Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Manufacturing Engineering

School of Mechanical Engineering

Vellore Campus

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Advances in Materials Science and Engineering

A Review on Machine Learning Models in Injection Molding Machines

Phase Change Materials in Metal Casting Processes: A Critical Review and Future Possibilities

Journal of the Electrochemical Society

Review - Contemporary Progresses in Carbon-Based Electrode Material in Li-S Batteries

Materials Research Express

Performance of air plasma sprayed Cr3C2-25NiCr and NiCrMoNb coated X8CrNiMoVNb16-13 alloy subjected to high temperature corrosion environment

Evolution and recent developments of high performance electrode material for supercapacitors: A review

Journal of Energy Storage

Evolution and recent developments of high performance electrode material for supercapacitors: A review

Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimum temperature using backpropagation algorithms. The favourite ML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics.

School of Computer Science and Engineering

CMES-Computer Modeling in Engineering & Sciences

A Contemporary Review on Drought Modeling Using Machine Learning Approaches

In the recent trends, production plants in the automobile industries all over the world are facing a lot of challenges to achieve better productivity and customer satisfaction due to increasing the passenger’s necessity and demand for transportation. In this direction, the belt, tyre, and tube manufacturing plants act as vital roles in the day-to-day life of the automobile industries. Tyre production plant comprises five major units, namely, raw material selection, preparation, tyre components, finishing, and inspection. *e main purpose of this research is to implement the new method to predict the most critical subsystems in the tyre manufacturing system of the rubber industry. As mathematically, any one maintenance parameter among reliability, availability, maintainability, and dependability (RAMD) parameters is evaluated to identify the critical subsystems and their effect on the effectiveness of the tyre production system. In this research, the effect of variation in maintenance indices, RAMD, is measured to identify the critical subsystem of the tyre production system based on the mathematical modeling Markov birth-death approach (MBDA), and the equations of the subsystems are derived by using the Chapman–Kolmogorov method. Besides, it also calculates the performance of certain maintenance parameters concerning time such as mean time between failures (MTBF), mean time to repair (MTTR), and dependability ratio for each subsystem of the tyre production system. Finally, RAMD analysis of the tyre production systems has been executed for predicting the most critical subsystem by changing the rates of failure and repair of individual subsystems with the utilization of MATLAB software. RAMD analysis reveals that the subsystem bias cutting is most critical withtheminimumavailabilityof0.8387,dependability 5.19, dependabilityratio0.8701, andmaximum MTTR38.46 hours ofthe subsystem.Inthisimplementation ofthe proposedmethod,areal-time casestudyofthe industrialrepairablesystemoftyre manufacturing system has been taken for evaluating RAMD indices of the production plant of rubber industry cited in the southern region of Tamil Nadu India

Mathematical Problems in Engineering

Performance Analysis of Tyre Manufacturing System in the SMEs Using RAMD Approach

Prosthetics are widely used to help people with permanent disabilities and provide a new chance to lead a normal and independent life. Prosthetics are customized for each individual, keeping in mind the use and physical attributes. However, the conventional prosthetics are heavier and the production is expensive and time consuming. The present study addresses this problem and proposes an alternative design for prosthetic foot and calf. The weight of the prosthetics is minimized with a hybrid optimisation approach using generative design and topology optimization. The proposed design has a <a href="https://www.sciencedirect.com/topics/materials-science/compliant-mechanisms">compliant mechanism</a> to minimize the load and it provides ease to the wearer. The designed prosthetic was simulated while walking to analyse the stress distribution. The study concludes that the wearer will feel comfortable with the new design, and will be able to walk naturally.

materials today proceedings

<a href="https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=Sm2CrvQAAAAJ&amp;citation_for_view=Sm2CrvQAAAAJ:KlAtU1dfN6UC">Optimization of Prosthetic Leg Using Generative Design and Compliant Mechanism</a>

