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Surface roughness is a key factor in different machining processes and plays an important role in ergonomics, assembly process, wear and fatigue life of components. Other factors like 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 tool geometry, feed rate, depth of cut, rotational speed, lubrication, tool wear, etc. Tool vibrations during machining also have significant influence in surface roughness. In this work an attempt is made to predict the surface roughness of machined components made by the turning process by using machine learning of tool 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
S S, SafeerSadique, AnwarD, Navaneeth
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 7 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 7 exhibits
Natarajan, ManikandanPasupuleti, ThejasreeKumar, VKrishnamachary, PCSomsole, Lakshmi NarayanaSilambarasan, R
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 preliminary intention of this work is to concentrate on Cupronickel 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 ANFIS model suggested for Cupronickel provides more flexibility, efficiency, and accuracy compared to conventional approaches, allowing for enhanced monitoring and control in ECM operations
Pasupuleti, ThejasreeNatarajan, ManikandanRamesh Naik, MudeKiruthika, JothiSilambarasan, R
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly nature, affordability, and ease of processing. This study aims to optimize the parameters of Fused Deposition Modeling (FDM) for PLA material using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The researchers performed experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including dimensional accuracy, surface finish, and mechanical properties. The methodology of design of experiments (DOE) enabled a systematic exploration of parameters. The TOPSIS approach, a technique for making decisions
Natarajan, ManikandanPasupuleti, ThejasreeD, PalanisamyKatta, Lakshmi NarasimhamuSilambarasan, R
The primary issues in using pure vegetable oils for internal combustion engines are their high soot output and reduced thermal efficiency. Therefore in the present investigation, a Heavea Brasiliensis biodiesel (HBB) is used as a carbon source of fuel and ethoxy ethane as a combustion accelerator on a compression ignition (CI) engine. In this investigation, an only one cylinder, four-stroke, air-cooled DI diesel engine with a rated output of 4.4 kW at 1500 rpm was utilized. Whereas heavea brasiliensis biodiesel was delivered straightly into the cylinder at almost close to the end of compression stroke and ethoxy ethane was sprayed instantly in the intake manifold in the event of intake stroke. At various loads, the parameter of ethoxy ethane volume rate were optimised. To minimise exhaust emissions, an air plasma spray technology was employed to cover the engine combustion chamber with a thermal barrier coating. Because of its adaptability for high-temperature applications, YSZ (Yttria
Sagaya Raj, GnanaNatarajan, ManikandanPasupuleti, Thejasree
Environmental awareness is being fostered in every sector, with particular emphasis on the automotive industry. Conventional internal combustion engines are responsible for greenhouse gas emissions and health issues. Researchers are looking for alternative technologies to reduce carbon footprint and for a green environment. In this study, electric drivetrain is designed for 20% range extension and retrofitted in conventional two-wheeler. An effective control technique has been developed, thoroughly tested, and effectively implemented on the two-wheeler. The hybrid drivetrain architecture is assessed for complexities such as the required space for the battery and the location for fitting the electric motor. During low-speed conditions, the electric motor reduced the emissions and minimized fuel consumption. Consequently, the overall utilization of internal combustion engines at low-speed conditions has decreased, leading to a decrease in the vehicle's fuel consumption and exhaust gases.
