated Manufacturing Systems,2013,19(10):2633-2643.
[15]耿秀丽.顾客价值驱动的产品服务系统方案设计建模与决策技术研究[D].上海:上海交通大学,2012:29-31.GENG Xiuli.Research on modeling and decision making technology of product service system conceptual design driven by customer value[D].Shanghai:Shanghai Jiao Tong University,2012:29-31.
[16] KANG H,KANG M,YOON S,et al. A consumer value analysis of mobile internet protocol television based on a means-end chain theory[J].Service Business,2014,8(4):587-613.
[17]谢友柏.现代设计理论中的若干基本概念[J].机械工程学报,2007,43(11):7-16.XIE Youbai.Some basic concepts in modern design theory[J].Journal of Mechanical Engineering,2007,43(11):7-16.
[18]陈昊,李兵,刘常昱.一种无确定度的逆向云算法[J].小型微型计算机系统,2015,36(3):544-549.CHEN Hao,LI Bing,LIU Changyu.An algorithm of backward cloud without certainty degree[J].Journal of Chinese Computer Systems,2015,36(3):544-549.
[19] NAHM YE,ISHIKAWA H,INOUE M.New rating methods to prioritize customer requirements in QFD with incomplete customer preferences[J].International Journal of Advanced Manufacturing Technology,2013,65(9-12):1587-1604.
[20]丁华,常琦,杨兆建,等.基于极限学习机的采煤机功率预测算法研究[J].煤炭学报,2016,41(3):794-800.DING Hua,CHANG Qi,YANG Zhaojian,et al.Research on the algorithm of shearer power prediction based on extreme learning machine[J].Journal of China Coal Society,2016,41(3):794-800.