Prediction of packaging life-cycle design performance
Originally published in the Proceedings of the 2005 Northeast Decision Sciences Institute Conference, Philadelphia, Pennsylvania, March 30-April 1, 2005 (CD-ROM)
We develop a back-propagation neural network (BPN) to predict the life-cycle design performance for transport packaging. The BPN is constructed and trained on the packaging design attributes to detect hidden relationships among historical or pre-existing life-cycle design data to predict a new concept design through supervised learning, by minimizing the squared difference between the actual and the predicted life-cycle design performance. To this end, the designer could use the predicted life-cycle design in a trade-off analysis and concept selection for a potential packaging design. A case example is used to illustrate the methodology.
Surendra M. Gupta, Lerpong Jarupan, and Sagar V. Kamarthi. "Prediction of packaging life-cycle design performance" (2005).
Available at: http://works.bepress.com/skamarthi/6