Banad, Chandrashekhar BDevunuri, SureshNair, Jayashri NarayananHadagali, BalappaPrasad, Gvl
Copper Antimony Sulfide (CuSbS2) is a promising ternary semiconductor for use as an absorber layer in third-generation thin film heterojunction solar cells. This newly developed optoelectronic material offers a viable alternative to cadmium telluride (CdTe) and copper indium gallium di-selenide (Cu(In,Ga)Se2) due to its composition of inexpensive, readily available, and non-toxic elements. These films were successfully produced at an optimal substrate temperature of 533 K using the conventional spray technique. X-ray diffraction and Raman studies confirm that the films exhibit a chalcostibite structure. Characterization studies reveal that the films possess lattice parameters of a = 0.60 nm, b = 0.38 nm, and c = 1.45 nm, with an absorption coefficient of 105 cm-1 and a band gap of 1.50 eV. Notably, the films exhibit p-type conductivity. All of these studies confirm that CuSbS2 is an excellent choice for the absorber layer in solar cell applications. An attempt was made in this study to
Kumar, YB KishoreYb, KiranTarigonda, HariprasadReddy M, Surya Sekhar
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
Pasupuleti, ThejasreeNatarajan, ManikandanRaju, DhanasekarKrishnamachary, PCSilambarasan, R
India has seen a significant boost in automotive research and development, specific to Vehicle Dynamics active safety systems and ADAS. To develop these systems, without excessive reliance on full working prototypes, vehicle manufacturers are relying on virtual models to better fine tune the design parameters. For this, there is a real requirement of digital twins of the proving grounds. This virtual testing surfaces will help in reducing test costs, test times and increase iteration counts, leading to fine-tuned prototype vehicle and finally a market leading product. National Automotive Test Tracks (NATRAX) is already playing a crucial role in the testing and development of these technologies, on its test tracks. Recognizing the need to assist in virtual testing for Indian automotive manufacturers, NATRAX is taking steps to develop virtual proving grounds to complement physical testing and reduce the development time. This paper targets a comparative analysis of dynamic parameters
S J, SrihariUmorya, DivyanshPatidar, DeepeshJaiswal, Manish
Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication
Ann Josy, TessaSadique, AnwarThomas, MerlinManaf T M, AshikVr, Sreeraj
Due to energy competition and scarcity of natural gas resources in recent years, fossil fuels have been significantly replaced by renewable energy sources. Because of this, battery electric vehicles (EVs) and hybrid electric vehicles (HEVs) are getting adopted instead of internal combustion engine (ICE) vehicles. The main component of electric vehicles and hybrid vehicles is the battery management system (BMS), which is necessary to ensure that the battery pack operates efficiently, reliably, and effectively. The battery should not degrade its performance by charging and discharging too much, which can lead to serious failures if the battery is left to its end of life. This paper aims to present a novel Machine learning-based battery health estimation algorithm by mitigating risks associated with real-time battery data. This study used proprietary data collected from nickel-cobalt-aluminum (NCA) chemistry battery cells in electric vehicles. Machine learning models are trained to
Joshi, UmitaMandhana, Abhishek
Human-wildlife conflicts pose significant challenges to both conservation efforts and community well-being. As these conflicts escalate globally, innovative technologies become imperative for effective and humane management strategies. This paper presents an integrated autonomous drone solution designed to mitigate human-wildlife conflicts by leveraging technologies in drone surveillance and artificial intelligence. The proposed system consists of stationary IR cameras that are setup within the conflict prone areas, which utilizes machine learning to identify the presence of wild animals and to send the corresponding location to a drone docking station. An autonomous drone equipped with high-resolution IR cameras and sensors is deployed from the docking station to the provided location. The drone camera utilizes object detection technology to scan the specified zone to detect the animal and emit animal repelling ultrasonic sound from a device integrated to the drone to achieve non
Sadanandan, VaishnavSadique, AnwarGeorge, Angeo PradeepVinod, VishalRaveendran, Darshan Unni
In the Agricultural tractor- transmission system plays major role to transfer power from Engine to final drive through gear box enabling Forward/Reverse (F/R) movements during field operations and transportation conditions. The F/R retainer plate with idler gear, shaft is located between clutch housing and transmission gear box housing. If the retainer housing plate gets failure, then power will not be able to transfer from engine to transmission gear box main drive. In one of the tractor model retainer plate failures was observed during field testing. To simulate the failure mode from field to lab condition, the resultant forces and angle were calculated based on the drive line assembly. Resultant loads were applied on Idle gear shaft assembly through servo actuator in cyclic mode at lab. The failure was observed in the retainer plate and the location of failure was matching with field failure. CAE virtual simulation was carried out for measured load as per the laboratory boundary
