Design Histories for Enhanced Concurrent Structural Design

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:


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.