A finite element based data analytics approach for modeling turning process of Inconel 718 alloys
Introduction
The recent spurt in emerging technologies in manufacturing engineering sector has given rise to a greater demand for energy. Manufacturing engineering components generally involves metal cutting process such as turning, milling, grinding etc. The most commonly used engineering material is the Inconel 718 which is a Nickel based alloy and can be used for applications in space vehicles, aircraft gas turbines, nuclear reactors, reciprocating engines, petroleum industries and thermal exchangers (Olovsjö et al., 2012, Zhou et al., 2012, Cantero et al., 2013). The low machinability characteristic of Inconel 718 makes the turning process a challenging task due to excessive tool wear resulting in excessive heat generation and poor surface finish (Bhatt et al., 2010, Khidhir and Mohamed, 2010). The power required for machining of Inconel 718 is normally fed through a central power distribution system (Zhu et al., 2013). The cutting force is an expensive and essential component for driving the metal cutting process. Optimizing cutting force can directly regulate the power consumption in metal cutting industry which can result in greener and eco-friendly industrial process. Much of the earlier studies on turning process have laid emphasis on machine process parameters on the mechanical properties such as wear, surface roughness, temperature distribution of the material and machining tool (Zhao et al., 2002, Fahad et al., 2012, Ezilarasan et al., 2014). With the focus for green manufacturing taking center stage in recent days, some studies have also been conducted to improve environmental performance of certain manufacturing processes (Campatelli et al., 2014, Emami et al., 2014). In this work, the authors have presented an attempt to formulate an integrated numerical model that improves the sustainability of metal cutting of Inconel 718 alloy.
The machinability characteristics of Inconel 718 alloy has been extensively researched in literature using laboratory based machining experimentation and computational approaches. Thakur et al. (2009) investigated the machinability parameters such as the cutting temperature, surface roughness, cutting force, etc. while subjecting Inconel 718 alloy to high speed machining. The studies showed that the chip characteristics, surface integrity, shear angle influences the machining properties of the Inconel alloy. Pawade and Joshi (2011) applied Taguchi method for optimizing the machining of Inconel alloy at high cutting velocities. They concluded that the turning characteristics is vastly influenced by the tool cutting depth. Zhu et al. (2013) conducted a review on machining properties of Inconel 718. Their review indicated that a wide research scope exists in understanding tool wear mechanisms by considering the factors such as tool coating layer, monitoring the machining process and tool geometry. Apart from experiments, finite element modeling (FEM) has also been used in numerical modeling and analysis of the mechanics of Inconel machining process. Zhao et al. (2002) modeled the flank wear of orthogonal cutting of Inconel 718 alloy by considering the material softening due to heat buildup and the normal stress. Zębala and Słodki (2013) conducted an FEM based analysis for investigating the effectiveness of chipbreakers for machining of Inconel 718. The simulation showed that the distance between the tool rake face and its cutting edge affects the stress and temperature distribution in the tool. Lorentzon and Järvstråt (2008) implemented an empirical cutting model in a commercial FEM software to predict the machining characteristics in turning of Inconel 718. In their work, they analyzed different friction and wear models and assessed their impact on the wear profiles generated from the finite element (FE) model.
Numerical studies based on FE models are widely used to understand and analyze the machining process of Inconel 718 alloy when compared to that of performing expensive machining in laboratory conditions. This is due to the reason that FE models serves as a viable alternative to understand the wear mechanisms and chip formation without conducting time consuming experiments for monitoring machining process (Li, 2012, Senthilkumaar et al., 2012). In addition, the FE models are capable of generating accurate solutions and can give rapid insight to various parameters such as pressure and temperature distribution of materials at low cost (Pittalà and Monno, 2010). These FE models can generate the necessary data which can then be fed into an Artificial Intelligence or other data analytics (DA) models (Yildiz, 2012, Yildiz, 2013a, Yildiz, 2013b, Yildiz, 2013c) for formulating mathematical relationship between the various input and output functions. Hence, the objective of the authors' research work is to develop an integrated finite element based data analytics (FE-DA) model that can integrate the advantages of these two numerical approaches. The proposed model also helps the industry to gain insight on the effect of input machining variables on the environmentally effective manufacturing process of Inconel 718 alloy.
In this work, FE modeling has been performed to determine the cutting force and power consumption required while turning the Inconel 718 alloy based on the given process inputs viz. cutting velocity, cutting angle, depth of cut and concentration of cutting fluid. The data generated from the FE model is used to obtain an explicit mathematical model of cutting force using DA approach. The model performance is measured by comparing the results from the DA model with that of the FEM calculated results and the most dominant input factor that affects the cutting force of Inconel 718 alloy are determined.
Section snippets
An integrated FEM based DA approach
The machining characteristics of Inconel 718 have been modeled in this work using an integrated FE modeling based DA modeling. The input data obtained from FE modeling is combined with the paradigm of a popular DA technique, genetic programming (GP). A brief description of the FE modeling and the data analytics modeling using GP technique is described in this section.
Modeling the turning operation using FE modeling
The FE method which is used to simulate the turning operation of Inconel 718 alloy and the subsequent validation by comparing with experimental data is described in this section. At first, a 3D solid model is created in commercially available FEM software, ABAQUS. The 3D solid model consists of workpiece geometry which is a prismatic rectangle and cutting tool which is triangular shaped with an inclination angle χ = 90°, flank angle α = 5° and rake angle γ = −6°. In the simulation, workpiece is
FE-DA model for cutting force analysis of Inconel 718 alloy
The FE modeling generates the values of cutting force and power consumption by systematically varying the input turning parameters, viz. depth of cut (x1), cutting velocity (x2), feed rate (x3), tool cutting angle (x4) and cutting fluid concentration (x5). The input variables were varied in the FE modeling by means of full-factored design of experiments. The FE modeling resulted in 63 datasets and the statistical metrics of the dataset is shown in Table 2. The dataset is then divided in the
Analysis of cutting force and power consumption with specific input process parameters
The information regarding the individual contribution of each of the specific input process parameters towards the cutting force is performed next. As can be seen in the previous section, the DA technique provides a mathematical relationship between the measured output variable (cutting force) with each of the four specific input process variables. Using this model, a weighted analysis is performed to determine which of the input exerts maximum influence on the cutting force. The formula to
Conclusions
The present work introduced a theoretical approach for improving the sustainability of hard to machine alloys such as Inconel 718. An initial FE modeling was done to predict the cutting force and power consumption with input process variables and subsequently data analysis was applied on the data generated by the FE model. The data analysis performed using genetic programming resulted in a mathematical model and the validation of model was carried out by comparing the computed tool flank wear
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