bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison on machining characteristics of AISI 304 steel during micro and nano powder mixed electrical discharge machining process
Authors: Rajesh J V , Singh Kushpreet , Giridharan Abimannan 
Published: September 2020

Abstract
The experimental analysis on the influence of duty factor and powder particle size on discharge energy, material removal rate and surface integrity is made during powder mixed electrical discharge machining process. Molybdenum-di-sulfide (MoS2) powder particles of two different size viz. 40μm (micro) and 90 nm (nano) are used. During experimentation duty factor is varied as 2, 4, 6, 8, 10 and 12. A comparative evaluation on the effect of using two size of molybdenum-di-sulfide powder particle in dielectric medium is studied. For all duty factors, micro powder offered high discharge energy and better surface finish compared to that of nano powder. Nano powder offered high material removal rate compared to that of micro powder. The occurrence of series discharge during powder mixed electrical discharge machining is observed using pulse train generated in gap and using scanning electron microscope image of machined surface. The e energy dispersive X-ray spectroscopy analysis of machined surface reveals the deposition of molybdenum-di-sulphide from dielectric medium for both micro and nano powder mixed electrical discharge machining process.
Optimization approaches of industrial serial manipulators to improve energy efficiency: A review
Authors: Lakshmi Srinivas G , Javed Arshad 
Published: September 2020

Abstract
The objective of this paper is to provide a comprehensive review of existing approaches and techniques developed in the field of industrial robotics to make it energy efficient. The usage of industrial manipulators is increasing globally due to its repeatability and superiority. The considerable energy demand is also predicted because of the increased application of robots in industries, this can be overcome by making energy-efficient manipulators. Different approaches are already established in this field to make robots energy efficient such as; elimination of needless densities by topology optimization, selection of optimal path using trajectory optimization, usage of light-weight components, analysing speed, and providing energy storage devices, etc. Among different available approaches, major three methodologies are reviewed in this paper. The first group comprises the topology optimization method that optimizes the design space by eliminating needless densities based on given boundary conditions and constraints. Further, the topology optimization method sub-classified into two types based on loading conditions such as static analysis and dynamic analysis. The second group comprises trajectory optimization of robots; this is achieved by selecting the optimal path of the work-cycle, or minimization of torque. Again trajectory optimization classified into point-to-point and multi-point optimization. The third group includes the application of light-weight structural components in the field of industrial robotics and its challenges. Presented work will be useful to analyze different approaches to make the robot as energy efficient.
Selection of optimal capacity for reconfigurable machines during lean manufacturing
Authors: Pattanaik L N , Munda Pooja , Jyoti  , Kumar Amit 
Published: September 2020

Abstract
Presently manufacturing industries are facing the challenge of increased market volatility and unpredictability. Capacity control is an effective measure to address this problem by utilizing the flexibility in the plant capacity. This research paper proposes a genetic algorithm (GA) based methodology to optimally select levels of capacity for machines which are reconfigurable in nature. The objective function for the optimization problem is minimization of an error with takt time. Takt as a key feature of lean manufacturing is considered in the model to manufacture the products in a fixed rate as decided by their respective demands and available production time. A hypothetical numerical problem is illustrated to describe the selection methodology, GA computation steps and the solution procedure.
Effect of hot corrosion demeanour on aerospace-grade Hastelloy X made by pulsed and constant current arc welding in molten salts at 820 °C
Authors: Sathishkumar M , Subramani P , Natesh M , Venkateshkannan M , Arivazhagan N , Manikandan M 
Published: September 2020

