PROJECT PHASE-I REVIEW

“PREDICTION OF METAL REMOVAL RATE AND SURFACE FINISH OF AN ELECTRO CHEMICAL MACHINED SURFACE USING

NEURAL NETWORK ”

By V.Suresh Guided by – Prof.S.AYYAPPAN.,M.E.,04/10/23 1

ECM is suitable for machining hard and extra hard materials, making dies and used in aeronautics and die industries. ECM is a very complex process as a result of electric, mechanical and chemical parameters.

So the analytical modeling of the process may not provide the exact relationship of parameters and responses.

The artificial neural network modeling simplifies the relationship between the input parameters responses. Hence the neural network modeling is proposed and trained with a set of data obtained experimentally from ECM.

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WORKING PRINCIPLE:

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Applied voltage between the tool (cathode) and work piece (anode) (V)

Tool feed rate (mm/min) Electrolyte discharge rate (l/min) Inlet temperature of electrolyte Inter Electrode Gap (IEG)

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ECM is known from the past as an environmental polluting process.

Each product and each material demands a new research.

High energy consumption .

The electrode design is complex and initially expensive,

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“On some aspects of surface formation in ECM” by V.G.K.Murthy, V.Radhakrishnan in Journal of Engineering for Industry,August 1981, Vol.103/1 by ASME

.“Modelling of abrasive flow machining process: a neural networkapproach” by R.K. Jain , V.K. Jain , P.K. Kalra in Journal of wear ,MARCH 1999.

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“On the machining of alumina and glass” by V.K.Jain, S.K.Choudhury, K.M.Ramesh in International Journal of Machine Tools and Manufacture 42(2002)1269-1276.

“Prediction of Surface roughness during abrasive flow machining” by V.K.Gorana, V.K.Jain, G.K.Lal in International Journal of Advance Manufacture Technology (2006)31: 258-267

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The base metal is chosen as Aluminium 6061.

The reinforcement is chosen as Aluminium oxide(Al2O3) .

With the base metal as Aluminium, the composite is to be fabricated with 10% volume of Aluminium oxide Particulates .

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Aircraft and aerospace components Marine fittings Transport  Bicycle frames  Camera lenses  Drive shafts  Electrical fittings and connectors

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Al2O3 particles are the most commonly used reinforcement materials in the discontinuously reinforced metal-matrix composite system.

Aluminum matrix composites reinforced with Al2O3 particulates provide for a low-cost, high-modulus material that can be processed via conventional powder metallurgy techniques.

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Stir Casting is a liquid state method of composite materials fabrication, in which a dispersed phase (ceramic particles, short fibers) is mixed with a molten matrix metal by means of mechanical stirring.

Al6061 was first heated to its melting point in graphite crucible.

Metal matrix Al2O3(10 vol%) was added to the molten metal

Stirring was carried out at constant rate of 350-450 rpm for 10 min.

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An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.

ANNs, like people, learn by example.

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A trained neural network can be thought of as an"expert" in the category of information it has been given to analyse.

Adaptive learning Self-Organisation Real Time Operation Fault Tolerance via Redundant Information Coding

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Feed-forward networks Feedback networks

Network layers It consists of three groups, or layers, of

units: a layer of "input" units is connected to a layer of "hidden" units, which is connected to a layer of "output" units

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Since neural networks are best at identifying patterns or trends in data, they are well suited for prediction or forecasting needs including:

Sales Forecasting Industrial Process Control Customer Research Data Validation Risk Management Target Marketing

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M R R

SURFACE ROUGHNESS

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Al/Al2O3 composite was fabricated through stir casting process.

Artificial Neural network modeling is to be learned to fit the relationship between process parameters and responses.

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In the phase-II project, ECM process parameters to be optimized using Genetic algorithm based Neural Network model in order to predict MRR and Surface roughness.

 

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1. Krishnaiah Chetty O V, Murthy R V G K , Radhakrishnan V(1981) , On Some Aspects of Surface Formation in ECM. Journal of Engineering for Industry.vol.103/1.

2. Mamilla Ravi Sankar, Ramkumar J , Jain V K , Experimental investigation and mechanism of material removal in nano finishing of mmcs using abrasive flow finishing (AFF) process. Wear 266(2009) z -698.

3.Jain R K ,. Jain V K, Kalra P K , Modelling Of Abrasive Flow Machining Process: A Neural Network Approach. Wear 231(1999) 242-248.

4. Jain V K, Dixit P M, Pandey P M, On the analysis of the electrochemical spark machining process. International Journal of Machine Tools & Manufacture 39 (1999) 165-186.

5. Jain N K, Jain V K, Jha S, Parametric optimization of advanced fine-finishing processes. Int J Adv Manu V. K. f Technol (2007) 34:1191-1213.

6. Gorana V K , Jain V K , Lal G K, Prediction of surface roughness during abrasive flow machining. Int J Adv Manuf Technol (2006) 31: 258-267

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THANK YOU

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