Avanish Kumar Dubey

Electrical discharge machining–based hybrid machining processes: A review

Electrical discharge machining is one of the widely used noncontact-type advanced machining processes in which material removal takes place due to melting and vaporization by thermal energy of electric sparks. Electrical discharge machining has the capability of machining difficult-to-cut materials such as superalloys, advanced ceramics, and composites with complex shapes at both macro- and micro-levels. But its application is limited to electrically conductive materials. Other limitations include low material removal rate, high tool wear rate, recast layer formation, and geometrical inaccuracy in the form of taper and overcut. To overcome such limitations, the mechanism of electrical discharge machining has been combined with the mechanism of one or more other machining/physical/chemical processes. The mechanism of two constituent processes may be applied simultaneously or sequentially to constitute the hybrid machining process. It has been found that the performance of hybrid machining processes is better than the constituent processes. This article presents the comprehensive review of the research work carried out so far in the area of electrical discharge machining–based hybrid machining processes. It discusses about the experimental and theoretical studies of electrical discharge machining–based hybrid machining processes to elucidate the effects of various control factors or input parameters on process performances. This article includes modeling and optimization studies and discusses the future trend of the research work in this area.

FUZZY EXPERT SYSTEM FOR PREDICTION OF KERF QUALITIES IN PULSED LASER CUTTING OF TITANIUM ALLOY SHEET

Laser cutting is an advanced thermal cutting process of complex nature. Its process behavior drastically changes with slight variation in processing conditions. The prediction of process performance becomes more difficult if cutting materials have non-favorable optical and thermal properties. Titanium (Ti) alloys are characterized by their low thermal conductivity and high chemical reactivity at elevated temperature and hence difficult to cut by laser. It has been found that complex and nonlinear behavior of manufacturing process can better be dealt with the application of artificial intelligence (AI) tools. The aim of present research is to develop a fuzzy expert system for prediction of the laser cutting process behavior of Ti alloy (Ti-6Al-4 V) sheet. A hybrid approach of neural network and fuzzy logic theory has been applied to develop the fuzzy expert system to predict the kerf widths and kerf deviation. The predicted results have been compared with the experimental data and found appropriate. The effects of significant process parameters on the different quality characteristics such as kerf widths and kerf deviation have been discussed.

Intelligent Modeling and Multiobjective Optimization of Electric Discharge Diamond Grinding

The grinding of metal matrix composites (MMCs) is very difficult by conventional techniques due to its improved mechanical properties. It often results in poor surface quality (surface damage) in the form of surface cracks/residual stresses and requires frequent truing and dressing due to clogging of the grinding wheel. The electric discharge diamond grinding (EDDG), a hybrid process of electric discharge machining and grinding may overcome these problems up to some extent. But low material removal rate (MRR) and high wheel wear rate (WWR) are the main problems in EDDG to achieve economic performance. The present paper investigates the EDDG process performance during grinding of copper-iron-graphite composite by modeling and simultaneous optimization of two important performance characteristics such as MRR and WWR. A hybrid approach of artificial neural network, genetic algorithm, and grey relational analysis has been proposed for multi-objective optimization. The verification results show considerable improvement in the performance of both quality characteristics.

Quality Improvement by Parameter Optimization in Laser Trepan Drilling of Superalloy Sheet

Drilling of small-diameter holes meeting stringent quality standards in superalloys such as Inconel718 (having widespread applications in aeroengine component manufacturing) has always been a challenging task. Laser drilling has wide applications in the aerospace industry. Laser trepan drilling (LTD) provides better control over the drilled hole geometry compared with laser percussion drilling to fulfill the higher dimensional accuracy requirement. This article presents an integrated approach of artificial neural network and genetic algorithm for the modeling and optimization of geometrical quality characteristics such as hole taper and circularity during LTD of 1.6 mm thick Inconel718 superalloy sheet. The optimum results show considerable improvements in hole taper, and hole circularities at laser beam entry and exit sides. Higher values of laser pulse frequency and trepanning speed in the present range have resulted in more circular holes with reduced taper.

Intelligent Modeling of Laser Cutting of Thin Sheet

needs a reliable model for prediction of the process performance. This research work presents a modeling study of laser cutting process. A hybrid approach of Artificial Neural Network (ANN) and Fuzzy Logic (FL), Adaptive Neuro Fuzzy Inference System (ANFIS) has been used for developing the Kerf width and Material removal rate (MRR) models. The developed ANFIS based models of Kerf width and Material removal rate have also been compared with Response Surface Methodology (RSM) based models and it has been found that the values of Kerf width and Material removal rate predicted by the ANFIS based models are more closer to the experimental values.

