Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process |
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| Affiliation: | 1. Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India;2. Department of Mechatronics Engineering, Manipal Institute of Technology, India;3. Department of Mechanical Engineering, Sanjeevan Engineering and Technology Institute, Kolhapur, India;1. Department of Mechanical Engineering, Delhi Technological University, Delhi, 110042, India;2. Management Development Institute, Gurgaon, Haryana, 122007, India;3. Department of Production and Industrial Engineering, Delhi Technological University, Delhi, 110042, India;1. Laboratoire de Methodes de Conception de Systemes (LMCS), Ecole nationale Superieure dInformatique (ESI), BP 68M, Oued Smar, 16000 Alger, Algeria;2. Laboratoire LSIS, Domaine Universitaire de Saint-Jerome, Univesite Aix-Marseille Batiment Polytech, Avenue Escadrille Normandie-Niemen, 13397 Marseille Cedex 20, France;1. TU/e Department of Mechanical Engineering, University of Technology Eindhoven, De Zaale, 5612AZ Eindhoven, the Netherlands;2. Structural Integrity Group, Universidad de Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain;1. Homi Bhabha National Institute, Anushaktinagar, Mumbai 400094, India;2. Reactor Safety Division, Bhabha Atomic Research Centre, Mumbai 400085, India;1. Homi Bhabha National Institute, Anushakti Nagar, Mumbai 400094, India;2. Reactor Safety Division, Bhabha Atomic Research Centre, Trombay, Mumbai 400085, India |
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| Abstract: | Today, in competitive manufacturing environment reducing casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in squeeze casting process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of casting variables (that is, squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of casting variables for the required casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and précised control of squeeze casting process. |
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| Keywords: | Squeeze casting process Genetic algorithm neural network (GA-NN) Back-propagation neural network (BPNN) Recurrent neural network (RNN) and forward and reverse mapping |
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