Journal of Manufacturing Engineering <p><strong>Journal of Manufacturing Engineering</strong> is an open access international journal, peer-reviewed, quarterly-published, launched in 2005. The journal is published by <strong>Society for Manufacturing Engineers</strong> in both the paper form as well as the electronic format.</p> Journal of Manufacturing Engineering en-US Journal of Manufacturing Engineering 0973-6867 Structural and Thermal Properties of a Selected Host Crystal Lattice: Exploration of Inherent Possibilities <p>Host Ba<sub>2</sub>MgSi<sub>2</sub>O<sub>7</sub> phosphor was successfully prepared via low temperature combustion synthesis route. The phase identification of the prepared phosphor was done with the help of powder XRD technique. The XRD pattern of the phosphor revealed its monoclinic crystal symmetry with a space group C2/c. The XRD pattern have well clarified with JCPDS PDF card no. #23-0842. The average crystallite size was calculated as 42nm and crystal lattice strain size calculated as 0.24, respectively. It is acquired that the sample UV exposed for 15min gives optimum TL intensity at 112.19<sup>0</sup>C temperature and displays single TL glow peak. On the basis of TL glow curve, it can be suggested that the Ba<sub>2</sub>MgSi<sub>2</sub>O<sub>7</sub> (BMS) phosphor is an efficient host lattice but not a better TL phosphor. In our present study, we have discussed on the XRD, FESEM and thermo-luminescence (TL) characteristics as well as different kinetic parameters of this phosphor. &nbsp;</p> Sanjay Kumar Dubey Sanjay Kumar Copyright (c) 2022 Journal of Manufacturing Engineering 2022-09-01 2022-09-01 17 3 104 110 Tensile, Hardness, XRD and Surface Vonmises Stress of 316 L Stainless Steel Built by Wire Arc Additive Manufacturing (WAAM) <p class="5abscont"><span lang="EN-US">Wire arc additive manufacturing (WAAM) is a popular wire feed additive manufacturing technology that creates components through the deposition of material layer-by-layer. WAAM has become a promising alternative to conventional machining due to its high deposition rate, environmental friendliness, and cost-competitiveness. It is used to Fabricate complex shaped parts. The variable parameters are current, welding speed, shielding gas, and gas flow rate. This research fabricates 316 L stainless steel (WAAM plate) using a wire arc welding robot machine. Substrate and Side edges are removed using Microwire cut EDM, and the vertical milling machine finishes the surface. The tensile, hardness and X-ray Diffraction are compared with the standard 316 L stainless steel. The modelling and analysis of 316L stainless steel are carried out using COMSOL Multiphysics 5.3 software. It is concluded that the additive manufacturing of 316L stainless steel by wire and arc process is feasible.</span></p> Vinoth V Sathiyamurthy S Prabhakaran J Harsh Vardhan Sundaravignesh S Sanjeevi Prakash K Copyright (c) 2022 Journal of Manufacturing Engineering 2022-09-01 2022-09-01 17 3 098 103 Influence of Plasma Gas Flow Rate on the Mechanical and Microstructural Aspects of Plasma Arc Welded Titanium Alloy Joints <p>In the present investigation, the effect and role of plasma gas flow rate on the formation of microstructure during plasma arc welding of Ti<sub>6</sub>Al<sub>4</sub>V titanium alloy were studied using microscopic observation, energy dispersive spectroscopic analysis, tensile tests and microhardness measurements. Plasma gas flow rate influences the arc pressure, arc constriction, and stability. The transformation of plasma arc from conduction mode to keyhole mode causes severe changes to the microstructural characteristics of the titanium welds. This transformation takes place with slight variations of PGFR. Weld geometries increase with an increase in the PGFR. The microstructural examination shows that there are various phases formed during the variation in PGFR. Fusion zone had acicular α and widmanstätten α. Mechanical properties (i.e) strength and hardness of the joints increase with an increase in plasma gas flow rate. In the joint welded with 1 L/min, there is the formation of α-case which is an oxygen rich brittle subsurface structure and found detrimental to the ductility of the joints.</p> Pragatheswaran T Rajakumar S Balasubramanian V Copyright (c) 2022 Journal of Manufacturing Engineering 2022-09-01 2022-09-01 17 3 080 086 Design and Analysis for Heat Transfer Through Twisted Tape with Nanoparticles <p>Different techniques have been used to achieve a high heat transfer rate. Among them, one of the advanced techniques is a suspension of nanoparticles in the base fluids as water and coated with aluminum and titanium. The present work has been carried out on a double pipe heat exchanger with twisted tape insert with twist ratio (y/w = 4 and 6) and thickness (0.8mm) for heat transfer investigation of water to water and nanofluid to water with counter flow arrangement under turbulent flow conditions. The computational fluid dynamic code simulates different concentrations of nanofluid (0.01% to 0.19%) in ANSYS FLUENT R 18.1 software. The overall heat transfer coefficients for all concentrations are measured as a function of the hot and cold stream's mass flow rates. The thermal performance parameter overall heat transfer coefficient is compared for nanofluids with water. The work concludes that there is a good enhancement in heat transfer rate using nanofluid.</p> Subravel V Chandrasekar V Copyright (c) 2022 Journal of Manufacturing Engineering 2022-09-01 2022-09-01 17 3 087 090 A Comparative Study on Prediction of Cutting Force using Artificial Neural Network and Genetic Algorithm during Machining of Ti-6Al-4V <p>The purpose of this comparative study is to improve the predictive accuracy of the cutting force during the turning of Ti-6Al-4V on a lathe machine. By optimizing the machining process parameters such as cutting speed, feed rate, and depth of cut, the cutting force in the machining process can be improved significantly. Cutting force is one of the crucial characteristics that must be monitored during the cutting process in order to enhance tool life and the surface finish of the workpiece. This paper is based on the experimental dataset of cutting forces collected during the turning of titanium alloy under the Minimum Quantity Lubrication (MQL) condition. To predict the cutting forces, two machine learning techniques are explored. Firstly, a black-box model called an Artificial Neural Network (ANN) is proposed to predict cutting force. Using the Levenberg-Marquardt algorithm, a two-layered feedforward neural network is built in MATLAB to predict cutting force. The second model to be implemented was the Genetic Algorithm (GA), a white-box model. GA is an optimization technique which is based on Darwinian theories. It is a probabilistic method of searching, unlike most other search algorithms, which require definite inputs. Using symbolic regression in HeuristicLab, a GA model is developed to estimate cutting force. The anticipated values of cutting forces for both models were compared. Since the ANN model had fewer errors, it was ascertained that the particular model is preferable for machining process optimization.</p> Rolvin Barreto Malagi R R Chougula S R Copyright (c) 2022 Journal of Manufacturing Engineering 2022-09-01 2022-09-01 17 3 091 097