Abstract
The effect of hot corrosion tendency of Hastelloy X weldment made by pulsed current tungsten inert gas (PCTIG) and constant current tungsten inert gas (CCTIG) welding has been studied. The welding has been done using ERNiCrCoMo-1 (Mo-1) filler. The hot corrosion demeanour of the Hastelloy X has studied in two different molten salt circumstance such as MS-1 (molten salt-1) (75Na2SO4 + 20V2O5 + 5NaCl) and MS-2 (molten salt-2) (75Na2SO4 + 25V2O5) for 25 cycles (one hour in each cycle) at 820 °C. The MS-1 substrate shows the maximum weight gain compared to MS-2. Also, the largest parabolic constant (Kp) is observed for PCTIG MS-1. The existence of NaCl in MS-1 has improved the corrosion rate by generating more spallation and exfoliation on the surface. The trends of hot corrosion resistance are following the order of, CCTIG Mo-1 MS-1 <Base Metal MS-1 <PCTIG Mo-1 MS-1 <Base Metal Mo-1 MS-2 <CCTIG MS-2 < PCTIG Mo-1 MS-2. The smaller grain size and higher protective oxides (NiO, NiFe2O4, NiCr2O4, and Cr2O3) developed in PCTIG MS-1 and MS-2 improves the corrosion resistance compared to coarser gains and fewer protective oxides in CCTIG MS-1 and MS-2, respectively. The non-protective oxides (Fe2O3 and MoO3) improved the corrosion rate of the substrate. From this investigation, it is clear that PCTIG weldment in the MS-2 environment is providing better protection to corrosion at 820 °C.
Condition based monitoring for fault detection in windmill gear box using artificial neural network
Authors: Abishekraj N , Jeeva Prashanna G R , Suriyaa M S , Barathraj T , Mohanraj D 
Published: September 2020

Abstract
Gears are condemning element in a diverse industrial application such as machine tool and gearboxes. Many contributing losses occur due to an unexpected breakdown of the gears. Recently, in many researches fault diagonsis has been the most contributing content in the gears. Vibration analysis has been used as a deciding tool for several machinery maintenance decisions. The most efficient form of indication is by the increment in the vibration level. By measuring and analysing the machine’s vibration, it is possible to determine both the nature and severity of the defect, and hence predict the machine’s failure. The vibration signal of a gearbox carries the signature of the fault in the gears and early fault detection of the gearbox is possible by analysing the vibration signal using different signal processing techniques. The present work depicts about the predictive maintenance program implementation based on the experimental data set of the gear box assembly in wind turbines by using the Artificial neural network and comprises of the prediction of variation in the accuracy of the experimental data set. The code values are generated using python 3.7 version software. The accuracy of the Model was found to be 61.86%. This accuracy indicates the functioning of the gears and can also be used for predictions in real time scenario.
Design and Development of Bio-Sensitive Robotic Arm using Gesture Control
Authors: Bakshi Subhodeep , Ingale Karan , Jain Atishay , Karuppudiyan S 
Published: September 2020

Abstract
In this paper, a model of a robotic arm is designed and manufactured using a 3D printer, which is to be operated via human gesture by using the Accelerometer and Gyroscope Module. This arm is proposed to help in the medical field, in military operations, in hazardous conditions and in industries to maximize human safety. The robot arm is designed such that, it has 5 degrees of freedom controlled by the 3-axis accelerometer mounted on the IC placed on the glove of the user. The module will replicate the movement of the user’s hand to extend, retract, and rotate accordingly to accurately position as required for the application. The user’s finger action is used to manipulate the working of the gripper. Therefore, gesture control in a broad sense is a computerized interface that allows computers to record and interpret those gestures into commands to adhere to actions.
Development and Workspace Analysis of Smart Actuation based Planar Parallel Robotic Motion Stage
Authors: Singh Deep , Choudhury Rutupurna , Singh Yogesh , Mukherjee Manidipto 
Published: September 2020

