http://dspace.dtu.ac.in:8080/jspui/handle/123456789/121 2026-04-28T04:03:37Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/20944 Title: IMPROVED TRIBOLOGICAL PROPERTIES BY UTILIZING ELCTRICAL DISCHARGE MACHINE COATING WITH Hbn, CU & MoS2 USING GREEN COMPACT POWDER BLEND FOR SOLID LUBRICATION Authors: SINGH, PRASHANT KUMAR Abstract: The electro-discharge machining (EDM) technique is one of the most efficient non-conventional machining methods in the current study for cutting extremely hard materials that are challenging to mill using standard machining techniques. In addition to the surface erosion that occurs during the EDM process, some tool material is also lost as a result of the operation's inherent nature. Electro discharge coating (EDC) was developed as a result of this nature's usage of the EDM process's properties. Ignition erect places tool material on the substrate surface in the EDC technique of surface modification. It functions according to the opposite polarity of EDM. A layer of disulphide of molybdenum, meahexagonal boron nitride(hbn)(hbn), and copper covering modifies the substrate's surface. With reverse polarity, an electrical discharge machine is used to deposit a coating layer over the substrate surface. The layer thickness was controlled by a number of variables, including peak current amplitude, powder blending ratio, and duty factor. The mixture was mixed for one and a half hours (1:30) hours in a mortar before being used to construct a compacted green electrode made of (hbn/Cu/MoS2) that was used to deposit the layer. After being put through a hot mounting press machine, the powdered mixture created a compacted green electrode with exact specifications. After the trials, a number of investigations were performed to look at the coated surface's morphology. The FESEM image displays the architecture of the coating layer with tiny gaps with a blending ratio of (hbn/Cu/MoS2) (30/40/30), a peak current of 5 ampere, and a 50% duty factor. Additional molecules found by X ray diffraction studies include MoS2, Hbn, and Cu molecules. 2021-06-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/20943 Title: ANALYSE THE EFFECT OF CLAD RATIO ON STRESS STRAIN CURVE OF TITANIUM-CLAD BIMETALLIC STEEL FOR DIFFERENT STRAIN RATES AND TEMPERATURES USING JOHNSON-COOK MODEL Authors: RAHATGI, HARSHIT Abstract: Titanium–clad bimetallic steel plate finds its application in the construction of large pressure vessels which are used for storage and processing of petrochemicals. Mechanical properties can be imparted to any material according to its application by a technique known as cladding. To improve material, two main functions are performed by cladding. The first is to improve the material‘s surface properties like wear resistance in conditions like erosion, abrasion, and Corrosion. The second is to impart bulk-dependent properties like strength, hardness, etc. Titanium-clad bimetallic steel finds its application in various industrial fields like shipbuilding, construction of bridges and buildings, and high-pressure vessels used in the petrochemical industry. Titanium-clad steel enhances the service life of welded pipes as well as reduces the material cost. The finite element analysis (FEA) technique is an efficacious numerical method for solving several engineering problems. This technique not only provides reliable test results but also reduces the cost of the experiment. The aim of this report is to analyze the effect of clad ratio on stress-strain curves of titanium-clad bimetallic steels for different strain rates and different temperatures using the Johnson-Cook flow stress model. The ratio of the cladding layer thickness (tc) to the total thickness of Titanium-Clad bimetallic steel plate (t) is known as Clad ratio (α). In this report, Titanium Grade 5 is used as a cladding material and AISI 1006 is a parent metal. Furthermore, model constants were estimated from the analyzed result by using material constant for both cladding material and parent material from the research papers. The dimensions of the specimens were taken from GB/T228.1–2010 testing standard. The three-dimensional design of the specimens was created in Solidworks 20 and analyzed in Ansys Explicit Dynamics. A tension test was performed using Ansys for three different temperatures (293K,673K, and 973K) and for three different strain rates (1/sec, 100/sec, and 500/sec). In this report, the reference strain rate was taken as 1/sec and the reference temperature as 293K. 2021-06-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/20942 Title: INTEGRATING NLP-BASED SENTIMENT ANALYSIS INTO SUPPLY CHAIN MANAGEMENT: A CASE STUDY OF INDIAN PRODUCTS ON AMAZON Authors: VATS, KUSHAGRA Abstract: A critical component of modern business operations is Supply chain management (SCM). It involves the effective coordination of activities from the procurement of raw materials to the delivery of finished products to customers. With the rapid technology advancements, the integration of Machine Learning (ML) and Artificial Intelligence (AI) techniques has emerged as a powerful tool in enhancing supply chain management practices. One particular area that has gained significant attention in recent years is sentiment analysis using Natural Language Processing (NLP). Sentiment analysis (SA) or Opinion Mining (OM) is a special branch of NLP that focuses on extracting subjective information, sentiments and opinions from text data, such as customer reviews. By analyzing the sentiment expressed in these reviews, businesses can gain