Browse Topic: Machining processes

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A Methodology to Characterize the Influence of Paint Baking and Pre-Straining on the Tensile Properties and Sheared Edge Sensitivity of Third Generation Advanced High Strength Steels2025-01-8223To be published on 04/01/2025
The existing ASTM A653 standard for characterization of the bake hardening index (BHI) involves application of a pre-strain followed by bake hardening and subsequent testing of an ASTM-E8 tensile coupon that can lead to premature fracture in steels with high strengths and lower elongations. To this end, a new test procedure is proposed with application to 980 and 1180 MPa third generation advanced high strength steels (3G-AHSS). The proposed methodology involves pre-straining over-sized tensile specimens using the so-called KS-1B geometry, which is followed by the extraction of an ASTM E8 sample from the gauge region on which the baking and secondary tensile testing are conducted. Various pre-strain levels from 2% to 10% were considered with all tests performed using the proposed methodology avoiding premature fracture compared to the ASTM A653 method that was evaluated for pre-strain levels of 2% and 8%. Finally, to characterize the influence of paint baking on regions with
Northcote, Rhys Brennan, Berry, Avalon, Narayanan, Advaith, Tolton, Cameron, Butcher, Cliff, McCarty, Eric
Technical Paper
Comparative Evaluation of Untextured and Textured Surfaces for Wear, Wettability and Friction Characteristics2025-01-8331To be published on 04/01/2025
Reduction of frictional losses by changing the surface roughness in the form of surface textures has been reported as an effective method in reducing friction in the boundary regime of lubrication. LASER based microtexturing has been mostly used to create these texture patterns and it is reported that it can reduce the frictional resistance by ~20-50%. However, the use of laser-based techniques for texture preparation led to residual thermal stress and micro cracks on the surfaces. Hence, the current study emphasizes using conventional micromachining on piston material (aluminium alloy Al4032) to overcome this limitation. Three variations of semi-hemispherical geometries were prepared on the surface of aluminium alloy with dimple depths of 15, 20 and 40 µm and dimple diameters of 90, 120 and 240 µm. Prepared textured surfaces with untextured surfaces are compared in terms of wear, wettability, and friction characteristics based on Stribeck curve behaviour. Conventional micromachining
Sahu, Vikas Kumar, Shukla, Pravesh Chandra, Gangopadhyay, Soumya
Technical Paper
NdFeB Magnets Manufactured by Hot Pressing Using Strip-Cast and Melt-Spun Powders2025-01-8217To be published on 04/01/2025
The initial powder used for the manufacturing of NdFeB permanent magnets is usually prepared either by melt spinning or strip casting. The powders produced by these two methods show different particle shapes and sizes which may be suitable for different applications, e.g., manufacturing bonded, sintered, or hot-deformed magnets. While melt-spun powder is a good initial material for bonded and hot-deformed magnets, strip-cast powder is normally used for sintered magnets. In this study, NdFeB powders manufactured by melt spinning and strip casting were hot pressed (without particle alignment) under the same conditions. Although the processing parameters are exactly the same (pressed in the same mold), the magnetic properties of the magnets made from the two powders are significantly different. The magnet made from the strip-cast powder (after ball milling) shows reasonably good magnetic properties, with a coercivity (HcJ) of 1270 kA/m and a remanence (Br) of 0.7 T, while that made from
He, Youliang, Song, Shaochang, Walsh, Dan, Bernier, Fabrice, Mozharivskyj, Yurij, Peng, Phillip
Technical Paper
A hybrid algorithm for optimizing machining and spraying parameters in MQL-turning of Inconel 800H2025-28-0042To be published on 02/07/2025
Inconel 800H superalloy is a difficult-to-turn material. This study aims to achieve optimal machining results, including reduced cutting force, improved surface roughness, and minimized residual stress, by optimizing input machining parameters like cutting speed, feed rate, spraying angle, and nozzle distance on Inconel 800H. The Taguchi L27 method is utilized for experimentation, while the Harris hawks optimizer (HHO) is applied in a multi-objective optimization model. Additionally, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is used to identify the optimal input parameters. Five distinct weight schemes were employed, including the Analytic Hierarchy Process (AHP), the Entropy weight method, Criteria Importance through Inter-Criteria Correlation (CRITIC), Grey relational analysis (GRA), and Principal Component Analysis (PCA) to determine response weights. The analysis revealed that the primary factor affecting all measured weights is the feed
Kannan, Venkatesan, Mokshajna, Kotha
Technical Paper
AI-Driven Optimization of Advanced Machining for Haste alloy by ANFIS Method2025-28-0141To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study primarily examines the use of Inconel 625 in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the statistical significance of process parameters that impact performance indicators. The proposed regression models for Inconel 625 are more versatile
Natarajan, Manikandan, Pasupuleti, Thejasree, D, Palanisamy, Silambarasan, R, Krishnamachary, PC
Technical Paper
Optimizing Titanium Alloy Grade 7 Machining with Grey Relational Analysis2025-28-0049To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. Due to the growing need for superior products and the requirement for quick design adjustments, decision-making in production has become more complex. This study focuses on Titanium Grade 5 and suggests creating predictive models utilizing a Taguchi-grey technique to achieve multi-objective optimization in ECM. The trials are structured based on Taguchi's principles, utilizing Taguchi-grey relational analysis (GRA) to simultaneously maximize several performance indicators. This entails optimizing the pace at which material is removed, decreasing the roughness of the surface, and attaining precise geometric tolerances. ANOVA is used to assess the relevance of process variables that affect these measures. The suggested predictive technique for Titanium Grade 5 outperforms current models in terms of flexibility
