Abstract
The major challenge for contemporary enterprises is to make products fulfill customers’ expectations. Conceptual design is the foundation of the development process, as it starts from the customer needs identification and decides about the whole product life cycle. Quality function deployment (QFD) helps to extract product characteristics from customer demands. Optimization of the product development process requires different product variant information at the early stage of product development. At the early designing stage, designers lack sufficient product information and have difficulty in determining it. The idea of the paper is to provide measurable engineering information for the quality function deployment method. For this purpose, a chosen artificial intelligence method was used. In the experiment, artificial neural network (NN) was applied. The results of analyses show that the intelligent estimation methods are useful and effective. The methods of estimation consist of four stages: goal setting, data acquisition, configuration of NN architecture, fulfilling of the QFD matrix. Finally, to illustrate the procedure of the chosen engineering characteristic estimation, a toothed gear box example was used.
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Akao, Y. (1997). QFD: Past, present, and future international symposium on QFD. ’97 Linköping.
Baxter D., Gao J., Case K., Harding J., Young B., Cochrane S., Dani S. (2007) An engineering design knowledge reuse methodology using process modeling. Research in Engineering Design 18: 37–48
Chou Y. (2004) Applying neural networks in quality function deployment process for conceptual design. Journal of the Chinese Institute of Industrial Engineers 21(6): 587–596
Dagli C., Kusiak A. (1994) Intelligent Systems in design and manufacturing. Asme Press, New York
Esche S. K., Chassapis C., Manoochehri S. (2001) Concurrent product and process design in hot forging. Concurrent Engineering 9(1): 48–54
Fung R., Tang J., Yiliu Tu P., Chen Y. (2003) Modelling of quality function deployment planning with resource allocation. Research in Engineering Design 14: 247–255
Haouani M., Lefebvre D., Zerhouni N., Moundni A. (2000) Neural network implementation for modelling and control design of manufacturing system. Journal of Intelligent Manufacturing 11: 29–40
Hernandez-Matias J. C., Vizan A., Hidalgo A., Rios J. (2006) Evaluation of techniques for manufacturing process analysis. Journal of Intelligent Manufacturing 17: 571–583
Iranmanesh H., Thomson V. (2008) Competitive advantage by adjusting design characteristics to satisfy cost targets. International Journal of Production Economics 115: 64–71
Karsak E. E., Sozer S., Alptekin S. E. (2003) Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers & Industrial Engineering 44: 171–190
Kingsly D., Jebaraj C. (2008) Feature-based design for process planning of the forging process. International Journal of Production Research 46(3): 675–701
Kulon J., Mynors D. J. (2006) A knowledge-based engineering design tool for metal forging. Journal of Materials Processing Technology 177(1–3): 331–335
Kuo C.-F.J , Wu Y.-S. (2006) Application of a Taguchi-based neural network prediction design of the film coating process for polymer blends. The International Journal of Advanced Manufacturing Technology 27: 455–461
Lee I.-H., Cha J.-H., Park M.-W. (2003) An integrated inference architecture for machine tools design involving complex knowledge. The International Journal of Advanced Manufacturing Technology 22: 321–328
Lin M. C., Chen L. A., Chen M. S. (2009) An integrated component design approach to the development of a design information system for customer-oriented product design. Advanced Engineering Informatics 23: 210–221
Liu T.-C., Li R.-K., Chen M.-C. (2006) Development of an artificial neural network to predict lead frame dimensions in an etching process. The International Journal of Advanced Manufacturing Technology 27: 1211–1216
Mazur G. (1994) QFD for small business The Sixth Symposium on Quality Function Deployment. Novi, Michigan
Meler-Kapcia M., Zielinski S., Kowalski Z. (2005) On application of some artificial intelligence methods in ship design. Polish Maritime Research 1: 14–20
Natarajan U., Periasamy V. M., Saravanan R. (2007) Application of particle swarm optimisation in artificial neural network for the prediction of tool life. The International Journal of Advanced Manufacturing Technology 31: 871–876
Ojha D. K., Dixit U. S. (2005) An economic and reliable tool life estimation procedure for turning. The International Journal of Advanced Manufacturing Technology 26: 726–732
Pilot T., Knosla R. (1998) The application of neural network in group technology. Journal of Materials Processing Technology 78: 150–155
Poel I. (2007) Methodological problems in QFD and directions for future development. Research in Engineering Design 18: 21–36
Pons D. J., Raine J. K. (2005) Design mechanisms and constraints. Research in Engineering Design 16: 73–85
Rao S., Nahm A., Shi Z., Deng X., Syamil A. (1999) Artificial intelligence and expert systems applications in new product development—A survey. Journal of Intelligent Manufacturing 10: 231–244
Sonar D. K., Dixit U. S., Ojha D. K. (2006) The application of a radial basis function neural network for predicting the surface roughness in a turning process. The International Journal of Advanced Manufacturing Technology 27: 661–666
Sukthomya W., Tannock J. (2005) The optimisation of neural network parameters using Tagchi’s design of experimental approach—An application in manufacturing process modelling. Neural Computing and Applications 14(4): 337–344
Thibault A., Siadat A., Sadeghi M., Bigot R., Martin P. (2009) Knowledge formalization for product-process integration applied to forging domain. The International Journal of Advanced Manufacturing Technology 44: 1116–1132
Tsai J. P., Kao Y.-C., Lee R. S. (2002) Development of a remote collaborative forging engineering system. The International Journal of Advanced Manufacturing Technology 19(11): 812–820
Valasek, M., & Zdrahal, Z. (2000). Knowledge models in engineering design. European Conference on Artificial Intelligence 2000. Workshop on Knowledge Modelling in Engineering, Berlin.
Wang Q., Rao M., Zhou J. (1994) Intelligent systems for conceptual design of mechanical products. In: Mital A., Anand S. (eds) Handbook of Expert Systems Applications in Manufacturing: Structures and Rules. Chapman & Hall, New York
Xu D., Yan H.-S. (2006) An intelligent estimation method for product design time. The International Journal of Advanced Manufacturing Technology 30: 601–613
Yang, D. Y., Ahn, D. G., & Lee, C. H. (2002). Integration of CAD/CAM/CAE/ RP for the development of metal forming process. Journal of Materials Processing Technology, 125–126, 26–34.
Zhang X., Peng Y., Ruan X. (2004) A web-based cold forging process generation system. Journal of Materials Processing Technology 145(1): 1–6
Zheng J., Wang Q., Zhao P., Wu C. (2009) Optimization of high-pressure die-casting process parameters using artificial neural network. The International Journal of Advanced Manufacturing Technology 44: 667–674
Zhongtu L., Qifu W., Liping C. (2006) A knowledge-based approach for the task implementation in mechanical product design. The International Journal of Advanced Manufacturing Technology 29: 837–845
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Kutschenreiter-Praszkiewicz, I. Application of neural network in QFD matrix. J Intell Manuf 24, 397–404 (2013). https://doi.org/10.1007/s10845-011-0604-7
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DOI: https://doi.org/10.1007/s10845-011-0604-7