Aktepe, AdnanErsoz, SuleymanLuy, Murat2020-06-252020-06-252012closedAccess978-3-642-32908-11865-0929https://hdl.handle.net/20.500.12587/533113th International Conference on Engineering Applications of Neural Networks -- SEP 20-23, 2012 -- Coventry Univ, Otaniemi, FINLANDLUY, Murat/0000-0002-2378-0009; Aktepe, Adnan/0000-0002-3340-244XThe aim of this study is to develop predictive Artificial Neural Network (ANN) models for welding process control of a strategic product (155 mm. artillery ammunition) in armed forces' inventories. The critical process about the production of product is the welding process. In this process, a rotating band is welded to the body of ammunition. This is a multi-input, multi-output process. In order to tackle problems in the welding process 2 different ANN models have been developed in this study. Model 1 is a Backpropagation Neural Network (BPNN) application used for classification of defective and defect-free products. Model 2 is a reverse BPNN application used for predicting input parameters given output values. In addition, with the help of models developed mean values of best values of some input parameters are found for a defect-free weld operation.eninfo:eu-repo/semantics/closedAccessBackpropagation neural networkswelding process controlartillery ammunitionConference Object311172+2-s2.0-84880640837Q3WOS:000312463700018N/A