Pasupuleti, Thejasree, Natarajan, Manikandan, Kumar, V, Sagaya Raj, Gnana, Krishnamachary, PC, Silambarasan, R
Technical Paper
Advanced AI Tools for Haste alloy Machining: ANFIS Approach to Advanced Machining Optimization2025-28-0156To be published on 02/07/2025
The objective of this research is to develop an optimization method for the Electrochemical Machining (ECM) process on Inconel 625 material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Inconel 625 is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
Pasupuleti, Thejasree, Natarajan, Manikandan, Ramesh Naik, Mude, Somsole, Lakshmi Narayana, Silambarasan, R
Technical Paper
Taguchi Based Grey Approach for Optimizing the Advanced Machining of Nickel Alloys in Manufacturing of Automotive Parts2025-28-0147To be published on 02/07/2025
The objective of this research is to develop an optimization technique for the Electrochemical Drilling process on Inconel 625 material, considering various performance factors. The Taguchi approach, along with Grey Relational Analysis (GRA), forms the basis for optimization. Inconel 625 has a wide range of uses in industries such as aerospace, nuclear, and marine, especially in harsh environments. The experimental trials conducted in accordance with Taguchi's approach have utilized three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. When doing this examination, we analyze not only the rate at which material is removed and the roughness of the surface, but also other characteristics that indicate performance, such as overcut, shape, and orientation tolerance. The analytical findings indicate that the feed rate is the primary factor that directly impacts the required performance standards. Regression models are constructed to make predictions, and
Natarajan, Manikandan, Pasupuleti, Thejasree, Sagaya Raj, Gnana, Silambarasan, R, Somsole, Lakshmi Narayana
Technical Paper
Predictive Grey-ANFIS Model for High-Precision Machining of Nimonic Alloy in Automobiles2025-28-0043To be published on 02/07/2025
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Incoloy 800HT material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Incoloy 800HT is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's principle and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that affects the intended performance criteria. In order to
Natarajan, Manikandan, Pasupuleti, Thejasree, C, Navya, Kiruthika, Jothi, Silambarasan, R
Technical Paper
Grey Relational Analysis for Multi-Objective Optimization in Aerospace Machining of Titanium Grade 192025-28-0047To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, irrespective of their hardness. Due to the growing need for superior products and quick design adjustments, decision-making in production has become increasingly complex. This study focuses on Titanium Grade 9 and proposes creating predictive models using a Taguchi-grey technique to achieve multi-objective optimization in ECM. The experiments are structured based on Taguchi's principles, utilizing Taguchi-grey relational analysis (GRA) to simultaneously optimize several performance indicators, including the material removal rate, surface roughness, and geometric tolerances. ANOVA is employed to assess the significance of process variables affecting these measures. The proposed predictive technique for Titanium Grade 9 outperforms current models in terms of flexibility, efficiency, and accuracy, providing enhanced capabilities for monitoring and control
Pasupuleti, Thejasree, Natarajan, Manikandan, Krishnamachary, PC, Katta, Lakshmi Narasimhamu, Silambarasan, R
Technical Paper
Revolutionizing Titanium Alloy Machining: ANFIS Model for Advanced Machining Performance Optimization2025-28-0046To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 9 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 9 provides more flexibility, efficiency, and accuracy in comparison to
Pasupuleti, Thejasree, Natarajan, Manikandan, Raju, Dhanasekar, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Technical Paper
Optimization of machining parameters based on desirability function analysis for Stir Casted Aluminium Hybrid Metal Matrix Composites2025-28-0119To be published on 02/07/2025
These days, aluminum and other material composites are indispensable for a wide range of engineering applications, including automotive-related ones. The machinability investigations of hybrid metal matrix composites (HMMC) made of Al 6061 are reported in this paper. Graphene nanoparticles (GNp) and boron carbide were used to reinforce Al6061 alloy for the experiment. Stir casting was used to create the hybrid composite under the right circumstances. Since HMMC is difficult to machine using traditional machining techniques, the advanced method of electrical discharge machining (EDM) was used. On stir-casted Al-B4C-GNP composites, EDM machinability investigations were conducted. Using input parameters such pulse on time, pulse off time, and peak current for the response of material removal rate (MRR) and surface roughness, Taguchi based Desirability function Analysis was used to optimize the EDM process parameters. The optimal machining parameters have been determined by the composite
Kala, Lakshmi K, Madhuri, K, Reddy, Damodara, Tarigonda, Hariprasad, R L, Krupakaran, Tharehallimata, Gurubasavaraju, Naidu, B Vishnu Vardhana
Technical Paper
Optimization of WEDM Parameters for Invar 36 Automotive Components Using Taguchi-Grey Relational Analysis2025-28-0153To be published on 02/07/2025
Wire Electrical Discharge Machining (WEDM) is an essential procedure used to shape intricate geometries in conductive materials such as Invar 36, which is recognized for its minimal thermal expansion characteristics used for high temperature applications especially in automobile engine parts and exhaust valves. The objective of this study is to improve the effectiveness and precision of Wire Electrical Discharge Machining (WEDM) for Invar 36 material by utilizing Taguchi-based Grey Relational Analysis (GRA). The study examines the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness (Ra), material removal rate (MRR). The experimentation have been planned and conduced as per Taguchi L9 orthogonal array design. Grey relational grades are computed to measure the correlation between input parameters and machining responses, enabling the determination of the most favourable parameter
