Department of Mechanical engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1921

Browse

Author: search.filters.author.Jasti, Naga Vamsi KrishnaSubject: search.filters.subject.Mechanical Engineering

Search Results

Now showing 1 - 10 of 36
  • No Thumbnail Available
    Item
    Prediction of drilling induced delamination and circularity deviation in GFRP nanocomposites using deep neural network
    (Elsevier, 2022) Sharma, Panchagnula Jayaprakash; Jasti, Naga Vamsi Krishna
    Drilling of Glass Fiber Reinforced Polymer (GFRP) nanocomposites is most prevailing topic to understand the composite behaviour under different cutting conditions. The present study is mainly focused on prediction of drilling output responses such as delamination factor and circularity error randomly with the help of deep neural network (DNN) model. L9 orthogonal array is used for experimentation. Drilling operation is performed on 0.3 wt% multi-walled carbon nano tubes reinforced GFRPs with solid carbide, TiCN and TiAlN coated (6 mm- diameter) twist drills. Based on experimental results, two different deep neural network models are prepared with single and double hidden layers by varying node numbers such as 8, 16, 32, 64, and 128. Thrust force, Acoustic Emission RMS voltage, and drill type (coating) are considered as input to the neural network and delamination factor at exit, circularity error are treated as predicted output responses for the given network model. The revealed predicted results recommended that two hidden layers with 32 nodes network model give the lowest absolute error of 0.08% and 3.13% in delamination factor and circularity errors respectively. Similarly, the highest absolute error is identified as 4.19% in delamination factor and 13.14% in circularity error by single hidden layer with 128 nodes. Therefore, it is urged that DNN is the most suitable modelling technique for prediction of drilling responses on GFRP nano composites.
  • No Thumbnail Available
    Item
    Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates
    (IOP, 2024) Sharma, Panchagnula Jayaprakash; Jasti, Naga Vamsi Krishna
    Multi-walled carbon nanotubes (MCNTs)-enhanced glass fiber composite (GFC) laminates are among the most promising materials for fulfilling various structural and non-structural requirements. They have also shown exceptional functional applications as excellent electrical and thermal conductors, as well as electromagnetic interference shielding materials. The present work primarily focuses on developing regression models for the drilling process of 0.3 wt% MCNTs-GFC laminates. For experimentation, three different coated drills—carbide, TiCN-coated, and TiAlN-coated—are used under both dry and chilled air cutting environments. The lowest thrust force, torque, and delamination factor were observed at a feed rate of 10 mm min−1 and a speed of 1500 RPM using a TiCN-coated drill in a chilled air environment. Regression analysis reveals that feed rate significantly influences thrust force, as justified by the R2 value, which is above 90% for the selected cutting conditions. The corresponding t and F statistics values indicate the statistical significance of the relevant explanatory factors. The efficiency of the developed models is further validated by considering the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, which are 136.9 and 144.7, respectively. These values indicate a good regression fit and likelihood of the models for data prediction. Additionally, there is a strong correlation (coefficient > 0.85) between thrust force and delamination factor under the selected cutting environments. Concurrently, the developed regression models are simulated and evaluated for random experiments (Nos. 87, 125, 187, 243, 244, and 399), and the predicted responses closely match the experimental values.
  • No Thumbnail Available
    Item
    A Conceptual Study of Continuing Educational Programmes in Some Indian Industries in the Globalized World
    (SSRN, 2015-03) Jasti, Naga Vamsi Krishna
    This paper deals with study of different models of training and learning programmes implemented by some of the leading Indian manufacturing conglomerates for their employee skill and knowledge enhancement in partnership with several leading Indian and overseas educational institutions and universities. The learning programmes include both on-campus and off-campus programmes. The paper discusses candidate selection, instructor selection, course delivery, project selection and mentoring, student assessment, instructor assessment, overall programme assessment, hurdles encountered and in-house convocation. Moreover, future challenges and opportunities for conventional education settings versus arrival of new industry run universities and foreign run universities are also discussed. In the globalized business environment, the companies’ visionary managements believe in creating leaders at every level in different segments of their business to retain their top notch global position, which is achieved through systemic and systematic approach. The employees are trained to take sound decisions at all levels based on observations, synthesis and analysis, which is possible only through thorough understanding of scientific, engineering, technological, and business principles. Leaders are also developed for new areas such as energy conservation, human safety, environmental protection, green manufacturing, green IT etc. Thus the companies believe in empowering their employees with advanced skills and knowledge through advanced training and learning programmes. The companies invest heavily in the various programmes.
  • No Thumbnail Available
    Item
    Assessing the Understanding Level of Students for the Computer Programming Course through MATLAB: A Case Study of Working Professional Student
    (SSRN, 2022-02) Jasti, Naga Vamsi Krishna; Kale, Samir Ramdas
