MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

Engineering design is an iterative process that requires to be dealt with all feasible design solutions in order to arrive at a desired objective. Gear drives are considered as the critical components in a mechanical power transmission system. It is possible that gears will predominate as the most effective means of transmitting power in future machines due to their high degree of reliability and compactness. With the advancing technology in recent years, the machines are demanding varying speed and load. Over the years, research has been done predominantly on gears to minimize or to reduce the failures and for optimal design. The main aim of this work is to design and analyse the hybrid composite gears for two stage constant mesh helical gearbox and to compare the stress and deformation results with and without the inclusion of Carbon Fibre Reinforced (CFR) material. The objective of this research work is to reduce weight and rotational inertia of the entire assembly by using Hybrid Helical Gears. Carbon Fibre Standard UT Prepreg was selected for this study because of its high strength to weight ratio. By comparing the weights of EN36 Steel Gear and Hybrid Gear, a 25.3% reduction in weight was observed. This research work illuminates the replacement of EN36 alloy steel gear in terms of stresses and deformation with an added advantage of reduced weight and rotational inertia of the helical gear by using EN36/Carbon Fibre Composite material while considering the safety of the component in the application of two-stage constant mesh gearbox. © 2021 Elsevier Ltd. All rights reserved.

World University root node

School of Mechanical Engineering

Materials Today: Proceedings

Design of helical gear with carbon reinforced EN36 steel for two stage constant mesh gearbox weight reduction

Topology optimization is a technique which has proven to be a very crucial and effective tool in modification of the geometry of various mechanical or load bearing components. This technique has been used in aerospace, mechanical and civil applications to save material and reduce the overall mass of the component. There are various existing topology optimization algorithms which produce remarkable results, each algorithm is unique, and the results obtained are different. Implant dentistry has become a new area where topology optimization has found a good scope. The goal of modifying a dental implant is to improve the osseointegration and enhance its stability, hence when a topological optimization study is being performed on a dental implant these additional factors are necessary to be considered. The literature pertaining to the topology optimization employed in dental implants is limited and this study focuses on the available literature and other important aspects like selection of the optimization algorithm, bone density and manufacturing method which influence the topology optimization procedure when employed in the modification of dental implants.

AIP Conference Proceedings

A review on techniques employed for topology optimization in implant dentistry

Thermoplastic PVC matrix was reinforced with waste fiber portion of Citrus Maxima (CM) fruit for making composites. The CM fibers were separated, dried at 50-60 °C for 24 h, coarse ground and sieved. PVC/CM fiber composite film with different fiber loading (10-70 wt%) were cast using solvent casting method. Sheets were cast in large petri dish using tetrahydrofuran (THF) as solvent and dried in an oven thoroughly at 50-60 °C. The evaluation of mechanical properties was done as per ASTM D882; stereo image of the fractured specimen and FTIR was used for the study of morphology and microstructure of the composite. Incorporation of fibers into PVC made the material stiff, leading to significant increase in Tensile Modulus, Breaking Strength and Yield Stress at higher filler loadings. Uniform distribution of the fibers in the microstructure of PVC matrix led to the formation of cross-ply structure and improved the mechanical properties. These results indicate that the fiber portion from CM can be effectively used as an eco-friendly reinforcing additive to form PVC composites.