Natarajan, Manikandan, Pasupuleti, Thejasree, Kumar, V, Sagaya Raj, Gnana, Krishnamachary, PC, Silambarasan, R
Technical Paper
Multi-Objective Optimization for Contemporary Drilling of Nimonic Alloy Using Taguchi-Grey Relational Analysis2025-28-0050To be published on 02/07/2025
The objective of this research is to develop an optimization strategy for the Electrochemical Drilling process on Inconel 800HT material, taking into account various performance factors. The optimization strategy relies on the integration of the Taguchi method with Grey Relational Analysis (GRA). Inconel 800HT is extensively utilized in aerospace, nuclear, and marine industries, specifically in situations that are prone to corrosion. The experimental trials are structured based on Taguchi's principle and encompass three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This inquiry examines performance indicators like the rate of material removal, surface roughness, as well as geometric parameters such as overcut, shape, and orientation tolerance. Based on the investigation, it is determined that the feed rate is the primary factor that directly affects the intended performance criteria. In order to enhance the accuracy of predictions, multiple
Pasupuleti, Thejasree, Natarajan, Manikandan, D, Palanisamy, Silambarasan, R, Krishnamachary, PC
Technical Paper
Smart Machining of Titanium Grade 7: ANFIS Application for Process Parameter Prediction2025-28-0160To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has grown increasingly intricate. This study specifically examines Titanium Grade 5 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modelling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 5 provides more flexibility, efficiency, and accuracy in
Natarajan, Manikandan, Pasupuleti, Thejasree, D, Palanisamy, Kiruthika, Jothi, Silambarasan, R
Technical Paper
A Machine learning Approach for the prediction of Surface Roughness Using The Tool Vibration Data In Turning Operation2025-28-0152To be published on 02/07/2025
Surface roughness is a key factor in different machining processes and plays an important role in ergonomics, assembly, wear and fatigue life of components. Functionality, performance and durability of parts are also affected by surface roughness. Although maintaining an optimum surface roughness is a major challenge in many manufacturing industries. Surface roughness during machining depends upon machining parameters such as cutting speed, feed rate and depth of cut. Also, tool vibrations during machining have significant influence in surface roughness. In this work an attempt is made to predict the surface roughness of machined components during the turning process by using machine learning techniques with vibration signals. By varying different machining parameters and keeping other tooling and material properties same, a range of surface roughness values can be obtained. For each condition, corresponding tool vibration signals were recorded. Our experimental setup involves
S S, Safeer, Sadique, Anwar, D, Navaneeth
Technical Paper
Data-Driven Optimization of Titanium Grade 7 Machining for Automotive Applications2025-28-0143To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. With the rising demand for superior products and the necessity for quick design modifications, decision-making in the industrial sector becomes increasingly complex. This study specifically examines Titanium Grade 5 and suggests creating prediction models through regression analysis to estimate performance measurements in ECM. The experiments are formulated based on Taguchi's ideas, utilizing a multiple regression approach to deduce mathematical equations. The Taguchi method is utilized for single-objective optimization in order to determine the ideal combination of process parameters that will maximize the material removal rate. ANOVA is a statistical method used to determine the relevance of process factors that affect performance measures. The suggested prediction technique for Titanium Grade 5 exhibits
Natarajan, Manikandan, Pasupuleti, Thejasree, Kumar, V, Krishnamachary, PC, Somsole, Lakshmi Narayana, Silambarasan, R
Technical Paper
Advanced Parameter Forecasting for Titanium Grade 19 Machining in Automotive Applications2025-28-0045To be published on 02/07/2025
Electrochemical machining (ECM) is a remarkably effective technique for producing detailed designs in materials that can conduct electricity, regardless of their level of hardness. As the desire for high-quality products and the necessity for rapid design changes grow, decision-making in the industrial sector becomes increasingly intricate. This work focuses on Titanium Grade 9 and proposes the development of prediction models using regression analysis to estimate performance measurements in ECM. The experiments are designed using Taguchi's methodology, employing a multiple regression approach to produce mathematical equations. The Taguchi technique is utilized for the purpose of single-objective optimization in order to determine the optimal combination of process parameters that will optimize the rate at which material is removed. ANOVA is a statistical method used to assess the relevance of process factors that impact performance indicators. The suggested prediction technique for
Pasupuleti, Thejasree, Natarajan, Manikandan, Ramesh Naik, Mude, Silambarasan, R, D, Palanisamy
Technical Paper
Optimization of Wire Electrical Discharge Machining Parameters for Invar 36 Material Using Regression Modeling2025-28-0122To be published on 02/07/2025
Wire Electrical Discharge Machining (WEDM) is a commonly employed technique for shaping conductive materials such as SAE 1010 steel, providing highly accurate machining capabilities. This low-carbon steel has excellent formability, weldability, and machinability, making it useful in many contexts including bolts, nuts, valves and cold headed fasteners. The objective of this study is to improve the efficiency and precision of the Wire Electrical Discharge Machining (WEDM) process for SAE 1010 material. This will be achieved by creating a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The study investigates the influence of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness (Ra), material removal rate (MRR). The experimentation has been planned and conduced as per Taguchi L9 orthogonal array design. The ANFIS predictive model is constructed by utilizing the obtained