Artificial intelligence based modeling and optimization of heat affected zone in Nd:YAG laser cutting of duralumin sheet

Duralumin is an alloy of aluminium which has some unique properties such as high strength to weight ratio, high resistance to corrosion, light in weight, and more demanding alloy in various sectors such as space craft, marine, chemical industries, construction and automobile. These applications require very precise and complex shapes which may not be obtained with conventional machining. Pulsed Nd:YAG laser cutting may be used to fulfill these objectives by using optimum setting of process parameters. The present research paper has experimentally investigated the modeling and optimization of heat affected zone in the pulsed Nd:YAG laser cutting of Duralumin sheet with the aim to minimize heat affected zone. The quality is improved by the proper control of different process parameters such as gas pressure, pulse width, pulse frequency and scanning speed. Artificial intelligence (AI) algorithms have been used to solve the many engineering problems successfully through development of Genetic Algorithm (GA), Fuzzy Logic (FL) and Artificial Neural Network (ANN) systems. The optimization of heat affected zone has been carried out by using Hybrid Approach of Multiple Regression Analysis (MRA) and GA. In this methodology, the second order regression model has been developed by using MRA with the help of experimental data obtained by L27 orthogonal array (OA). Further this equation has been used as objective function in GA based optimization. The significant factors have been found with further discussion of their effect on the heat affected zone.

Neuro Fuzzy Modeling of Laser Beam Cutting Process

Laser Beam Cutting (LBC) being a complex cutting process needs a reliable model for prediction of the process performance. This research work presents a modeling study of LBC process. A hybrid approach of Artificial Neural Network (ANN) and Fuzzy Logic (FL) has been used for developing the Kerf width model. The developed Neuro Fuzzy model of Kerf width has also been compared with Response Surface Methodology (RSM) based model and it has been found that the values of Kerf width predicted by the Neuro Fuzzy Model are more closer to the experimental values.

Modeling and Optimization of Kerf Taper in Pulsed Laser Cutting of Duralumin Sheet

Duralumin sheets are strong, hard, light weight and heat treated alloy of Aluminum, widely used by different sectors such as automobile, marine, aircraft and satellites. Many a times those applications demand complex shapes and intricate profiles with stringent design requirements which are not completely achieved by conventional sheet cutting methods. Laser cutting has capability of quality cutting with above requirements in thin sheetmetals. But highly reflective and thermally conductive sheetmetals like Duralumin pose difficulty in achieving quality cuts by laser beam. The kerf taper always occurs in laser cut specimen due to inherent converging-diverging profile of laser beam. The optimization of kerf taper and other cut qualities such as surface roughness, heat affected zone and recast layer formation in difficult-to-laser-cut sheetmetals like Duralumin or Aluminium alloy has become recent research interests. The aim of present research is to optimize kerf taper in pulsed laser cutting of Duralumin sheet using hybrid approach of ‘design of experiment (DOE)’ and ‘artificial intelligence tool’ such as genetic algorithm. The empirical model for kerf taper has also been proposed with the discussion on parametric effect.© 2012 ASME

Optimization of Multiple Quality Characteristics in Laser Cutting of Difficult-to-Laser-Cut Material

This paper presents experimental study of laser cutting of Aluminium alloy sheet with the aim to optimize multiple quality characteristics such as cut edge surface roughness and kerf deviation, simultaneously. The Taguchi method combined with Grey relational analysis has been used for parameter optimization. The Principal component analysis and entropy measurement method have been used for eliminating co-linearity and deciding the weighting factors, respectively. The results indicate considerable improvements in multiple quality characteristics.

Parameter optimisation in laser cutting of aluminium alloy sheet

Being a thermal energy-based advanced cutting process, the capability of laser beam cutting depends mainly on optical and thermal properties of materials to be cut rather than mechanical properties. Highly reflective and thermally conductive sheetmetals are known as difficult-to-laser-cut materials. This paper presents an experimental approach to investigate the optimum laser cutting parameters, which minimise the surface roughness during pulsed laser cutting of difficult-to-laser-cut aluminium alloy sheet. Robust parameter design has been used for deciding the optimal parameter levels for straight cut profiles. An empirical model for surface roughness has also been developed. On the basis of optimisation results, it has been found that the optimal parameter level suggested give minimum surface roughness in present operating conditions. The results have also been verified by running some confirmation tests.