School of Chemical Engineering

School of Polymer Engineering

Studies on effective utilization of Citrus Maxima fibers based PVC composites

The use of composite adsorbent with nano particle additives having high surface area can lead to enhanced heat transfer, higher adsorption capacity and faster rate leading to better adsorber performance. The objectives of the present study were to investigate the effectiveness of nanofluid as a cooling fluid of a composite adsorber by carrying out nanofluid characterization and to investigate adsorption-desorption characteristics of a composite adsorbent for ammonia as an adsorbate. An experimental set up with tube-in-tube type configuration was constructed to monitor the thermal characteristics (heat transfer from and to the adsorber) and ammonia adsorption/desorption rates. The composite of calcium chloride with activated carbon and expanded graphite nanoparticles was made having different concentrations of CaCl2 ranging from 40 % to 80 % in the composite and expanded natural graphite (ENG) content ranging from 0-3 % by weight. The desorption characteristics were studied at different temperatures from 70° C to 110° C. The cooling fluid with and without graphite nanofibers (GNF) was circulated in the inner tube at different flow rates from 5 LPH to 25 LPH. From the experimentation on adsorption-desorption characteristics, it was found that the total amount of ammonia adsorbed increased from 133 g for 50:50 composition of CaCl2:AC to 156 g for CaCl2:AC composition of 50:50 with the addition of 3 % ENG nanoparticles. Also the adsorption process time reduced from 22 minutes to 18 minutes. For 50 % CaCl2 in the composite the temperature at which the maximum desorption occurred was 70°C. So, desorption could occur at lower temperature, which is advantageous as it increases the number of options for heat sources to be used. When nanofluid was used as a cooling fluid of the composite adsorber, the total amount of ammonia adsorbed increased from 79.411 g for basefluid to 90.206 g for 0.4 % graphite nanofiber nanofluid for flow rate of 25 LPH. With the use of composite adsorbent of CaCl2:AC of 50:50 composition and addition of expanded natural graphite (ENG) by 3% and when 0.4 % graphite nanofiber nanofluid was used as a cooling fluid, the maximum ammonia was adsorbed up to the bed length of 600 mm only out of the total bed length of 1 m indicating the possibility of reduction in the size of an adsorber. © 2021 Author(s).

Analysis of composite adsorber with graphite nanofiber based nanofluid as coolant

The main aim of the this work is to design and develop a suitable mechanism to finish the hard surfaces and improve the performance of finishing processes based on MR fluid using external magnetic field. This work is carried out by considering the significant process parameters like the concentration ratio of CIPs to aluminum oxide (Al2O3) as an abrasives, magnetizing current, the rotating speed of the tool, feed rate of workpiece, working gap and hardness of work pieces to know the response as a percentage change in surface roughness Ra. SEM technique is employed to know the surface integrity and understand the performance of MR fluid based finishing process over the surface of die steel materials. While, performing the experimental work, a full factorial design is applied and the results are characterized using regression and ANOVA technique. It is revealed from the results that the foremost contribution of the factors in the highest order are working gap, magnetizing current and the hardness of the work piece followed by the ratio of CIP to Al2O3 abrasives, feed rate and rotating speed of the tool in improving the surface roughness Ra. The regression model confirms the linear relationship between observed Ra and the predicted Ra and its correlation coefficient is + 0.85. The average percentage deviation is 3.56 within 5% error band. Thus, this method is capable to obtain the Nano finishing over the surface of hardened materials

Experimental investigation of parameters and surface integrity of die steel based on MR fluid finishing process

In this research novel approach to the selection of the best suitable composition of nanocomposite from the various alternatives of nano-Al2O3/Al6061 using multi-criteria decision-making, techniques are presented. Low strength and hardness limit the use of Al6061 in moderate to high-stress applications such as in aerospace and automobiles. To overcome this deficiency, multi-characteristics <a href="https://www.sciencedirect.com/topics/materials-science/al2o3">Al2O3</a> nanoparticles are successfully reinforced in various wt. % to Al6061 matrix to fabricate novel Al2O3/Al6061 nanocomposites on aluminum stir casting furnace. That results in an improvement in mechanical properties at a different order of importance and various material cost. Hence it is essential to determine the best suitable alternative of nanocomposite and corresponding optimum quantity satisfying application requirements. Therefore, to tackle the difficulty of best suitable composition selection at required order of importance, multicriteria decision-making techniques such as TOPSIS and EXPROM2, was implemented and rankings are obtained. Weight criteria are determined using the AHP model. Comparative study of all MCDM is carried out by spearmen’s rank correlation. The best material found is 4A (4% Al2O3 reinforcement). MCDM analysis was found satisfactory for material selection problems.