Pasupuleti, Thejasree, Natarajan, Manikandan, Raju, Dhanasekar, Krishnamachary, PC, Silambarasan, R
Technical Paper
Enhanced Aero Engine Components Manufacturing Through Predictive Models for Cupronickel Machining2025-28-0137To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design changes, decision-making in the manufacturing industry has become increasingly intricate. The primary objective of this work is to concentrate on Inconel 718 and suggest the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the purpose of predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the target of maximizing material removal rate, minimizing surface roughness, and simultaneously achieving precise geometric tolerances. The relevance of process parameters affecting these performance metrics is assessed using analysis of variance (ANOVA). The ANFIS model suggested for Inconel 718 provides more flexibility, efficiency
Pasupuleti, Thejasree, Natarajan, Manikandan, Ramesh Naik, Mude, Kiruthika, Jothi, Silambarasan, R
Technical Paper
Process Parameter Prediction for Advanced Machining of Copper-Nickel Alloy Turbine Components2025-28-0155To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study focuses on Inconel 718 and suggests creating predictive models to anticipate performance metrics in ECM through regression analysis. The experiments are formulated based on Taguchi's principles, and a multiple regression model is utilized to deduce the mathematical equations. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the relevance of process parameters that impact performance indicators. The proposed prediction technique for Inconel 718 is more adaptable, efficient, and accurate
Pasupuleti, Thejasree, Natarajan, Manikandan, Sagaya Raj, Gnana, Silambarasan, R, Somsole, Lakshmi Narayana
Technical Paper
Performance Forecasting in Nickel Alloy Machining: A Study for Aerospace Engineering Applications2025-28-0154To be published on 02/07/2025
The process of electrochemical machining, often known as ECM, is capable of effectively shaping complicated structures in materials that conduct electricity, independent of the materials' level of hardness hence especially used for automobile and aerospace applications. As a result of the demand for high-quality products and the desire for rapid design changes, the manner in which decisions are made in the manufacturing industry has become increasingly contentious. With the assistance of regression analysis, this study proposes the development of predictive models for the purpose of forecasting the performance measures in electrochemical machining of Inconel 800HT. The trials are designed in accordance with Taguchi's principles, and a multiple regression model is utilized in order to derive the mathematical equations. Taguchi's method can be applied as a methodology for single objective optimization in order to attain the most optimal combination of process parameters for the purpose
Natarajan, Manikandan, Pasupuleti, Thejasree, Sagaya Raj, Gnana, Silambarasan, R, Kiruthika, Jothi
Technical Paper
Taguchi-Grey Hybrid Method for Enhanced Advanced Machining of Cupronickel Alloys2025-28-0041To be published on 02/07/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. With the growing demand for superior products and the increasing necessity for quick design modifications, decision-making in the manufacturing industry becomes increasingly complex. The primary objective of this work is to concentrate on Inconel 718 and suggest the creation of predictive models through the utilization of a Taguchi-grey technique for the purpose of multi-objective optimization in ECM. The trials follow Taguchi's principles and utilize a Taguchi-grey relational analysis (GRA) technique to maximize numerous performance indicators concurrently. This involves optimizing the pace at which material is removed while decreasing the roughness of the surface and obtaining precise geometric tolerances. ANOVA is a statistical method used to determine the importance of process factors that influence these
Natarajan, Manikandan, Pasupuleti, Thejasree, C, Navya, Somsole, Lakshmi Narayana, Silambarasan, R
Technical Paper
Superfinishing of HVOF Applied Tungsten Carbide CoatingsAMS2452 (Current)01/09/2025
This specification covers requirements for the superfinishing of High Velocity Oxygen/Fuel (HVOF) applied tungsten carbide thermal spray coatings
AMS B Finishes Processes and Fluids Committee
Material Specification
Steel Bars 1.0Cr - 0.20Mo - 0.45Se (0.39 - 0.48C) (4142H Modified) Die-Drawn, 130 ksi (896 MPa) Yield Strength Free MachiningAMS6378J (Current)12/18/2024
This specification covers a free-machining, low-alloy steel in the form of round bars 3.50 inches (88.9 mm) and under in nominal diameter produced by a die-drawing process
AMS E Carbon and Low Alloy Steels Committee
Material Specification
Machining Studies on AISI 4140 Steel as Automotive Axle and Drive Shafts in Automobile via Numerical Simulation2024-01-523212/10/2024
This study focuses on machining automobile parts such as drive shafts and axles made of low alloy steel AISI 4140. The influence of cutting inserts geometrical parameters, viz., relief angle (RIA), rake angle (RAA), and nose radius (NA) are studied by designing experiments using Taguchi’s methodology. Numerical simulation is conducted using DEFORM-2D; a suitable L9 orthogonal array (OA) is considered for this work for varying combinations of inputs, and the resultant cutting force, maximum principal stress, and tool life are determined. Adopting a signal-to-noise (S/N) ratio minimizes the outputs for better machining conditions and achieves high-quality components with precision, tolerance, and accuracy. The ideal conditions obtained from the S/N ratio are RAA of 6°, RIA of 3°, and NR of 0.6 mm. Analysis of variance presents that the NR influences the resultant cutting force, wear depth, and work piece damage 73.51%, RAA following by 23.99%, and RIA by 2.03% achieved with a R2 value of
Senthilkumar, N.