Abstract
The applications of light weight planar parallel robotic manipulators are increasing enormously because of their various desirable characteristics such as low weight, lower inertia, higher stiffness, energy efficiency etc. Higher accelerations and accuracies can be achieved using planar parallel manipulators (PPMs). Also, Shape Memory Alloy (SMA) based actuators are replacing huge and bulky actuators as SMA actuators provide higher work per unit mass as compared to other actuators and also undergoes silent actuation by simple actuation process. In the present study, three configurations (3PRP, PPR-2PRP and PRR-2PRP) of 3 degrees of freedom U-shaped base planar parallel robotic manipulator have been presented using SMA based smart actuators. The manipulator constitutes of one fixed base and one moving platform. The SMA spring acts as linear actuators mounted on the base which is connected to the end effector with the help of three limbs. A detailed experimental study was carried out to understand the workspace associated with each configuration. The movement of manipulators was investigated with the various combinations associated with the input SMA translation joints. And the study showed that the various configurations acquire different areas of workspace in the manipulator region. This study also shows the essence of SMA springs in PPMs for precise and accurate positioning of the end-effector and also its tracking ability for the development of future micro-motion stage. Note: P refers to Passive Prismatic Joint, R refers to Passive Revolute Joint and P refers to Active Prismatic Joint.
Identification of tool wear status and correlation of chip morphology in micro-end milling of mild steel (SAE 1017) using acoustic emission signal
Authors: Prakash M , Kanthababu M , Arul Jeya Kumar A , Arun N , Prasanna Venkadesan V 
Published: September 2020

Abstract
This study describes the identification of micro-end mill wear by means of acoustic emission (AE) signals received from an AE sensor during the micro-end milling (slot milling) of mild steel. The obtained AE signals were processed in the time-domain to compute root mean square (RMS) and dominant frequency and amplitude are obtained from frequency-domain. The RMS value shows the linear trend with the tool wear, and helps to classify the tool wear regions, such as initial, progressive and accelerated wear regions. The Welch power spectral density and spectrogram (short term Fourier transform) analysis help to identify the tool rotational, tool passing and machining frequencies. The discrete wavelet transformation (DWT) technique is used to discretize the AE signal in to five frequency ranges. AE specific energy was obtained from the discretized AE signals. The AE specific energy indicated that a combined type of material removal mechanism occurred in micro-end milling, similar to the macro-end milling. However, ploughing is also observed from the surface topography. Chip structures are also studied and correlated with the micro-end mill wear for tool wear identification.
Experimental investigation and modeling of drilling process parameters for surface roughness of Al6061/SiC/Gr hybrid composite using RSM
Authors: Sai Tarun Kumar P , Muniappan A. 
Published: September 2020

Abstract
Surface quality and material removal rate (MRR) are two significant factors in the manufacturing which influence precision thus mirrors the productivity of the industry. Objective of this work is motivated to determine the optimum operating parameters for the drilling Aluminum hybrid composite. The metal hybrid composite of Aluminum was fabricated by stir casting process. Silicon carbide and graphite particles were added to improve material properties. The main cutting parameters, namely, spindle swiftness, feed amount and type of coolant are considered in this study. In this work, the experimentations were planned by a Box-Behnken design (BBD) method. The impact of procedure parameters on the reactions are assessed and ideal cutting conditions for limiting surface roughness and boost the MRR were resolved utilizing reaction tables, reaction charts, association tables, 3-D surface plots and attractive quality investigation. To approve, affirmation tests have been completed and anticipated outcomes have been seen as in great concurrence with experimental discoveries.
Deep Learning – A Review
Authors: Kota Vamsi Madhav , Manoj Kumar V , Bharatiraja C 
Published: September 2020

