Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

Inconel 625 is a solid-solution strengthened nickel-based superalloy that found widespread usage in aerospace, marine, and nuclear applications due to its outstanding mechanical properties. Thus in the current study, the machining performance of Inconel 625 under nano-minimum quantity lubrication (nMQL) environment is investigated. Graphene nanoparticles were selected to enrich the physicochemical properties of coconut oil as a cutting fluid. Experiments were conducted using AlTiN PVD coated carbide cutting tool inserts under stable nano-cutting fluids of various concentrations and at different levels of cutting speed, feed, and depth of cut. The experiments were conducted according to Taguchi’s L16 orthogonal design. Surface roughness, tool flank wear, and material removal rate were selected as responses to study the effects of cooling conditions and machining parameters. The turning process performance was optimized by the grey relational multi-objective optimization method. Cutting speed of 80 m/min, feed of 0.05 mm/rev, depth of cut of 0.8 mm, and cooling condition with 0.50 wt. % graphene nanofluid MQL was established as the optimal machining conditions. Analysis of variance (ANOVA) was performed to identify the importance of input parameters on machining performance and the results indicate that feed and cooling conditions are the most influential parameters. Also, the tool wear mechanisms and chip morphology were identified and analyzed using SEM and EDS to understand the cutting tool performance under each cooling condition by considering the optimal machining parameters. The lesser tool wear and better chip morphology were obtained under 0.50 wt. % graphene nano-cutting fluid environment. © 2021, King Fahd University of Petroleum & Minerals.

World University root node

Department of Mechanical Engineering

Arabian Journal for Science and Engineering

Investigation on the Effect of Graphene Nano-cutting Fluid Minimum Quantity Lubrication on the Machining Performance of Inconel 625

This study aims to create an image processing algorithm that categorises the wire electric discharge machine (WEDM) processed finish cut surfaces, based on surface microdefects. The algorithm also detects the defect locations and suggests alternate parameter settings for improving the surface integrity. The proposed automated analysis is more precise, efficient and repeatable compared to manual inspection. Also, the method can be used for automatic data generation to suggest parameter changes in closed loop systems. During the training phase, mean, standard deviation and defect area fraction of enhanced binary images are extracted and stored. The training dataset consists of 27 WEDM finish cut surface images with labels, ‘coarse', ‘average' and ‘smooth'. The trained model is capable of categorising any machined surface by detecting the microdefects. If the machined surface image is not classified as a smooth image, then alternate input parameter settings will be suggested by the model to minimise the microdefects. This is done based on the Euclidean distance between the current image datapoint and the nearest ‘smooth' class datapoint.

Journal of Micromanufacturing

Image processing algorithm for detection, quantification and classification of microdefects in wire electric discharge machined precision finish cut surfaces

The production of cement is a carbon dioxide-intensive process. Replacing ordinary Portland cement (OPC) with industrial by-products can bring down the carbon footprint associated with cement production. Various industrial residues are currently used as alternative cementitious materials in this regard. However, developing a low carbon dioxide composite cement with different pozzolans alters the packing density, which influences its properties. Although studies have been conducted on the use of fly ash and slag at lower cement replacement levels, studies on the packing density and strength of ternary and quaternary composite cements with higher replacement levels are limited. In this study, fly ash, blast-furnace slag, ultra-fine fly ash and ultra-fine slag are used as a partial replacement for cement in various proportions. Out of the 51 mixtures tested in the study, 11 combinations were selected, based on the maximum packing density, for further investigations on fresh and hardened properties to arrive at the best trade-off between cement reduction and desired properties. The early-Age strength is influenced by the packing density of composite cements, whereas the later-Age strength is found to be highly governed by the amount of OPC and the pozzolanic potential of the industrial by-products. © 2022 ICE Publishing: All rights reserved.

Department of Civil Engineering

Proceedings of the Institution of Civil Engineers: Engineering Sustainability

Composite cements: Synergistic effects of particle packing and pozzolanicity

Use of blended cement with industrial by-products as supplementary cementitious materials is a sustainable alternative to carbon-intensive conventional cements. Although the potential of several new alternative cementitious materials has been reported in the literature, their industrial acceptance is minimal compared to fly ash and slag. In addition to performance evaluation in concrete, it is essential to compare the performance of new alternative cementitious materials with other widely recognised materials in industries to facilitate their acceptance. Therefore, the present study focuses on a thorough review of the characteristics and performance of bagasse ash in concrete compared to fly ash and slag blended concrete. Moreover, fresh, hardened and durability characteristics of bagasse ash blended concrete are critically reviewed. Based on a comprehensive review, the optimum replacement levels for bagasse ash, fly ash and slag are found to be 20%, 30% and 50% respectively. Even though adverse effects such as delayed initial set, workability reduction and increased consistency were observed for bagasse ash blended cements, the strength gain of bagasse ash blended concrete is found to be better than fly ash/slag blended concrete. Significant enhancement in resistance against chloride and water permeability is also reported for bagasse ash blended concrete compared to fly ash and slag blended concrete. © 2020 Elsevier Ltd

Journal of Cleaner Production

Comparison of sugarcane bagasse ash with fly ash and slag: An approach towards industrial acceptance of sugar industry waste in cleaner production of cement