Technical Paper
Empirical Modelling of Machinability during Drilling of Aluminium Oxide/Glass Fiber Reinforced Resin/Epoxy Composites2024-01-521912/10/2024
This study describes the Taguchi optimization process applied to optimize drilling parameters for glass fiber reinforced composite (GFRC) material. The machining process is analyzed in relation to process parameters using analysis of variance (ANOVA). The characteristics assessed for both the drilling and the specimen include speed, feed rate, drill size, and specimen thickness. The commercial software program MINITAB14 was used to collect and analyze the measured results. Cutting force and torque during drilling are examined in relation to these parameters using an orthogonal array and a signal-to-noise ratio. The primary goal is to identify the critical elements and combinations of elements that impact the machining process to achieve minimal cutting thrust and torque, based on the evaluation of the Taguchi technique
Raja, Rosari, Jannet, Sabitha, Kandavalli, Sumanth Ratna
Technical Paper
Optimization and ANFIS Predictive Modeling of Wire Electrical Discharge Machining for Invar 36 Material2024-28-024312/05/2024
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining approach that is well-known for its capability to create intricate forms in materials with high levels of hardness and intricate geometries. Invar 36, a nickel-iron alloy, is extensively utilized in industries that demand exceptional dimensional stability across a wide temperature range. The objective of this exploration is for optimizing the WEDM parameters of Invar 36 material. Additionally, a predictive model called Adaptive Neuro-Fuzzy Inference System (ANFIS) will be developed to forecast the machining performance. The study involved conducting experimental trials to analyze the influence of crucial factors in WEDM. These parameters included pulse-on time (Ton), pulse-off time (Toff), and current. The objective was to examine their influence on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of Design of Experiments (DOE) enabled a systematic
Natarajan, Manikandan, Pasupuleti, Thejasree, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Technical Paper
Effect of Temperature on Aluminum Alloy (Al4032) Using Textured Surface2024-28-016012/05/2024
The larger domain of surface texture geometry and other input variables related to engine operation, i.e., elevated temperature, has remained to be studied for finding suitable surface texture for real-time engine operations. In previous efforts to find suitable surface texture geometry and technique, the tribological performance of the piston material (Al4032) with dimples of varying diameters (90 to 240 μm) was evaluated under mixed and starved lubrication conditions in a pin-on-disk configuration. The disc was textured using a ball nose end mill cutter via conventional micromachining techniques. The area density and aspect ratio (depth to diameter) of the dimples were kept constant at 10% and 1/6, respectively. SAE 20W-40 oil was used as a lubricant with three separate drop volumes. The experiments were conducted in oscillating motion at temperatures of 50, 100 and 150°C. Conventional micromachining achieved improved dimensional precision and minimized thermal damage. Textured
Sahu, Vikas Kumar, Shukla, Pravesh Chandra, Gangopadhyay, Soumya
Technical Paper
Optimization of Wire Electrical Discharge Machining Parameters for Invar 36 Material Using Regression Modeling2024-28-024612/05/2024
Wire Electrical Discharge Machining (WEDM) is an advanced method of machining that provides distinct benefits in machining materials with high hardness and intricate geometries. Invar 36, a nickel-iron alloy with a lower coefficient of thermal expansion, is widely used in the aerospace, automotive, and electronic industries because of its excellent dimensional stability across a broad range of temperatures. The main objectives are to optimize the machining parameters and create regression models that can accurately predict the key performance indicators. Experimental trials were performed utilizing a WEDM setup to machine Invar 36 under various machining conditions, such as pulse-on time, pulse-off time, current setting percentage (%). The machining performance was evaluated by measuring the material removal rate (MRR), surface roughness (Ra). The design of experiment method (DOE) was utilized to systematically investigate the parameter space and determine the most effective machining
Natarajan, Manikandan, Pasupuleti, Thejasree, Kiruthika, Jothi, Krishnamachary, PC, Silambarasan, R
Technical Paper
Optimization and Regression Modeling of Wire Electrical Discharge Machining for Cupronickel Material2024-28-024412/05/2024
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining method that is well-known for its capacity to create complex forms in conductive materials with exceptional precision. Cupronickel, a hard material consisting of copper, nickel, and additional components, is widely employed in marine, automotive, and electrical engineering fields because of its exceptional ability to resist corrosion and conduct heat. The intention of this study is to optimize the parameters of Wire Electrical Discharge Machining (WEDM) for Cupronickel material and create regression models to accurately forecast the performance of the machining process. An exploration was carried out to analyze the influence of important parameters in wire electrical discharge machining (WEDM), namely pulse-on time, pulse-off time, and applied current on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of design of experiments (DOE) enabled a systematic
Natarajan, Manikandan, D, Palanisamy, Pasupuleti, Thejasree, A, Gnanarathinam, Umapathi, D, Silambarasan, R
Technical Paper
Nickel - Copper Alloy, Corrosion-Resistant, Bars, Forgings and Forging Stock 67Ni - 30Cu - 0.04S Free MachiningAMS4674J (Current)12/05/2024
This specification covers a corrosion-resistant nickel-copper alloy in the form of bars up to 3.00 inches (76.2 mm), inclusive, in thickness and forgings and forging stock of any size
AMS F Corrosion and Heat Resistant Alloys Committee
Material Specification
Optimization of Wire Electrical Discharge Machining Parameters for SAE 1010 Material Using Taguchi-Based Grey Relational Approach2024-28-023112/05/2024