valuable insights into customer preferences, pain points and satisfaction levels. Applying sentiment analysis techniques to supply chain management allows organizations to better understand customer feedback and reviews, leading to informed decision-making and improved overall performance. The goal of this study is to look into how NLP-based sentiment analysis could be used to enhance supply chain management, specifically through utilizing consumer feedback for Indian items. The study's goal is to examine the use of sentiment analysis to learn more about how customers feel about various elements of the supply chain, including product availability, delivery time, packaging, and customer service. The study attempts to identify patterns, trends, and customer feelings by analyzing a dataset of over 1000 customer reviews gathered from various categories, including clothing, hair and skin care goods, and technological devices on the Amazon platform. The research's goal in conducting this case study is to draw attention to the useful uses of sentiment analysis in supply chain management. The study's results will help us comprehend sentiment analysis as a decision-making tool better and how it can be utilized to enhance different supply chain components. By giving businesses insights into client preferences and empowering them to make data-driven decisions to improve product offerings, inventory management, logistics optimisation, and overall customer pleasure, this research has the potential to be helpful to businesses. 9 We used a theoretical framework that is based on supply chain management, sentiment analysis, and NLP literature that has already been published. For the purpose of our study, we analyzed a corpus of research publications on supply chain management, sentiment analysis, and related subjects. Our study strategy included exploratory data analysis (EDA), the use of the VADER and RoBERTa models for sentiment analysis, and a qualitative analysis of the outcomes to pinpoint the most important conclusions and their ramifications. The results imply that NLP-based sentiment analysis can offer useful supply chain management insights. Our research of the Amazon dataset showed that sentiment analysis may pinpoint a product's advantages and disadvantages, point out potential areas for development, and reveal consumer preferences and expectations. Additionally, the study found that sentiment analysis can offer helpful data for a variety of supply chain management functions, such as product design, manufacturing, inventory control, and customer service. By illustrating the importance of sentiment analysis in enhancing decision-making in several supply chain management sectors, the research adds to the body of knowledge on NLP-based sentiment analysis and supply chain management. The paper also emphasizes the need for additional investigation to examine sentiment analysis's potential in other supply chain management domains, such as supplier management and logistics. The study reveals how sentiment analysis can help with decision-making and customer happiness, which has applications for supply chain managers. Additionally, our study offers a methodology that may be used in various contexts to undertake sentiment analysis in supply chain management. The study also emphasizes how critical it is to incorporate NLP-based sentiment analysis into supply chain management systems in order to monitor customer comments and reviews in real-time and create supply chains that are more responsive and centered on the needs of their customers. As a result, our research shows how NLP-based sentiment analysis may enhance supply chain management. The significance of sentiment analysis in determining consumer preferences and expectations, enhancing product design and quality, and improving inventory management is highlighted in our work as a contribution to the literature. Because it offers a methodology for doing 10 sentiment analysis and emphasizes the value of incorporating NLP-based sentiment analysis into supply chain management systems, our research also has applications for supply chain managers. 2023-05-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/19881 Title: OPTIMIZATION OF WELDING PARAMETERS TO STUDY THE WELD ON BEAD OF AA6082 BY USING COLD METAL TRANSFER (CMT) WELDING PROCESS Authors: BANSAL, HARSHIT Abstract: Aluminium alloys are extensively employed in industries such as construction, automobile manufacturing, and spacecraft production owing to their remarkable attributes, including outstanding corrosion resistance, a high strength-to-weight ratio, excellent machinability, and ductility. The cold metal transfer technique shows potential as a welding method for constructing aluminium structures. This research work focuses on a comparison of influence of activated flux on microstructure and characteristics of weld bead geometry of Al-Mg-Si alloy AA6082 manufactured by cold metal transfer (CMT) process. ER4043 was used as a filler wire to make weld on bead on 3 mm thick AA6082 plates. Various input process parameters i.e., current (80, 100 and 110 A) and welding speed (30, 40, 50 cm/min) were used, whereas the nozzle tip distance andshielding gas flow rate remained fixed at 10 mm and 15 l/min, respectively. Optical microscopyis used to study the microstructural characteristics. The samples created with activated flux have a highlevel of penetration and percentage dilution. Also, there is an increase in micro-hardness of samples which are fabricated using activated flux. 2023-06-01T00:00:00Z