Parametric analysis and optimization of wire electrical discharge machining of γ-titanium aluminide alloy
Introduction
Wire electrical discharge machining (WEDM) has been found to be an extremely potential electro-thermal process in the field of conductive material machining. Owing to high process capability it is widely used in manufacturing of cam wheels, special gears, stators for stepper motors, various press tools, dies and similar intricate parts. Selection of optimum machining parameter combinations for obtaining higher cutting efficiency and other dimensional accuracy characteristics is a challenging task in WEDM due to presence of large number of process variables and complicated stochastic process mechanism. Hence, there is a demand for research studies which should establish a systematic approach to find out the optimum parametric setting to achieve the maximum process criteria yield for different classes of engineering materials. An effective way to solve this state of problem is to focus on establishing the relationship between machining input parameters and machining criteria performances. A number of research works has been carried out on different materials to study the influence of different process parameters on EDM and WEDM [1], [2], [3], [4], [5], [6], [7], [8].
In the present research study wire electrical discharge machining of γ-titanium aluminide alloy (Ti-44.5 Al-2 Cr-2 Nb-0.3B (at.%)) has been considered. The material is attracting considerable interest now a day due to their high temperature strength retention, low-density (3.76 g/cm3) excellent resistance to ignition and good creep and oxidation resistance. The room temperature mechanical properties of the alloy in primary annealed condition are exhibited in Table 1. TiAl-based alloys are potential candidates for replacing Ti-based and Ni-based superalloys for structural applications in the range of 400 °C–800 °C. This alloy is of great interest in aerospace and automobile industries. Most of these components have performed well in laboratory tests as well as in the field. Engine valves, turbine blades, airframes, seal supports and cases are some examples [9]. But like other intermatallics these alloys are not ductile and have low fracture toughness at room temperature which makes them difficult to fabricate [10]. Further it is found that it is extremely difficult to machine by conventional method due to its excellent strength property. Different aspects of machining have been investigated by several researchers [11], [12]. But no comprehensive research work has been reported so far in the field of wire electrical discharge machining of this alloy. No technology tables or charts are available for wire electrical discharge machining of such important and useful materials in industry. Therefore, it is imperative to develop a suitable technology guideline for optimum machining of this alloy.
Further, in majority of the past research works machining speed and surface finish have been considered. Some research work has been done keeping in view of the accuracy aspects [13], [14] but wire offset parameter has so far never been explored in any process modeling and optimization. Prior to machining the knowledge of wire offset value for different input parameter setting is very much essential for effective dimensional control.
As suggested by Phadke [15] an additive model has been introduced to model the process. In the present model dimensional deviation along with the machining speed and surface finish has been considered as cutting performance. After modeling the process, optimization has been carried out. Here the prime objective is to maximize both the cutting speed and surface finish quality. Hence, this is a class of optimization problem deals with simultaneous optimization of multiple objective functions i.e. cutting speed and surface finish quality. In general no perfect combination exists that can result simultaneously in both the best cutting speed and surface finish quality. To solve this multi-objective optimization problem it has been modeled explicitly as constrained optimization problem. Beside this by using another algorithm a set of optimal solutions known as Pareto-optimal solutions has been searched out. All those Pareto-optimal solutions are equally important as far as surface finish and cutting speed are concerned.
Section snippets
Plan of experimentation
Based on some literature survey and preliminary investigations, the following six parameters i.e. pulse on time (Ton), pulse off time (Toff), peak current (IP), servo reference voltage (SV), wire tension (WT) and dielectric flow rate (discharge pressure) (FR) were chosen as input.
Table 2 shows different levels of these control parameters considered for single pass cutting operation. There are other factors, which can be expected to have an effect on the measure of performance are kept constant
Additive modeling of WEDM process
Additive model has been employed on the basis of matrix experiments using orthogonal arrays [15]. An additive model can be viewed as superposition model or a variable separable model. It can be noted that superposition model implies that the total effect of several factors is equal to the sum of individual factor effects. It is possible for the individual factor effects to be linear, quadratic or of higher order. In an additive model cross product terms involving two or more factors are not
Parametric analysis based on Taguchi methodology
Fig. 2, Fig. 3, Fig. 4 shows the effect of six control factors on cutting speed (Vc), surface finish (Ra) and dimensional deviation (D). Based upon the experimental result shown in Table 3, an analysis of variance was performed in order to estimate the predictive accuracy of the model and to determine the relative significances of the different factors. From the analysis of the cutting speed data as shown in Table 4 it is observed that pulse on time, pulse off time and wire tension play
Strategy for parametric optimization through additive model
From ANOVA of all control factors it is apparent that none of the response parameters (i.e. cutting speed, surface finish and dimensional deviation) are significantly influenced by servo reference voltage within the experimental range. Hence, this control factor was eliminated from the additive model. After dropping out this factor the process has been modeled considering only the remaining five factors. Experimental result shown in Table 3 was used for modeling of this process. This model can
Optimization of WEDM process through constrained optimization technique
Because of complexity involved in multi-objective optimization algorithm, it is easier to consider only one objective and formulate the other objectives as constraints. For the production purpose, the best combination of parameter level should produce the maximum cutting speed, while maintaining the required surface roughness within desired limit. Here the cutting speed has been considered as objective function and surface roughness has been considered as constraints. This requirement may be
Search for Pareto-optimal WEDM process parameters
By searching the Pareto-optimal solution one can find multiple optimal solutions. All 243 outputs for cutting speed and surface finish were plotted in Fig. 5. For convenience, Vc and 1/Ra were considered as X- and Y-axis, respectively. Pareto-optimal solutions have to be searched out from all these 243 outputs. Here, the Pareto-optimal solutions means that it is better than any other output at lest with respect to one process criterion i.e. Vc or 1/Ra. If one parameter combination results in
Conclusions
Experimental investigation on single pass cutting of wire electrical discharge machining of γ-TiAl alloy has been carried out. The process has been successfully modeled using additive model. The predicted response parameters from the model agreed quite well with that of the experimental result. Based on the developed model influence of the various process parameter on the machining criteria was observed. It was noted that both surface roughness as well as dimensional deviation is independent of
Acknowledgement
Authors acknowledge assistance provided by the CAS Ph-II Programme of Production Engineering Department, Jadavpur University under University Grants Commission, New Delhi.
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