Wire Electrical Discharge Machining (WEDM) has attracted considerable attention in contemporary manufacturing because of its capacity to accurately form conductive materials. This study aims to optimize the parameters of Wire Electrical Discharge Machining (WEDM) for SAE 1010 material, which is a commonly used low-carbon steel. The Taguchi-based Grey Relational Approach (GRA) is employed for this purpose. The goal is to optimize machining efficiency and quality while minimizing production costs. The research methodology combines the Taguchi method for experimental design with the GRA for multi-response optimization. The Taguchi L27 orthogonal array is utilized to carry out experiments, taking into account three controllable factors: pulse-on time, pulse-off time, and discharge current. In addition, the performance characteristics to be optimized include surface roughness (Ra) and material removal rate (MRR). The experimental results are analyzed using the GRA (Grey Relational Analysis
Natarajan, Manikandan, Pasupuleti, Thejasree, Kiruthika, Jothi, Krishnamachary, PC, Silambarasan, R
Technical Paper
Optimization of Wire Electrical Discharge Machining Parameters for Cupronickel Material Using TOPSIS Method2024-28-024512/05/2024
Wire Electrical Discharge Machining (WEDM) is a widely used manufacturing method that is employed to shape complex geometries in conductive materials such as cupronickel, which is highly regarded for its resistance to corrosion and ability to conduct heat. The aspiration of this investigation is to improve the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for cupronickel material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study analyzes the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness, material removal rate. Experimental trials are performed to collect data on these parameters and their corresponding machining characteristics. The TOPSIS optimization method is utilized to determine the most favourable parameter settings by evaluating each parameter combination against the ideal and
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, C, Navya, Silambarasan, R
Technical Paper
Optimization of Wire Electrical Discharge Machining Parameters for SAE 1010 Material Using TOPSIS Method2024-28-023712/05/2024
Wire Electrical Discharge Machining (WEDM) is a highly adaptable machining process that is extensively employed across various engineering industries to achieve precise machining of conductive materials. SAE 1010, a steel with low carbon content, is widely used in automotive, aerospace, and machinery components because it can be welded easily and shaped effectively. The aspiration of this study is to optimize the parameters of WEDM for SAE 1010 material by employing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. An empirical study was carried out to examine the impact of crucial machining variables, such as pulse-on time, pulse-off time, applied current on performance metrics of machining, such as material removal rate (MRR), surface roughness (Ra) and Overcut. The utilization of the design of experiments (DOE) methodology enabled the methodical investigation of the parameter space. Taguchi based TOPSIS provides a comprehensive approach to parameter
Natarajan, Manikandan, Pasupuleti, Thejasree, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Technical Paper
Optimization of Wire Electrical Discharge Machining Parameters for Invar 36 Material Using TOPSIS Method2024-28-024212/05/2024
Wire Electrical Discharge Machining (WEDM) is a contemporary method that is extensively employed for intricate machining operations, especially in materials with high hardness and intricate shapes. Invar 36, a nickel-iron alloy known for its minimal change in size with temperature and consistent dimensions, poses distinct difficulties in the process of machining because of its specific properties. This study explores the process of optimizing WEDM parameters for Invar 36 material by adopting the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The study involved conducting experimental trials to analyze the influence of significant machining variables, such as pulse-on time, pulse-off time, applied current, on performance indicators such as material removal rate (MRR), surface roughness (Ra). The Taguchi based TOPSIS method was utilized to analyze the problem of multi-criteria decision-making and determine the most favorable parameter configurations
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, Krishnamachary, PC, Silambarasan, R
Technical Paper
Development of Regression Analysis for Wire Electrical Discharge Machining of SAE 1010 Material2024-28-023612/05/2024
Wire Electrical Discharge Machining (WEDM) is now a crucial technique for shaping complex shapes in conductive materials such as SAE 1010 steel. This study aims to enhance machining efficiency and accuracy by developing regression analysis to model and optimize WEDM parameters for SAE 1010 material. The study aims to examine the impact of various parameters in WEDM, such as pulse-on time, pulse-off time, and discharge current, on key machining responses, including surface roughness (Ra), material removal rate (MRR). Experimental investigations are being carried out to achieve this objective. A set of Wire Electrical Discharge Machining (WEDM) experiments are conducted using a factorial design, resulting in a dataset that can be used for regression modeling. Subsequently, regression models are constructed to forecast machining responses using input parameters. The models are improved through statistical analysis, to evaluate the importance of each parameter. The regression equations
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Technical Paper
Optimization of Wire Electrical Discharge Machining Parameters for Cupronickel Using Taguchi-Based Grey Relational Analysis2024-28-023512/05/2024
Wire Electrical Discharge Machining (WEDM) is an important method engaged to make intricate shapes in conductive materials like Cupronickel, which is well-known for its ability to resist corrosion and conduct heat. The intention of this exploration is to enhance the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for Cupronickel material by utilizing a Taguchi-based Grey Relational Analysis (GRA). The study examines the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on key machining outcomes such as surface roughness (Ra), material removal rate (MRR). A comprehensive dataset is generated for analysis through a systematic series of experiments designed using the Taguchi method. Grey relational grades are assessed to measure the connections between the input parameters and machining responses, making it easier to determine the best parameter settings. The Taguchi-based GRA approach provides a systematic approach for
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, Ramesh Naik, Mude, Silambarasan, R
Technical Paper
Development of ANFIS Predictive Model for Wire Electrical Discharge Machining of Cupronickel Material2024-28-023912/05/2024
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R.
