Dynamic quality characteristics modelling based on brittleness theory in complex manufacturing processes

To comprehensively and quantitatively analyse the influencing factors of dynamic quality characteristics in complex manufacturing processes, depending on the brittleness analysis of dynamic quality characteristics based on man–machine–environment influencing factors, this paper established a man–machine–environment brittleness model of dynamic quality characteristics based on the brittleness theory of complex system. According to the brittleness relation among the brittleness factors of dynamic quality characteristics (BFDQCs) in this model, a key man–machine–environment influencing factor identification method of dynamic quality characteristics was proposed by the definition of brittle risk degree, brittle coupling degree and brittle degree among BFDQCs. Finally, a case study for applicability was presented. The result showed that the proposed method was available, and can provide guidance for the analysis of dynamic quality characteristics and support for product reliability in complex manufacturing processes.

[1]  Junying Hui,et al.  Study on pattern time delay shift coding scheme , 2006 .

[2]  Zhang Shu-hui.  Key quality characteristics extraction model of complicated mechanical and electrical products , 2010 .

[3]  Wei Qi,et al.  Brittleness research on complex system based on brittle link entropy , 2006 .

[4]  Lee-Ing Tong,et al.  Process quality improvement of a qualitative response with dynamic characteristic , 2005 .

[5]  Hoda A. ElMaraghy,et al.  Quality prediction for reconfigurable manufacturing systems via human error modelling , 2008, Int. J. Comput. Integr. Manuf..

[6]  Lifeng Xi,et al.  Integration of product quality and tool degradation for reliability modelling and analysis of multi-station manufacturing systems , 2009, Int. J. Comput. Integr. Manuf..

[7]  William Swart,et al.  A methodology for assuring the quality of human performance , 2005, Int. J. Comput. Integr. Manuf..

[8]  Wang Yan.  Study of supply chain’s brittleness based on the complex system brittle theory , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[9]  Paulo Rogério Politano,et al.  On-line scheduling for flexible manufacturing systems based on fuzzy logic , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  Qi Wei,et al.  Brittleness Theory and its Application on the Analysis of SARS , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[11]  Ful-Chiang Wu.  Sequential optimization of parameter and tolerance design for multiple dynamic quality characteristics , 2007 .

[12]  G. Kannan,et al.  Implementation of Fuzzy Quality Function Deployment in an Automobile Component to Improve the Quality Characteristics , 2008 .

[13]  Paul B. Lochert,et al.  A fuzzy logic approach for dealing with qualitative quality characteristics of a process , 2008, Expert Syst. Appl..

[14]  Hsu-Hwa Chang,et al.  A data mining approach to dynamic multiple responses in Taguchi experimental design , 2008, Expert Syst. Appl..

[15]  Liya Wang,et al.  A methodology based on Fuzzy Extended Quality Function Deployment for determining optimal engineering characteristics in product-service system design , 2008, 2008 IEEE International Conference on Service Operations and Logistics, and Informatics.

[17]  Shimon Y. Nof,et al.  Springer Handbook of Automation , 2009, Handbook of Automation.

[18]  Ma Lihua,et al.  Comprehensive evaluation of fully mechanized mining face security based on unascertained - Information entropy model , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[19]  Seyed Taghi Akhavan Niaki,et al.  Designing a multivariate–multistage quality control system using artificial neural networks , 2009 .