Abstract
In recent years, tech giants in various parts are showing Curiosity on Artificial Intelligence by investment on the project that can be a game-changer for both corporate and researchers. A company such as Google, Baidu, and Yandex have already started their multimillion-dollar project in this pitch. This article presents the latest progress and also tries to paint a predictive picture of future research directions and developments in the domain of deep learning. Each of the said research directions and avenues is analyzed and summarized in a brief yet concise manner in this article. Initially, an outline of the three elementary models of deep learning that including multilayer perceptions and perceptrons, convolutional neural networks and recurrent neural networks. Building on the bases of foundation, further analyses of the emerging new types of convolutional neural networks and recurrent neural networks are also undertaken in this current study. This article then summarizes deep learning and its applications in the domain of artificial intelligence, counting speech processing, computer vision, and natural language processing. Finally, the purpose of deep learning is discussed. The current article also delves a little deeper into the inner workings of the neural networking architecture associated with object detection and computer vision.
Microstructure and Mechanical Behaviour of AZ91d Magnesium Composite Reinforced With B4C and Graphite by Casting Process
Authors: Aatthisugan I , Razal Rose A 
Published: September 2020

Abstract
In this study, we have manufactured AZ91D magnesium - based alloy and its composite reinforced with boron carbide (B4C) and Graphite (Gr) at various weight percentages by bottom pouring stir casting process. The work is aimed at comparing the microstructure and mechanical properties of developed composites with its matrix AZ91D magnesium alloy. The B4C reinforcement particles were added 1.5, 3, 4.5 weight percentage respectively to the matrix material and also solid lubricant graphite was added 1.5 weight percentage into composite. Density, Porosity, Rockwell hardness test, tensile test, and Optical Microstructure were evaluated. The developed composites reveal increased 11.18% hardness value when compared to base material, which could be accredited to the occurrence of B4C particles. Also, this study indicates that increase in percentage addition of B4C and graphite leads to increase in density and tensile strength of the developed composites. Optical microstructure images show the uniform distribution of particles into matrix.
Effect of Sintering Temperature on Microstructure and Mechanical Properties of Aluminium Composites
Authors: Aatthisugan I , Banerjee Aishik , Hasan Mohammed Moinuddin , Patel Aditya Singh , Pushkar Kumar , Dey Suraj , Sohail Saud Bin , Khateeb Syed Mohammad , Das Animesh 
Published: September 2020

Abstract
Sintering temperature might have varied effects on the properties of composites in general. Through this paper, an attempt has been made to investigate the effect of sintering temperature on the properties of Aluminium composites fabricated by powder metallurgy process. Alumina and Silicon Carbide were different types of reinforcements in this work. Green compacts of Aluminium composites were made at a compressing load of 1 tonne and 2 tonne separately. These compacts were sintered at 2 different sintering temperatures of 400°C and 450°C in oxygen free environment using muffle furnace for 1 hour, followed by annealing process which took 12 hours. Sintered compacts were than subjected to micro-structural examination and mechanical properties evaluation. Higher hardness has been attained for the composites containing 2.5% Silicon Carbide. Optical microstructure images show the uniform distribution of particles into aluminium matrix.
Effects of grinding α–β Titanium with 3% CNTs in CBN grinding wheel: An Experimental Study
Authors: Stephen Deborah Serenade , Sethuramalingam Prabhu 
Published: September 2020

Abstract
Carbon Nano Tubes (CNTs) have found an immovable niche in the world of manufacturing in the form of reinforcements in composites. Their many advantageous properties have allowed their usage in innumerable ways and this has led to a plethora of innovations in engineering. In advanced manufacturing of very-hard-to-machine materials like titanium, where surface integrity is of prime importance, the presence of multi-walled CNTs even as less as 3% alongside Cubic Boron Nitride (CBN) in grinding wheels, has demonstrated improved surface finish on Titanium alloy (Ti-6Al-4V). This proposed grinding wheel for use on Titanium, reduces surface roughness and improves Material Removal Rate (MRR) on the Titanium work pieces. It also shows slight improvement in the contour of the wheel.
Enhancement of surface quality in Wire EDM machining of Magnesium alloy using ANN modeling approach
Authors: Sreenivasulu M. , Muniappan A. , Bharathiraja G. , Karunagaran N. 
Published: September 2020