Technical Paper
Whole Systems Trade Analysis2024-01-345311/15/2024
ABSTRACT Ground vehicles are complex systems with many interrelated subsystems - finding the sweet-spot among competing objectives such as performance, unit cost, O&S costs, development risk, and growth potential is a non-trivial task. Whole Systems Trade Analysis (WSTA) is a systems analysis and decision support methodology and tool that integrates otherwise separate subsystem models into a holistic system view mapping critical design choices to consequences relevant to stakeholders. As a highly integrated and collaborative effort WSTA generates a holistic systems and Multiple Objective Decision Analysis (MODA) model. The decision support model and tool captures and synthesizes outputs from individual analyses into trade-space visualizations designed to facilitate rapid and complete understanding of the trade-space to stakeholders and provide drill down capability to supporting rationale. The approach has opened up trade space exploration significantly evaluating up to 1020+ potential
Edwards, Shatiel, Cilli, Matthew, Peterson, Troy, Zabat, Mike, Lawton, Craig, Shelton, Liliana
Technical Paper
Rigorous Accelerated Approach to Thermomechanical Processing Optimization for New Ballistic Protection Alloys2024-01-380711/15/2024
ABSTRACT The armor research and development community needs a more cost-effective, science-based approach to accelerate development of new alloys (and alloys never intended for ballistic protection) for armor applications, especially lightweight armor applications. Currently, the development and deployment of new armor alloys is based on an expert-based, trial-and-error process, which is both time-consuming and costly. This work demonstrates a systematic research approach to accelerate optimization of the thermomechanical processing (TMP) pathway, yielding optimal microstructure and maximum ballistic performance. Proof-of-principle is being performed on titanium alloy, Ti-10V-2Fe-3Al, and utilizes the Hydrawedge® unit of the Gleeble 3800 System (a servo-hydraulic thermomechanical testing device) to quickly evaluate mechanical properties and simulate rolling schedules on small samples. Resulting mechanical property and microstructure data are utilized in an artificial intelligence (AI
Lillo, Thomas, Chu, Henry, Anderson, Jeffrey, Walleser, Jason, Burguess, Victor
Technical Paper
Magnesium Alloy and Silicon Nitrides Composite Study: Synthesis and Abrasive Water Jet Machining Behavior and Optimization05-18-02-001411/15/2024
Related to traditional engineering materials, magnesium alloy-based composites have the potential for automobile applications and exhibit superior specific mechanical behavior. This study aims to synthesize the magnesium alloy (AZ61) composite configured with 0 wt%, 4 wt%, 8 wt%, and 12 wt% of silicon nitride micron particles, developed through a two-step stir-casting process under an argon environment. The synthesized cast AZ61 alloy matrix and its alloy embedded with 4 wt%, 8 wt%, and 12 wt% of Si3N4 are subjected to an abrasive water jet drilling/machining (AJWM) process under varied input sources such as the diameter of the drill (D), transverse speed rate (v), and composition of AZ61 composite sample. Influences of AJWM input sources on metal removal rate (MRR) and surface roughness (Ra) are calculated for identifying the optimum input source factors to attain the best output responses like maximum MRR and minimum Ra via analysis of variant (ANOVA) Taguchi route with L16 design
Venkatesh, R.
Journal Article
HIGH MN, HIGH AL STEELS FOR THICK PLATE ARMOR APPLICATIONS2024-01-373911/15/2024
ABSTRACT FeMnAlC alloys exhibit lower density (6.5-7.2 g/cm3) than traditional military steels (7.9 g/cm3) while maintaining similar energy absorption capabilities. Material substitutions in legacy systems must meet existing form/fit/function requirements, limiting opportunities for lightweighting of existing designs. This study examines production and material properties of thick plate with a nominal chemistry of 30% Mn, 9%Al and 1%C, in the wrought condition. Due to the high aluminum and carbon content, there are unique challenges to large scale (45+ ton heat) production versus typical armor steel chemistries. Lab-scale wrought and production material are characterized, comparing microstructure, and mechanical properties. Processing practices including teeming flux and rolling temperature are discussed. The high manganese content of this alloy presents challenges for welding and machining practices, such as limited compatibility of weld wires and substantial work hardening during
Sebeck, Katherine, Toppler, Ian, Rogers, Matt, Limmer, Krista, Cheeseman, Bryan, Howell, Ryan, Herman, William
Technical Paper
3D-PRINTING IMPACTS ON SYSTEMS ENGINEERING IN DEFENSE INDUSTRY2024-01-352711/15/2024
ABSTRACT Today’s combat vehicle designs are largely constrained by traditional manufacturing processes, such as machining, welding, casting, and forging. Recent advancements in 3D-Printing technology offer tremendous potential to provide economical, optimized components by eliminating fundamental process limitations. The ability to re-design suitable components for 3D-printing has potential to significantly reduce cost, weight, and lead-time in a variety of Defense & Aerospace applications. 3D-printing will not completely replace traditional processes, but instead represents a new tool in our toolbox - from both a design and a manufacturing standpoint
Deters, Jason
Technical Paper
Comparative Investigations of Tool Wear and Cutting Force for Wet and Dry Turning of Super Duplex Stainless Steels 250705-18-02-001211/07/2024