V, SaravananMani, SureshKumar, SasiMore, AmitDumpa, Mahendra Reddy
Exploration vehicles on Titan are to be developed with considerations on the atmosphere present, especially the abundance of Nitrogen. This study focuses on identification of optimum materials for the propellers supporting an airship specifically created for Titan exploration. The base airship is designed to accommodate the coaxial propeller. The base of this airship is to be developed with four weather stations for collection of data samples. The stations are installed on inflatable platforms and have storage devices for recording and transmitting data collected by the aerobot. The airship will operate in Titan's atmosphere and atmospheric conditions, focusing on its design and computational analysis of structural effects and fluid dynamics. The Titan aerobot is built with a co-axial 4-blade propeller, horizontal and vertical fins, and a reaction wheel for yaw maneuvers. The co-axial propulsive system is capable of overcoming drag during steady level flight in the Titan atmosphere
Baskar, SundharVinayagam, GopinathPisharam, Akhila AjithGnanasekaran, Raj KumarRaji, Arul PrakashStanislaus Arputharaj, BeenaL, NatrayanGanesan, BalajiRaja, Vijayanandh
In automotive applications, most of the engineering components come across the material removal process in manufacturing. Face milling is one of the prominent material removal processes wherein a multi-point cutter is used to machine the flat workpiece to bring it to its required dimension. In the material removal process, the cost of the cutting tool occupies the major part of the total manufacturing cost of a product. Also, the continuous usage of the cutting tool results in tool wear. The usage of the cutting tool after the threshold value of the tool wear deteriorates the surface finish of the workpiece which leads to product rejection. Hence, optimal tool usage is inevitable. The continuous monitoring of the cutting tool condition will ensure optimal tool usage. In the present work, four real-time tool conditions are considered, namely, fresh tool (G), tool flank wear (FW), tool flaking on rake surface (FL) and tool with broken tip (B). Vibration signals are acquired while milling
D, Pradeep KumarSyed, ShaulV, MuralidharanS, Ravikumar
This research explores the use of salt gradient solar ponds (SGSPs) as an environmentally friendly and efficient method for thermal energy storage. The study focuses on the design, construction, and performance evaluation of SGSP systems integrated with reflectors, comparing their effectiveness against conventional SGSP setups without reflectors. Both experimental and numerical methods are employed to thoroughly assess the thermal behavior and energy efficiency of these systems. The findings reveal that the SGSP with reflectors (SGSP-R) achieves significantly higher temperatures across all three zones—Upper Convective Zone (UCZ), Non-Convective Zone (NCZ), and Lower Convective Zone (LCZ)—with recorded temperatures of 40.56°C, 54.2°C, and 63.1°C, respectively. These values represent an increase of 6.33%, 11.12%, and 14.26% over the temperatures observed in the conventional SGSP (SGSP-C). Furthermore, the energy efficiency improvements in the UCZ, NCZ, and LCZ for the SGSP-R are
J, Vinoth Kumar
The efficiency of combustion has a major impact on the performance and emission characteristics of a spark-ignited LPG (Liquified Petroleum Gas) engine. The shape of the combustion chamber determines the homogeneous charge intake velocity, which is crucial for the turbulent motion that encourages flame propagation and quickens combustion. It need the right amount of compression ratio, charge squish velocity and turbulent kinetic energy to sustain combustion and propel laminar flames. There are a number of names for the motion of the charge within the cylinder: swirl, squish, tumble and turbulence. All of these terms affect how air and fuel are mixed and burned. Piston shape affects in-cylinder motion, which in turn reduces fuel consumption and improves combustion characteristics. The shape of the piston quench zone has a substantial impact on the charge velocity inside the combustion chamber. The impact on charge motion was analyzed using computer modeling using STAR-CD on pentroof
Sagaya Raj, GnanaR L, KrupakaranPasupuleti, ThejasreeNatarajan, Manikandan
The paper present numerical effects of supercritical airfoil SC (2) 0414 having circular cavities at three different chord wise locations from leading to trailing edge. Here passive control method is widely applied by altering the \baseline airfoil surface coordinates to ascertain the aerodynamic behavior of the cavity at 40 %, 50 % and 60 % of the chord length respectively. The cavity shapes were deformed using Bezier curve to observe vortex pattern in the cavity region. Structured meshing was employed. The analysis was performed on SC 2 (0) 414 two-dimensional airfoil using commercial CFD ANSYS Fluent software where Spalart- Allmaras turbulence model technique is chosen to solve boundary layer problems on adverse pressure gradient and tested at extended range of angle of attack (-150 to 150) at Mach number 0.85. The study highlights the aerodynamic characteristics of lifting coefficient, drag coefficient and lift to drag ratio. It was observed that the cavity in suction surface
