Die Friction Coefficient Element Type Value Al2024-T3 Tool Steel / Neural Networks / DYNA Users Conference ABSTRACT Manufacturing / Artificial Neural Networks / HP / Microsoft / developed using Artificial Neural Networks (ANNs) / / /
Facility
Manufacturing Engineering Department Wichita State University / In building / /
IndustryTerm
sheet metal / real time solution strategy / real time transmission / feed-forward networks / metal sheet / stop-training algorithm / manufacturing end / networks / back-propagation algorithm / closed form solutions / manufacturing optimization / sheet metal forming press / responsive technology / manufacturing engineers / manufacturing process simulation / manufacturing process simulations / real time prediction / standalone software / manufacturing processes / forward networks / manufacturing process / manufacturing knowledge / feedback devices / parallel processing multi-processor system / manufacturing components / real time predictions / manufacturing process analyses / sheet metal component / noncommercial applications / back propagation algorithm / display device / feed-forward recurrent backpropagation networks / stress network / connected feed forward network / obvious solution / springback network / optimization tool / Virtual reality systems / hardware / manufacturing / public networks / feed-forward recurrent back-propagation networks / manufacturing simulation / modeled using simple feed-forward networks / /
OperatingSystem
HP-UX / /
Organization
Institute for Information Processing and Computer Supported New Media / Institute for Information Processing / Wichita State University / Computer Supported New Media / /
Position
translator / direct translator / Finite Element Model of Channel Forming / head / Cosmo player / Feed-forward / VRML translator / and the Artificial Neural Network module / simple feed-forward and feed-forward / analyst / /
Product
Java 1.2 / Opengl / developed using Java 1.2.x / /
th 4 European LS-DYNA Users Conference Metal Forming III