Super Duplex Stainless Steels (SDSS) are attracting attentions of the manufacturing industries due to the excellent corrosion resistance to critical corrosion. But SDSS2507 is the hardest to machine with lowest machinability index among DSS family. Moreover, formation of built-up layer (BUL) and work hardening tendency makes it further difficult to machine. Researchers have the conflict in opinions on using wet machining or dry machining using tool coatings. In this investigation SDSS2507 machining is carried out using uncoated and PVD–TiAlSiN-coated tools. The wet and dry machining environment are compared for increase in cutting speed from 170 m/min to 230 m/min. Excellent properties of PVD–TiAlSiN coatings exhibited microhardness of 39 GPa and adhesion strength of 88 N, which outperformed the uncoated tools. Tool life exhibited by coated tools was four times higher than uncoated tools. Wet machining was found to be ineffective when PVD-coated tools are used, exhibiting the same
Sonawane, Gaurav Dinkar, Bachhav, Radhey
Journal Article
Recent Developments on Aluminum 7075-Based Composite with Industrial Waste Fly Ash by Stir Casting and Their Applications05-18-02-000911/07/2024
Recent developments in manufacturing techniques and the development of Al7075 metal matrix composites (MMCs) with reinforcements derived from industrial waste have been steadily gaining popularity for aerospace and automobile applications due to their outstanding properties. However, there are still a lot of limitations with these composite materials. A great deal of research has been done to create new Al7075 MMC materials with the use of economic fly ash (FA) that possesses superior mechanical properties, corrosion resistance, density, and cycle cost. This review outlines different synthesis techniques used in the development of Al7075 MMCs using stir casting. Effects of FA along with other reinforcements on the mechanical, wear, machining, and microstructural properties of the composite are also discussed. Finally, a summary of the application of FA-based MMCs and a recap of the previous discoveries and challenges are reported. Future scope and potential areas of application are
Kumar, Randhir, Mondal, Sharifuddin
Journal Article
Optimizing Cutting Parameters for Effective Turning of EN-31 Steel by Utilizing Vegetable-Based Cutting Fluids as a Coolant05-18-01-000610/12/2024
The primary objective of this article is to study the improvement of machining efficiency of EN-31 steel by optimizing turning parameters using newly developed cutting fluids with different proportions of aloe vera gel and coconut oil, utilizing the Taguchi technique. Furthermore, performance metrics including material removal rate (MRR), surface roughness, and tool wear rate (TWR) were assessed. Analysis of variance (ANOVA) suggested that as cutting speed and feed increase, the MRR is positively influenced, but likewise tool wear is intensified. The surface roughness exhibited a positive correlation with cutting speed, and a negative correlation with increasing both cutting speed and feed. It was found that the maximum MRR value was attained at a cutting speed of 275 m/min, a feed rate of 1.00 mm/rev, and a cutting fluid composition of 30% aloe vera and 70% coconut oil. For the best surface smoothness, it is advisable to adjust the cutting speed to 350 m/min and the feed rate to 0.075
Premkumar, R., Ramesh Babu, R., Saiyathibrahim, A., Murali Krishnan, R., Vivek, R., Jatti, Vijaykumar S., Rane, Vivek S., Balaji, K.
Journal Article
Electrical Discharge MachiningAMS2549 (Current)10/11/2024
This specification provides processing and acceptance requirements for electrical discharge machining (EDM) when applied to the manufacturing of parts
AMS B Finishes Processes and Fluids Committee
Material Specification
Experimental Investigation of Electro-Discharge-Machined EN-24 and EN-42 Alloys05-18-01-000510/04/2024
The EN24 and EN42 materials were machined by the electric discharge machine (EDM). The study aimed to optimize the input variables for the multiple outputs, such as metal removal rate (MRR), tool wear rate (TWR), and surface roughness. The machining of the metal is essential to analyze the surface quality and the production rate. The MRR is a prediction of the production rate and surface roughness resembling the quality of the surface. The input variables were current (A), pulse on time (ton), and pulse duty factor (T). The three levels of current were 3A, 6A, and 9A. The ton time was selected as 30 μs, 50 μs, and 70 μs. The pulse duty factors were selected as 4, 5, and 6. The Taguchi optimization techniques are used to optimize process parameters. The L9 orthogonal array was selected for the process. ANOVA analysis was employed to check the rank of the input parameters relative to the output. The maximum MRR were at 9A, 70 μs, and 4 duty factor for the EN24. The best MRR were at 9A
Sahu, Kapil Dev, Singh, Rajnish, Chauhan, Akhilesh Kumar
Journal Article
Synthesis and Characterization of AA6351/SiC Composites Using Ball Milling Combined with Hot Extrusion05-18-01-000310/03/2024
In this investigation, AA6351 alloy matrix composites with a larger volume proportion of SiC (20 wt%) were fabricated and tested for microstructure and mechanical behavior. Composites were hot extruded from mechanically milled matrix and reinforcements. Hot extrusion uniformly distributed reinforcements in the matrix and strengthened phase interaction. Mechanical ball milling causes AA6351 powder to become more homogeneous, reducing the mean particle size from 38.66 ± 2.31 μm to 23.57 ± 2.31 μm due to particle deformation. The micrograph shows that the SiC particles are equally dispersed in the AA6351 matrix, avoiding densification and reinforcing phase integration issues during hot extrusion. In hot extrusion, SiC particles are evenly distributed in the matrix, free of pores, and have strong metallurgical bonds, resulting in a homogenous composite microstructure. SiC powders and mechanical milling increase microhardness and compressive strength, giving MMC-A 54.9% greater than AA6351
Saiyathibrahim, A., Murali Krishnan, R., Jatti, Vinaykumar S., Jatti, Ashwini V., Jatti, Savita V., Praveenkumar, V., Balaji, K.
Journal Article
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