Hastelloy C-276 nickel alloy is widely preferred for nuclear power plant applications where steam generators operate at 600 °C. However, stainless steels are still used for tubes in steam gas reformers which necessitates welds between dissimilar materials. In this study, hastelloy C-276 and SS 316 L alloys were joined using GTAW process. The welding parameters of GTAW were optimized to minimize the defects in samples of similar (SS 316 L-SS 316 L, HAS C-276-HAS C-276), and dissimilar (HAS C-276-SS 316 L) joints. SEM/EDS results confirm the presence of columnar dendritic and cellular structure in fusion zone for dissimilar weldments (HAS C-276-SS 316 L). EDS results also revealed the presence of larger amount of Mo, W and lower amount of Ni in the subgrain boundary as compared to the subgrain body. Compared to the similar (HAS C-276- Has C-276) and dissimilar (HAS C-276-SS 316 L) welded joints FZs, the enrichment of Mo is lesser in SS 316 L-SS 316 L similar welded joints. Higher UTS was obtained for dissimilar (HAS C-276-SS 316 L) GTAW joints 780 MPa compared to the similar welded joints of SS316 (552 MPa) and HAS C-276 (740 MPa). The average microhardness for the dissimilar SS 316 L and HAS C-276 is 220 Hv, which is higher compared to the similar welded joints, SS 316 L and HAS C-276, i.e., 190 Hv and 210 Hv, respectively. This study shows that GTAW process is suitable to fabricate sound weld joints of similar (SS 316 L- SS 316 L, HAS C-276-HAS C-276), and dissimilar (HAS C-276-SS 316 L) alloys which are useful for the nuclear power plant boilers. © 2021, The Indian Institute of Metals - IIM.

Transactions of the Indian Institute of Metals

Mechanical and Microstructural Characteristics of Gas Tungsten Arc Welded Similar and Dissimilar Joints of SS-316 L and Hastelloy C276

In the present study, Fe-42% Ni, Fe-2% Y2O3, and Fe-42% Ni-2% Y2O3 (30-60 nm) were prepared by mechanical alloying using high energy SPEX8000M ball mill. Consolidation of the milled sample was done by spark plasma sintering (SPS) at 800, 900, and 1000 °C at a pressure of 60 MPa with a holding period of 5 min in an argon atmosphere. The sintered density was found to be higher at a higher temperature, e.g., 98% at 1000 °C as compared to 78% at 800 °C. The addition of nanosize Y2O3 in Fe or Fe-Ni system showed a strong influence on the formation of intermetallic compounds and final grain size of the resultant material. x-ray diffraction analysis of the sintered Fe-42% Ni-2% Y2O3 alloy indicates the presence of Fe-Ni phase with additional peaks of intermetallic phases (Fe17Y2, Ni5Y) and oxides (Fe3O4, NiO). The nanoindentation hardness of the sintered (at 1000 °C) alloys was found to be the maximum for the Fe-42% Ni-2% Y2O3 (7.9 GPa), followed by Fe-2% Y2O3 (7.2 GPa) and Fe-42% Ni alloy (5.8 GPa). Dry sliding wear resistance of the spark plasma sintered (SPSed) (sintered at 1000 °C) Fe-42% Ni-2% Y2O3 was found to improve due to higher hardness and formation of oxide rich layer at the contact surface. Moreover, the SPSed specimen of the Fe-42% Ni-2% Y2O3 alloy showed a better corrosion resistance (Icorr = 0.78 μA/cm2) as compared to the corrosion resistance (Icorr = 1.34 μA/cm2) of the Fe-42% Ni alloy, both sintered at 1000 °C. The corrosion tests were conducted in a freely aerated 3.5% solution of NaCl electrolyte. A significant amount of grain refinement (due to the addition of nanosize yttria), formation of intermetallic phases (Fe17Y2, Ni5Y) as well as uniform distribution of fine oxide particles (Fe3O4, NiO) played the pivotal role in the improvement of wear, corrosion and mechanical properties of the SPSed Fe-42% Ni-2% Y2O3 alloy. © 2021, ASM International.