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OPTIMIZATION OF ARTIFICIAL NEURAL NETWORK MODEL FOR IMPROVEMENT OF ARTIFICIAL INTELLIGENCE OF MANUALLY DRIVEN BRICK MAKING MACHINE POWERED BY HPFM

OPTIMIZATION OF ARTIFICIAL NEURAL NETWORK MODEL FOR IMPROVEMENT OF ARTIFICIAL INTELLIGENCE OF MANUALLY DRIVEN BRICK MAKING MACHINE POWERED BY HPFM 

P. A. Chandak1 and J. P. Modak2 1Department of Mechanical Engineering, DMIETR, Wardha (MH), India 2 Professor Emeritus, Dean (R&D), PCE, Nagpur(MH), India 

ABSTRACT

 Considerable development has been done by some authors of this paper towards development of manufacturing process units energized by Human Powered Flywheel Motor (HPFM) as an energy source. This machine system comprises three sub systems namely (i) HPFM (ii) Torsionally Flexible Clutch (TFC) (iii) A Process Unit. Process unit so far tried are mostly rural based such as brick making machine (both rectangular and keyed cross sectioned), Low head water lifting, Wood turning, Wood strips cutting, etc HPFM comprises pedalling system similar to bicycle, a speed rising gear pair and a flywheel big enough such that a young lad of 21-25 years, 165cm height, slim structure can pump energy around 30,000 N-m in minutes time. Once such an energy is stored peddling is stopped and a special type of TFC is engaged which very efficiently brings about momentum and energy transfer from flywheel to a process unit. Process unit utilization time upon clutch engagement is 5 to 15 seconds depending on the application. In other word process units needing power of order of 3 hp to 10 hp can be powered by such a machine concept. Experimental data base model is formulated for Human Powered Flywheel Motor Energized Brick Making Machine (HPFMEBMM). The focus of the present paper is on development of an optimum Artificial Neural Network (ANN) model which will predict the experimental evidences accurately and precisely. The optimisation is acknowledged though variation of various parameters of ANN topology like training algorithm, learning algorithm, size of hidden layer, number of hidden layers, etc while training the network and accepting the best value of that parameter. The paper also discusses the effects and results of variation of various parameters on prediction of network. 

KEYWORDS

 ANN, Matlab, Manually driven brick making machine. Simulation




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