Abstract
Wire electrical discharge machining is an sophisticated technology that contracts with very high speed cutting performance characteristics and precision machining process. In This paper deals about the surface quality enhancement by demonstrating various device parameters using ANN modeling for wire EDM process and the work piece required for machining was magnesium mix alloy. For exploratory game plan the machining parameters for example pulse off time, pulse on time, wire feed and current(voltage) were considered. The experiment was organized by Response Surface Methodology (RSM), Central Composite Design (CCD) was used. Surface quality was foreseen by ANN modeling. Different enlistment limits were utilized to improve the methodology parameters for surface quality. The foreseen characteristics were uncommonly near the test regard and the best assortment was 4.3%. Confirmation test was moreover coordinated to endorse the results and ANN predicted results have been seen as in exceptional concurrence with test revelations.
Influence of Shot Peening on Surface Characteristics of Case Hardened SAE9310 Gear Material
Authors: Karthick S , Oliver Nesaraj S 
Published: September 2020

Abstract
The shot peening is the cold working process by which the compressive residual stresses are induced in the component, and that increases surface hardness, which can be helpful to avoid the fatigue failure of the component. Shot peening process is using to improve the fatigue life of the automobile and aerospace components. Effectiveness of the shot peening in surface characteristics can be measured by the Almen Intensity, Surface hardness and Surface roughness. The impact of shot peening on case-hardened differential bevel gear is defined in this study. In the shot peening process, the surface of the gear rammed by the small hardened cut wire shots with high velocity and overlapping indentations are created on the gear surface. Hence, thecharacteristics of surface of the gear can be greatly improved by shot peening. For bevel gears, bending fatigue crack is the major failure mode. This study is discussed to enhance the mechanical and surface properties of SAE9310 bevel gear by using the shot peening process. The optimization of shot peening process is done by including multi-performance characteristics i.e. Intensity, Surface hardness and surface roughness of the bevel gear. The analysis includes air pressure, nozzle distance, shot flow rate, exposure time as process parameters. The main objective of this study to decide the optimized shot peening process parameters for required performance characteristics and most influencing factors on surface layer characteristics. The experiments have been conducted using Taguchi’s L9 orthogonal array design techniques.
Application of principles of a Artificial Intelligence in Mechanical Engineering
Authors: Anush Lakshman S. , Ebenezer D. 
Published: September 2020

Abstract
As the world is tuning smart in every possible way, various engineering problems and processes are being made smart by the use of Artificial Intelligence (AI). As various AI methods are being tried in variety of applications in mechanical engineering like fault detection, autonomous vehicles, manufacturing, smart buildings etc., it becomes essential to survey and understand their suitability and performance to heighten the use of AI. Hence, this paper aims to survey the use of AI methods like artificial neural networks, genetic algorithm, deep learning, fuzzy logic, hybrid technique and case-based reasoning in some of the mechanical engineering applications and attempt to find out which AI method suits better for the applications discussed.
Surface roughness evaluation in machining titanium alloys using non-textured and textured cutting inserts
Authors: Abishekraj N. , Gowtham T. , Krishnaraj V. , Bibeye Jahaziel R. , Geetha Priyadarshini B. 
Published: September 2020

Abstract
Ti-6Al-4V alloy is well known for its difficulty in machining due to its ultimate hardness. The temperature generated at the tool-chip interface is typically high which results in poor surface finish and rapid tool wear. The current focus of work was to analyze the influence of response parameters such as cutting speed, feed rate and depth of cut on the surface roughness on the non-textured and textured cutting inserts. Response Surface Methodology (RSM) was utilized to generate the analytical relation between the process parameters and their interaction with the responses namely surface roughness. Analysis of Variance (ANOVA) was implemented to correlate with the significant factors and the correctness of the model. The machining was done experimentally using the textured and non-textured tools and the surface roughness was measured to analyze the influence of process parameters. It was found that the surface roughness was immensely reduced while using microtextured cutting tools.