Pushparaj, Catherine VictoriaP, Booma DeviD, PiriadarshaniGanesan, BalajiGanesan, Santhosh KumarRaja, Vijayanandh
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 7 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 7 provides more flexibility, efficiency, and accuracy in
Natarajan, ManikandanPasupuleti, ThejasreeD, PalanisamyKiruthika, JothiSilambarasan, R
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 Cupronickel 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. The proposed prediction technique for Cupronickel is more adaptable, efficient, and accurate in comparison to current models, providing enhanced monitoring capabilities. The updated models have
Pasupuleti, ThejasreeNatarajan, ManikandanSagaya Raj, GnanaSilambarasan, RSomsole, Lakshmi Narayana
The main design objectives to be achieved in the design of HVAC cowl box includes minimizing the pressure drop and eliminating the chances of water ingress in HVAC. There are CFD tools available to study the cowl box pressure drop. However, methods available to study rain water ingress in HVAC are expensive in both mesh preparation and computational time. Using SPH (Smooth Particle based Hydrodynamics) based Preonlab tool, an attempt has been made in this work to study the design improvements of HVAC cowl box to eliminate the chances of flooding during raining. ANSYS FLUENT tool used to study the pressure drop of each design. The simulation aims to investigate the pressure drop in the cowl box and the amount of water intrusion into the HVAC module. L9 orthogonal array (factorial study) conducted to study the factors influencing the cowl box pressure drop. Inlet area, drain area and outlet area are the factors considered. Designs with segregated airflow path (adding inner duct) in the
Baskar, SubramaniyanA, BoopalshanmugamRaju, Kumar
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized the manufacturing sector by enabling the production of complex geometries using various materials. Polylactic Acid (PLA) is a biodegradable thermoplastic often used in additive manufacturing (AM) because to its eco-friendliness, cost-effectiveness, and processing simplicity. This research seeks to enhance the parameters of Fused Deposition Modeling (FDM) for PLA material with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The researchers conducted experimental trials to investigate the influence of key FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on essential outcomes such as dimensional accuracy, surface quality, and mechanical qualities. The design of experiments (DOE) technique facilitated a systematic investigation of parameters. The TOPSIS method, a decision-making tool based on several
Natarajan, ManikandanPasupuleti, ThejasreeC, NavyaKiruthika, JothiSilambarasan, R
This study focuses on developing and deploying an Unmanned Aquatic Vehicle (UAV) capable of underwater travel. The primary objectives of this project are to detect the presence of dimethyl sulfide and toluene, as well as to identify any potential oil leakage in underwater pipelines. The UAV has a maximum operating depth of 300 m below the water surface. The design of this UAV is derived from the natural design of Rhinaancylostoma, an underwater kind of fish. The maximum operational setting for this mission is fixed at a depth of approximately 300 m beneath the surface of the sea, and the choice of this species is suitable for fulfilling the objectives of this undertaking. This technology will mitigate the risk associated with human interaction in inspection processes and has the potential to encompass various other resources in the future. The initial design data of the UAV is determined using analytical processes and verified formulas. The selection of the airfoil is done by comparing
Veeraperumal Senthil Nathan, Janani PriyadharshiniRajendran, MahendranArumugam, ManikandanRaji, Arul PrakashSakthivel, PradeshMadasamy, Senthil KumarStanislaus Arputharaj, BeenaL, NatrayanRaja, Vijayanandh
This work focuses on the design and multi-parametric analysis of a designed propeller for a Pentacopter unmanned aerial vehicle (UAV). The basic and secondary design inputs, along with performance data like propeller diameter, pitch angle, chord length, and lift coefficient, are established using a standard analytical method. Approximately ten distinct airfoils, specifically NACA 2412, NACA 4109, NACA 4312, NACA 4409, NACA 4415, NACA 5317, NACA 6409, NACA 6412, NACA 23024, and NACA 25012, are evaluated over 13 Reynolds Numbers with the angle of attacks (AOA) of 20, varying from -5 to 15 degrees, for the purpose of detailed propeller design. The lift and drag coefficient values for ten distinct airfoils, utilizing a Reynolds number of 13 and 20 angles of attack, are obtained from the XFOIL software. Three sophisticated airfoils are selected from a pool of ten based on their high Lift-to-Drag (L/D) ratio performance. The selected airfoils with a high L/D ratio are NACA 6409, NACA 4109
Veeraperumal Senthil Nathan, Janani PriyadharshiniArumugam, ManikandanRajendran, MahendranSolaiappan, Senthil KumarKulandaiyappan, Naveen KumarMadasamy, Senthil KumarStanislaus Arputharaj, BeenaL, NatrayanRaja, Vijayanandh