Rajesh Kumar Porwal, Department of Mechanical Engineering, Motilal Nehru National. Institute of Technology, Allahabad, India. Vinod Yadava, Department of ...
International Journal of Manufacturing, Materials, and Mechanical Engineering, 4(1), 1-21, January-March 2014 1
Neural Network Based Modelling and GRA Coupled PCA Optimization of Hole Sinking Electro Discharge Micromachining Rajesh Kumar Porwal, Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, India Vinod Yadava, Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, India J. Ramkumar, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India
ABSTRACT Determination of material removal rate (MRR), tool wear rate (TWR) and hole taper (Ta) is a challenging task for manufacturing engineers from the productivity and accuracy point of view of the symmetrical and nonsymmetrical holes due to hole sinking electro discharge micro machining (HS-EDMM) process. Thus, mathematical models for quick prediction of these aspects are needed because experimental determinations of process performances are always tedious and time consuming. Not only prediction but determination of optimum parameter for optimization of process performance is also required. This paper attempts to apply a hybrid mathematical approach comprising of Back Propagation Neural Network (BPNN) for prediction and Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) for optimization with multiple responses of HS-EDMM of Invar-36. Experiments were conducted to generate dataset for training and testing of the network where input parameters consist of gap voltage, capacitance of capacitor and the resulting performance parameters MRR, TWR and Ta. The results indicate that the hybrid approach is capable to predict process output and optimize process performance with reasonable accuracy under varied operating conditions of HS-EDMM. The proposed approach would be extendable to other configurations of EDMM processes for different material. Keywords:
Artificial Neural Network (ANN), Grey Relational Analysis (GRA), Hole Sinking Electro Discharge Micromachining (HS-EDMM), Modelling, Optimization, Principal Component Analysis (PCA), Taguchi Methodology
DOI: 10.4018/ijmmme.2014010101 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
2 International Journal of Manufacturing, Materials, and Mechanical Engineering, 4(1), 1-21, January-March 2014
INTRODUCTION In recent years, there is an increasing trend towards miniaturization of various engineering components. In view of this, micromachining techniques have become important in the fabrication of micro components based on the different mechanism of material removal. These miniaturized components/ products having multi functional characteristics are largely employed in electronics, optics, automobile, biotechnology, and aeronautical industries. Based on the different forms of innovative energy utilizing in micromachining processes are classified into various categories like electro discharge micromachining (EDMM) using electrical discharge spark energy, beam micromachining processes (BMMPs) using beam of photon, electron or ions, electrochemical micromachining (ECMM) using controlled electrolysis, chemical micromachining processes (CMMPs) using selective chemical reaction, ultrasonic micromachining (USMM) using high frequency vibrational energy and jet micromachining processes (JMMPs) using energy of jet. Electro discharge micromachining (EDMM) is one of the successful micromachining processes to create micro features in difficult to machine electrically conductive materials using thermal energy of spark created between tool electrode and workpiece. The mechanism of material removal in EDMM is similar to that of conventional EDM but application at micro scale. The machine setup for EDMM consists of a servo control system with sensitivity and positional accuracy of ±0.05µm along with the inter electrode gap of 1-5 µm. The power supply used in EDMM is relaxation or transistor type pulse generator with MHz of pulsating frequency (Jain, 2010). The EDMM process has been successfully used for creating micro features in wide varieties of conducting materials having conductivity (>10-2 S/cm) such as high temperature alloys, cemented carbide, and ceramics. Based on the configuration of tool and workpiece as well as the type of feature that
can be created by EDMM, it can be classified as: Die Sinking-EDMM, Hole Sinking-EDMM, Die Drilling-EDMM, Hole Drilling-EDMM, Pocket Milling- EDMM, Wire-EDMM and grinding EDMM. Hole Sinking-EDMM which has been considered in the present paper is used for creating symmetrical or nonsymmetrical micro holes by providing sinking action to the tool electrode. The micro holes created by HSEDMM are used for manufacturing of grid and biomedical filters, injection nozzles, starting hole for wire EDM. In literature it has been found that in the last decade many researchers were mainly involved with the development of EDMM equipment consisting of power supply, dielectric supply and electrode feeding system. Most of the developed EDMM machines are based on transistor type isopulse or DC servo type power, 3-axis or 4-axis local actuator module and servo type feed (Wong et al., 2003; Wansheng et al., 2004; Fuzuh et al., 2004; Yosihito et al., 2004). Nickel based alloys are extensively used for manufacturing aerospace components because of their high strength to weight ratio which is maintained over a high temperature range. Approximately half of the total materials used for gas turbine engine and jet engines are nickel alloys, Ezugwu et al. (2003). A micro turbine with a complex design was reported in Peirs et al. (2002) where the components were fabricated by a combination of micro-EDM and mechanical machining. The gears and other mechanical components have been made by micro-EDM of hard alloys such as high-carbon tool steel and WC-Ni-Cr super-hard alloy. The fabrication precision in micro-EDM of the gear components was reported to be within 0.4%, with standard deviation of 0.127. Tsai and Masuzawa (2004) studied the electrode wears of electrode materials (Ag, Al, Cu, Fe, Mo, Ni, Pt, Ti, Ta and W) with workpiece materials such as SUS-304, Cu (99.99%) and Fe (99.95%) on micro-EDM. Their experimental investigation shows that the electrode wear is small for electrode material with high boiling point, melting point and thermal conductivity, and it is independent of workpiece materials.
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