Informatics Publishing Limited

R. Saravanan ¹, S. Srihari ¹, A. Arvind ¹, P. Sreeranj ¹, K. S. Dheeraj ¹, R. Sellamuthu ¹, Sanjivi Arul ¹

Affiliations
1 Department of Mechanical Engineering, Amrita School of Engineering - Coimbatore, Amrita Vishwa Vidyapeetham University, Coimbatore - 641112, Tamil Nadu, IN

Abstract

Background/Objectives: The study aims to improve surface properties of aluminium A356 alloy by surface alloying it with nitrided titanium, in a nitrogen environment, using Gas Tungsten Arc (GTA) as heat source. Methods/Statistical Analysis: Nitrided titanium sheets were surface alloyed with cast aluminium A356 blocks, in nitrogen environment, with GTA as heat source for melting. The cross-sectional microstructure of the specimens was studied using inverted metallurgical microscope. Further analysis was carried out using SEM/EDS to identify the formation of nitrides and intermetallic compounds. The hardness of the specimens was measured using Vickers hardness tester and the wear rate was determined using pin-on-disc wear tester. Findings: Microstructure analysis revealed a uniform and granular refined structure in the modified layer compared to the coarse and dendritic structure of the cast block. EDS analysis indicated the formation of hard-intermetallic compounds. The hardness was measured to be highest at the surface of the central fusion zone, with a maximum value of 656 HV while as-cast aluminium block exhibited only 76 HV. The measured wear rate was 10×10^-4 mm³/m for the modified layer, compared to 52×10^-4 mm³/m of the substrate. Alongside, the loss in weight after wear dropped by 4 mg. The coefficient of friction of the modified surface showed a constant trend during the wear-off period. The enhancement in these surface properties is attributed to the formation of nitrides and other intermetallic compounds that in the modified layer during surface alloying. Additionally, the use of GTA as heat source renders the surface alloying process to be economically feasible relative to other employable methods. Applications/Improvements: The devised surface alloying method used to enhance the surface properties of A356 is cheap, flexible and effective and finds intensive application in marine, automotive and manufacturing sectors.

Keywords

A356, Gas Tungsten Arc, Hardness, Nitriding, Surface Alloying, Titanium, Wear Rate.

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S. Srihari ¹, S. Huxley ², Ajitha Savarimuthu ³

Affiliations
1 ITM Business School, Student, Chennai, Tamil Nadu, IN
2 Visiting faculty, ITM Business School and AVP Indium Software, Chennai, Tamil Nadu, IN
3 Associate Professor, Acharya Bangalore Business School, Bangalore. Karnataka, IN

Abstract

In today’s world, banking sector is crucial to the modern economy. As the primary supplier of credit, it provides money for people to buy cars and homes and for businesses to buy equipment, expand their operations, and meet their payrolls. The credit cards, debit cards, and checking accounts that banks make available facilitate all kinds of everyday transactions. They also help drive e-commerce, where cash is of little use. With banking products becoming increasingly commoditized, Analytics can help banks differentiate themselves and gain a competitive edge. Machine learning forecasting for banking enables more accurate reporting by automating credit risk testing for both banks and customers. By evaluating a consumer’s financial history, recent transactions, and purchasing patterns, machine learning can make accurate forecasts of future spending and income. Predictive analytics helps banks distinguish between the various portfolio risks effectively, by optimizing the collections process. It helps banks segregate risky customers from the risk-free ones. This can help banks devise actions and strategies to achieve positive results. Predictive Analytics is a stream of advanced analytics which uses new as well as historical data to forecast activity, behaviour, and trends to predict the future. This involves data mining, modelling, employing statistical analysis techniques, and automated machine learning algorithms to make the predictions. It helps organizations discover business issues in real time and address them at the right time to get the best outcomes

Keywords

Banking Sector, Modern Economy, Machine Learning Forecasting, Credit Risk, Predictive Analytics, Positive Results, Data Mining, Statistical Analysis

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