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Automation of Cushion Design Curves Using a Neural Network S. Malasri 1, Y. Zhou 2, J. Malasri 1, and A. Ray 1 1 Christian Brothers University 2 FedEx Express Presented at MAESC 2006 Conference – 3/31/06 © 2006 by S. Malasri
Packaging @ CBU Packaging Engineering Certificate Packaging Seminars for Professionals Activities for Pre- college Students Packaging R&D Projects www.cbu.edu/engineering/packaging
Cushioned Package Development
Cushion Design Curves
Using a Curve
Artificial Neural Networks Ability to learn Recognize patterns, including design curves
Previous Work Automation of stress concentration design curve
Current Work Automation of cushion design curves
Prototype Polyethylene foam 1.7 pcf density Average of Drops of 2-5” Heights: 12”, 18”, 24”, 30”, 36”, and 42” Based on the design curves from Sealed Air Corporation
Cushion Curve NN
Training Data Input Output 60 cases were used for training 5 other cases were used for validation
Training NeuroShell 2 software Backpropagation network 2 input cells, 10 hidden cells, 3 output cells Learning rate = 0.1 Momentum coefficient = 0.1
Validation 75% were within 5% error 17% were between 5-10% error 7% were between 10-20% error 1% were between 20-30% error 92% were within 10% error
Future Work Fine turning the network to improve its performance Networks for other cushion densities Based on various networks, develop a computer program to come up with an optimal design
Automation of Engineering Design Aids using Neural Networks Siripong Malasri and Jittapong Malasri Christian Brothers University Kriangsiri Malasri Georgia.
Appendix B: An Example of Back-propagation algorithm
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