Abstract
Integration of the measurement activity into the production process is an essential rule in digital enterprise technology, especially for large volume product manufacturing, such as aerospace, shipbuilding, power generation and automotive industries. Measurement resource planning is a structured method of selecting and deploying necessary measurement resources to implement quality aims of product development. In this research, a new mapping approach for measurement resource planning is proposed. Firstly, quality aims are identified in the form of a number of specifications and engineering requirements of one quality characteristics (QCs) at a specific stage of product life cycle, and also measurement systems are classified according to the attribute of QCs. Secondly, a matrix mapping approach for measurement resource planning is outlined together with an optimization algorithm for combination between quality aims and measurement systems. Finally, the proposed methodology has been studied in shipbuilding to solve the problem of measurement resource planning, by which the measurement resources are deployed to satisfy all the quality aims.
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References
Bobrek, M., Sokovic, M.: Implementation of APQP-concept in design of QMS. Journal of Materials Processing Technology 162-163, 718–724 (2005)
Bergman, B., Klefsjo, B.: Quality from Customer Needs to Customer Satisfaction, 1st edn., p. 156. McGraw-Hill, New York (1994)
Cai, B., Guo, Y., Jamshidi, J., Maropoulos, P.G.: Large Volume measurability analysis. In: Proceeding of the Fifth International Conference on Digital enterprise techniques (DET 2008), Nantes, France, October 22-24 (2008)
Chin, K.-S., Zheng, L.-Y., Wei, L.: A hybrid rough-cut process planning for quality. International Journal of Advanced Manufacturing Technology 22(9-10), 733–743 (2003)
Dai, W., Tang, X.Q.: Quality plan model for product development. In: Proceedings of the 38th International Conference on Computers and Industrial Engineering, Beijing, China, vol. 2, pp. 1535–1541 (2008)
Garvin, D.A.: What Does ‘Product Quality’ Mean? Sloan Management Review 26(1), 25–43 (1984)
He, Y.H., Tang, X.Q.: Design for quality based on product key quality characteristics. Acta Aeronautica ET Astronautica Sinica 28(6), 1468–1481 (2007)
Juran, J.M.: Juran’s Quality Handbook. McGraw Hill, New York (1990)
Kahraman, C., Ertay, T., Buyukozkan, G.: A fuzzy optimization model for QFD planning process using analytic network approach. European Journal of Operational Research 171(2), 390–411 (2006)
Labodova, A.: Implementing integrated management systems using a risk analysis based approach. Journal of Cleaner Production 12(6), 571–580 (2004)
Li, W.D., McMahon, C.A.: A simulated annealing-based optimization approach for integrated process planning and scheduling. International Journal of Computer Integrated Manufacturing 20(1), 80–95 (2007)
MIL-P-1629A, Procedures for Performing a Failure Mode, Effects and Criticality Analysis Department of Defense (1980)
Maropoulos, P., Zhang, D., Rolt, S., Chapman, P., Rogers, B.: Integration of measurement planning with aggregate product modelling for spacecraft design and assembly. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 220(10), 1687–1695 (2006)
Mohib, A., Azab, A., ElMaraghy, H.: Feature-based hybrid inspection planning: A mathematical programming approach. International Journal of Computer Integrated Manufacturing 22(1), 13–29 (2009)
Raharjo, H., Xie, M., Brombacher, A.C.: Prioritizing quality characteristics in dynamic quality function deployment. International Journal of Production Research 44(23), 5005–5018 (2006)
Singhal, K., Singhal, J., Starr, M.K.: The domain of production and operations management and the role of Elwood Buffa in its delineation. Journal of Operations Management 25(2), 310–327 (2007)
Tang, X.Q., Wang, M.Q., Chen, J., Zhang, S.: Research on Methodology of Quality Planning in Product Development. In: Proceedings of the 30th International Conference of Computers & Industrial Engineering, Tinos Island, Greece, vol. 2, pp. 881–886 (2002)
Tang, X.Q., Hu, Y.: Data model for quality in product lifecycle. Computers in Industry 59, 167–179 (2008)
Thornton, A.C.: A Mathematical Framework for the Key Characteristic Process. Research in Engineering Design 11(3), 145–157 (1999)
Wang, M.Q., Tang, X.Q.: Mapping Customer Requirements to Product Quality Characteristics. Chinese Journal of Mechanical Engineering 40(5), 136–140 (2004)
Xu, H.-M., Li, D.-B.: A clustering-based modeling scheme of the manufacturing resources for process planning. The International Journal of Advanced Manufacturing Technology 38(1), 154–162 (2008)
Zhou, M., Zhao, C.: An optimization model and multiple matching heuristics for quality planning in manufacturing systems. Computers & Industrial Engineering 42(1), 91–101 (2002)
Zheng, L.Y., McMahon, C.A., Li, L., Ding, L., Jamshidi, J.: Key characteristics management in product lifecycle management: a survey of methodologies and practices. Proceedings of the Institution of Mechanical Engineers, Part B: Engineering Manufacture 222(8), 989–1008 (2008)
Zheng, L.Y., McMahon, C.A., Maropoulos, P.G., Wei, L., Ding, L., Jamshidi, J.: Key characteristics driven rough-cut process planning. In: Proceeding of the Fourth International Conference on Digital enterprise techniques (DET 2007), Bath, UK, September 19–21, pp. 269–279 (2007)
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Dai, W., Maropoulos, P.G., Tang, X., Jamshidi, J., Cai, B. (2010). Measurement Resource Planning: A Methodology That Uses Quality Characteristics Mapping. In: Huang, G.Q., Mak, K.L., Maropoulos, P.G. (eds) Proceedings of the 6th CIRP-Sponsored International Conference on Digital Enterprise Technology. Advances in Intelligent and Soft Computing, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10430-5_77
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DOI: https://doi.org/10.1007/978-3-642-10430-5_77
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