TOPSIS &amp; EXPROM2 multicriteria decision methods for Al2O3/Al 6061 nanocomposite selection

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process's continuance and adding proactive and reactive features. The proposed algorithm's performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application. © 2013 IEEE.

School of Electronics & Communication Engineering

IEEE Access

Enhanced differential crossover and quantum particle swarm optimization for IoT applications

In the current era where ample data is generated every second and available to every individual in the form of information, it is easy to gain knowledge from this information but a systematic and methodical approach is required when it comes to skill development. Online and on campus programs help in gaining knowledge but the skills to apply this knowledge needs to be developed gradually through appropriate teaching pedagogy which helps in practicing higher order thinking skills as proposed by Bloom and well known as Bloom’s Taxonomy. In conventional teaching learning approach adopted for online and in-person classroom sessions, the transmission of knowledge takes place most of the time where learner’s involvement may be quite less. So from the perspective of improvement in learner’s involvement in teaching learning process, active learning methods play a significant role and hence practiced widely in recent times. Variety of methods are recommended by scientists and Education experts to improve upon assimilation of knowledge for problem solving. Depending upon the nature of the problem the learner either needs to use one of the lower order thinking skills like apply or higher order thinking skills like analyse, evaluate and create as defined by Bloom. The study presented in this paper discusses case studies of practicing active learning techniques and developing problem solving skills through these techniques. The consolidated feedback analysis for the active learning techniques practiced show that the 96% learners have agreed for clarity of understanding, 92.72% learners have shown confidence in application of concepts and 97.27% participants have shown improved satisfaction in learning. Qualitative testimonies suggests the promising strength of PBL and effectiveness of it in improvement of problem solving skills. The study encourages the effective use of active learning techniques for skill development which is need of time. © 2021, Rajarambapu Institute Of Technology. All rights reserved.

School of Computer Engineering & Technology

Journal of Engineering Education Transformations

Role of Active Learning Techniques in Development of Problem Solving Skills

The past few decades have witnessed an increase in dietary ailments, majorly caused due to unhealthy food habits. According to experts, mobile-based diet monitoring and assessment systems can capture real-time images of various food items as well as analyze the nutritional content present in it and can be very convenient to use and assist in improving food habits. This can help people lead a healthier lifestyle. This proposed model provides an innovative system that can automatically estimate various food attributes like the nutrients and ingredients by classifying the food image that is given as input. The approach involves the use of different types of deep learning models for accurate food item identification. Apart from image recognition and analysis, the food ingredients, nutrients and attributes are obtained and estimated by extracting words which are semantically related from a large collection of text, accumulated over the Internet. Experimentation has been performed with the Food-101 dataset. The proposed system assists the user to obtain the nutritional value of the food item in real-time which is effective and simple to use. The proposed system also provides supporting features such as food logging, calorie tracking and healthy recipe recommendations for self-monitoring of the user. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

A Model for Automated Food Logging Through Food Recognition and Attribute Estimation Using Deep Learning

Image Captioning is one of the emerging topics of research in the field of AI. It uses a combination of Computer Vision (CV) and Natural Language Processing (NLP) to derive features from the image, use this information to identify objects, actions, their relationships, and generate a description for the image. It is most important concept in artificial intelligence applied in the fields like aid to the blind, self-driving cars, and many more. This paper we demonstrates a concise state of art image captioning and its method for caption generation using deep learning concepts. We also determine the approach for image caption generation using Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) model in deep learning framework. Using this approach system intelligent enough to create sentences for images. It uses the encoder-decoder architecture, where CNN is used for image vector generation and LSTM is used for the generation of a logical sentence using the NLP concepts. Finally, we evaluate the proposed system experimental analysis with numerous existing systems and show the effeteness of system. © 2021 IEEE.