Design Histories for Enhanced Concurrent Structural Design

Authors: Adam Sobey, James Blake, Ajit Shenoi

Abstract:

The leisure boatbuilding industry has tight profit margins that demand that boats are created to a high quality but with low cost. This requirement means reduced design times combined with increased use of design for production can lead to large benefits. The evolutionary nature of the boatbuilding industry can lead to a large usage of previous vessels in new designs. With the increase in automated tools for concurrent engineering within structural design it is important that these tools can reuse this information while subsequently feeding this to designers. The ability to accurately gather this materials and parts data is also a key component to these tools. This paper therefore aims to develop an architecture made up of neural networks and databases to feed information effectively to the designers based on previous design experience.

Keywords:

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1054829

References:

[1] H.A. HAGHIAC and I. HAQUE. Quality function deployment as a tool for including customer preferences in optimising vehicle dynamic behaviour. International Journal of Vehicle Design, vol. 39(4):pp. 311-330, 2005.
[2] A.J. SOBEY, J.I.R. BLAKE, and R.A. SHENOI. Optimization of composite boat hull structures. In Computer and Information Management Applications for Shipbuilding (COMPIT),Liege, pages pp.502-515, 2008a.
[3] A.J. SOBEY, J.I.R. BLAKE, and R.A. SHENOI. Optimisation of composite boat hull structures as part of a concurrent engineering environment. In High Performance Marine Vehicles, Naples, pages pp.133-146, 2008b.
[4] A.J. SOBEY, J.I.R. BLAKE, and R.A. SHENOI. Optimisation approaches to design synthesis of marine composite structures. Schiffstechnik - Ship Technology Research, page Accepted for publication, 2009.
[5] G. BENNET and T. LAMB. Concurrent engineering: Application and implementation for shipbuilding. Journal of Ship Production, vol. 12(2):pp.107-125, 1996.
[6] M.A. EAGLESHAM. A Decision Support System for Advanced Composites Manufacturing Cost Estimation. PhD thesis, Virginia Polytechnic Institute and State University, 1998.
[7] K.G. SWIFT and N.J. BROWN. Implementation strategies for design for manufacture methodologies. Proc. Instn Mech. Engrs Part B: J. Engineering Manufacture, vol. 217, 2003.
[8] R. HAAS and M. SINHA. Concurrent engineering at airbus: A case study. International Journal of Manufacturing Technology and Management, vol. 6(3/4):pp.241 - 253, 2004.
[9] R. SHISHKO. The proliferation of pdc-type environments in industry and universities. Proceedings of the 2nd EUSEC, Munich, 2000.
[10] S. FINKEL, M. WILKE, H. METZGER, and M. WAHNFRIED. Design centers - transferring experience from astronautics to aeronautics. Proceedings of the 12th Annual Symposium of INCOSE International Council on Systems Engineering, Las Vegas, 2002.
[11] M. BANDECCHI, B. MELTON, B. GARDINI, and F. ONGARO. The esa/estec concurrent design facility. Proceeding of EuSec, 2000.
[12] K.J. CLEETUS. Concurrent engineering definition. Technical report, CERC Technical Report, ERC-TR-RN-92-016, 1992.
[13] H. BAI and C.K. KWONG. Inexact genetic algorithm approach to target values setting of engineering requirements in qfd. International Journal of Production Research, vol. 41:pp. 3861-3881, 2003.
[14] KPMG LLP. Sector competitiveness analysis of the uk leisure boatbuilding industry. Technical report, KPMG, 2006.
[15] W.S. MCCULLOCH and W. PITTS. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, vol. 5:pp. 115-133, 1943.
[16] F. ROSENBLATT. Principles of Neurodynamics. New York: Spartan, 1943.
[17] P.J. WERBOS. Beyond Regression: New tools for Prediction and Analysis in the behavioral Sciences. PhD thesis, Harvard University, 1974.
[18] D.E. RUMELHART, G.E. HINTON, and R.J. WILLIAMS. Learning representations by back-propagating errors. Nature, vol. 323:pp. 533-536, 1986a.
[19] D.E. RUMELHART, G.E. HINTON, and R.J. WILLIAMS. Learning internal representations by error propagation. Parallel Distributed Processing, vol. 1, 1986b.
[20] D.B. PARKER. Learning logic. Technical report, Technical Report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA, 1992.
[21] J.R. HAUSER and D. CLAUSING. The house of quality. Harvard Business Review, vol. 32(5):pp. 63-73, 1988.
[22] G. CYBENKO. Continuous valued neural networks with two hidden layers are sufficient. Technical report, Technical Report, Department of Computer Science, Tufts University, Medford, MA, 1988.
[23] K. HORNIK, M. STINCHCOMBE, and H. WHITE. Mul- tilayer feedforward networks are universal approximators. Neural Networks, vol. 2:pp. 359-366, 1989.
[24] T. OKADA and I. NEKI. Utilization of genetic algorithms for optimizing the design of ship hull structure. Journal of the Society of Naval Architects of Japan, vol. 171:pp. 71-83, 1992.
[25] H. NOBUKAWA and G. ZHOU. Discrete optimization of ship structures with genetic algorithm. Journal of the Society of Naval Architects of Japan, vol. 179:pp. 293-301, 1996.
[26] Z. SEKULSKI and T. JASTRZEBSKI. Optimisation of the fast craft deck structure by genetic algorithms. Marine Technology Transactions, vol. 9:pp. 165-188, 1998.
[27] Z. SEKULSKI and T. JASTRZEBSKI. Optimisation of the fast craft structure by genetic algorithm. In: T.Graczyk, T.Jastrzebski C.A.Brebbia (Editors) Third International Conference on Marine Technology ODRA -99, pages pp. 51-60, 1999a.
[28] Z. SEKULSKI and T. JASTRZEBSKI. 3d optimisation problem of the ship boat hull structure by the genetic algorithm. Marine Technology Transactions, vol. 10:pp. 247-264, 1999b.
[29] K. MANEEPAN. Genetic Algorithm based Optimisation of FRP Composite Plates in Ship Structures. PhD thesis, University of Southampton, 2007.