    As new digital technologies evolve in various engineering facets, there is a need for an engineer, as a stakeholder to understand and comprehend the backbone of the technologies and the logical framework involved. Programming plays an important role to understand it easily, therefore, the article emphasises the impact of teaching programming to the engineers and its effectiveness with respect to the learning outcomes. The case study involves working professionals who are students at the largest technical institute which offers work-integrated learning programs in India. The research is carried out for the recently concluded course Computer Programming in their current semester which was taught through MATLAB. It has been observed through a feedback survey that the level of understanding of the logic and the software was increased significantly at the end of the semester.
  • No Thumbnail Available
    Item
    Design of Remote Labs for Continuing Education Students in Engineering Domain
    (SSRN, 2022-02) Jasti, Naga Vamsi Krishna; Srinivas, Kota; Kale, Samir Ramdas
    Problem-solving is the key skill expected in any industry, especially in continuing learning related to engineering and technology. In the industry, the study requires stopping production and conducting the trials requiring a lot of time with financial implications. In the physical laboratory at the institute, the presence of the student and the faculty is limited by time and space. Remote labs and virtual labs can ease these concerns with the help of technology. Considering these factors, remote and virtual labs have been developed and offered for continuing education students across engineering domains. This research work has focused on the demonstration of the design and establishment of the Fluid Mechanics and Machines remote lab. The study includes IT, physical infrastructure and instructional strategy deployed in the lab. It also presents the statistics with respect to the number of experiments performed, assessment and feedback by the students.
  • No Thumbnail Available
    Item
    Inventory Modelling for technology generation products under uncertain trade credit terms and imprecise procurement costs
    (OSCM, 2022) Nagpal, Gaurav; Chanda, Udayan; Jasti, Naga Vamsi Krishna
    The inventory policies for any product under the trade credit mechanism are influenced by the procurement price per unit and the credit period offered by the seller to the buyer. This paper develops an inventory model for the technology generations under the imprecise trade credit period and the imprecise procurement cost. It considers the demand that is credit-linked and governed by innovation diffusion as well. The imprecise nature of the parameters is captured by the use of fuzzy numbers. The trapezoidal membership function has been used to fuzzify the profit function with the imprecise parameters, and then the centroid method is used to de-fuzzify the profit. The numerical illustrations have been performed, followed by the sensitivity analysis with the launch timing of the second generation product. A few important implications for the inventory practitioners and the possible extensions of this work have also been discussed
  • No Thumbnail Available
    Item
    Ore Grade Estimation in Mining Industry from petro-physical data using neural networks
    (ACM Digital Library, 2022) Nagpal, Gaurav; Nagpal, Ankita; Jasti, Naga Vamsi Krishna
    The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data. The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
  • No Thumbnail Available
    Item
    Predictive Analytics based Modeling of the purchase intention of electric vehicles, and understanding the drivers and risks in their adoption for the people of Tamil Nadu in India
    (IEEE, 2023) Jasti, Naga Vamsi Krishna; Nagpal, Gaurav; Nagpal, Ankita
    While the adoption of electric vehicles by the World is very important to address the issues of climate change, the rate of adoption of EVs is substantially low due to some of the barriers in their adoption by the population. While several studies have been done in the past using different theories to measure the perception of people towards the electric vehicles in the context of several countries including India, there is no such study for a specific Indian state. Since India is the land of diversity where people with different lifestyles and values co-exist, the state-specific studies can generate more insights for the policy makers, manufacturers and marketers of EVs. Therefore, this study measures the perception of people of Tamil Nadu, one of the prominent automotive manufacturing hubs in the country, towards the adoption of electric vehicles through a structured primary survey. A logistic regression model has also been developed to measure the purchase intention of the potential adopters. The important findings of the study and managerial implications have also been discussed.
  • No Thumbnail Available
    Item
    An empirical study on implementation of sustainable production practices in Indian manufacturing industry
    (Emerald, 2023-10) Jasti, Naga Vamsi Krishna; Nagpal, Gaurav; Kota, Srinivas
    Sustainable production (SP) is an efficient and influential approach of production for Indian manufacturing industries as it preserves the social, environmental and economic aspects of production activities altogether. The objective of this research work is to investigate the implementation status of SP practices in Indian manufacturing industries by utilizing empirical survey methodology.
  • No Thumbnail Available
    Item
    An occupational health and safety management system framework for lean process industries: an interpretive structural modelling approach
    (Emerald, 2022-10) Jasti, Naga Vamsi Krishna; Kale, Samir Ramdas
    The manufacturing sector has been observing various benefits by the implementation of lean manufacturing practices. However, the manufacturing sector has neglected the significance of health and safety management system implementation. The purpose of this research is to propose and validate an occupational health and safety management systems (OHSMS) framework based on critical success factors and their relationships in the lean manufacturing organizations.