In a recently published work (Sheng, 2020), Sheng identified inconsistencies in the concepts and principles leading to the Navier–Stokes equations and proposed a new asymmetric “friction tensor” to resolve these inconsistencies. We show that this friction tensor can be interpreted as the non-isotropic part of a vorticity-influenced viscous stress tensor following the analysis in Berdahl and Strang (1986). The asymmetric stress tensor however violates the principles of mechanical equilibrium in fluid flows that are free of external or body couples and is therefore non-physical in classical continuum hydrodynamics. We refute the arguments in favour of the asymmetric stress tensor in Sheng (2020) whilst also demonstrating that the friction tensor leads to the incorrect form of the Navier–Stokes equations in non-Cartesian co-ordinates and its ability to recover the correct governing equations in Cartesian co-ordinates is fortuitous. © 2020 Elsevier Masson SAS

European Journal of Mechanics, B/Fluids

Critique on “A revisit of Navier–Stokes equation”

Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this difficulty is the non-intuitive nature of the GAN loss curves, which necessitates a subjective evaluation of the generated output to infer training progress. Recently, motivated by game theory, duality gap has been proposed as a domain agnostic measure to monitor GAN training. However, it is restricted to the setting when the GAN converges to a Nash equilibrium. But GANs need not always converge to a Nash equilibrium to model the data distribution. In this work, we extend the notion of duality gap to proximal duality gap that is applicable to the general context of training GANs where Nash equilibria may not exist. We show theoretically that the proximal duality gap is capable of monitoring the convergence of GANs to a wider spectrum of equilibria that subsumes Nash equilibria. We also theoretically establish the relationship between the proximal duality gap and the divergence between the real and generated data distributions for different GAN formulations. Our results provide new insights into the nature of GAN convergence. Finally, we validate experimentally the usefulness of proximal duality gap for monitoring and influencing GAN training.

Department of Data Science

arXiv preprint arXiv:2105.

On Characterizing GAN Convergence Through Proximal Duality Gap

Meta-learning (ML) has emerged as a promising learning method under resource constraints such as few-shot learning. ML approaches typically propose a methodology to learn generalizable models. In this work-in-progress paper, we put the recent ML approaches to a stress test to discover their limitations. Precisely, we measure the performance of ML approaches for few-shot learning against increasing task complexity. Our results show a quick degradation in the performance of initialization strategies for ML (MAML, TAML and MetaSGD), while surprisingly, approaches that use an optimization strategy (MetaLSTM) perform significantly better. We further demonstrate the effectiveness of an optimization strategy for ML (MetaLSTM++) trained in a MAML manner over a pure optimization strategy. Our experiments also show that the optimization strategies for ML achieve higher transferability from simple to complex tasks.

arXiv preprint arXiv:2101.

Stress Testing of Meta-learning Approaches for Few-shot Learning

Additive manufacturing has advantages of fabricating complex parts at very greater speed with complete freedom for designer. LENSTMis an additive manufacturing that uses 3D CAD model for depositing metal products directly from metal powder. The effect of process parameters like Laser Power, Scan speed, powder flow rate and working distance on Porosity, instrumented Vickers Hardness and Bulk Hardness Top and Bottom layers for LENSTMdeposited components were investigated and the values are compared with wrought alloy. The density and Hardness obtained from LENSTMSamples are Higher than that of Conventional Methods. The Laser Power was significant factor according to Taguchi Analysis. © 2021 Elsevier Ltd. All rights reserved.

Materials Today: Proceedings

Effect of process parameters on micro hardness, bulk hardness and porosity of LENSTMdeposited SS 316L alloy

A simple computational model was created to predict the vaporization rates of droplets of urea-water-solution (UWS) evaporating in a hot air stream. A single-component evaporation model, which was based on Abramzon-Sirignano's vaporization model, was adapted to handle UWS droplets and the governing equations were solved numerically in MATLAB®. The temperature and species concentrations within the droplet were assumed to be uniform and various methods available in literature were employed to estimate important thermophysical properties such as saturation vapor pressures, partial pressures, vaporization enthalpy and vapor diffusivities. The suitability and limitations of the computational model were assessed by comparing the results with experimental data on UWS droplets evaporating under forced convective conditions. The temperatures considered in this study ranged from 373 K to 673 K and the corresponding relative air velocities were between 1.5 m/s and 4.3 m/s. The model was found to be able to capture the two-stage vaporization behavior of the UWS droplet (except crystallization, puffing and micro-explosion) with reasonable accuracy. While the first-stage vaporization rates, predicted by the model, were accurate to within 1.8% to 17.7%, the accuracies of the second-stage vaporization rates were significantly dependent on the methods used to estimate fluid properties such as the vapor pressures and the vaporization enthalpy. © 2020 Elsevier Ltd

International Journal of Heat and Mass Transfer

A computational model for the evaporation of urea-water-solution droplets exposed to a hot air stream

Two β-diketiminato-magnesium anthracene complexes, [{(ArNacnac)Mg}2(μ-C14H10)] (ArNacnac = [HC(MeCNAr)2]−, Ar = mesityl (Mes) or 2,6-diethylphenyl (Dep)) react with 1 atm. CO at room temperature to give tetra-magnesium enediolate complexes [{(ArNacnac)Mg}4(O2C16H10)2]viacoupling of two molecules of CO with the anthracenediyl fragment. Similarly, reaction of magnesium anthracene, [Mg(THF)3(C14H10)], with CO afforded a low isolated yield of a tetra-magnesium complex bearing both dianthraceneylenediolate and dibenzocyclohepteneolateyl ligands. The presented reactions represent very rare examples of magnesium alkyl carbonylations that occur under mild conditions and in the absence of catalysts. © The Royal Society of Chemistry 2021.