Studies on grain refinement and alloying additions on the microstructure and mechanical properties of Mg-8Zn-4Al alloy

MAGNESIUM ALLOYS have strong potential for weight reduction in a wide range of technical applications because of their low density compared to other structural metallic materials. Therefore, an extensive growth of magnesium alloys usage in the automobile sector is expected in the coming years to enhance the fuel efficiency through mass reduction. The drawback associated with the use of commercially cheaper Mg-Al based alloys, such as AZ91, AM60 and AM50 are their inferior creep properties above 100ºC due to the presence of discontinuous Mg17A112 phases at the grain boundaries. Although rare earth-based magnesium alloys show better mechanical properties, it is not economically viable to use these alloys in auto industries. Recently, many new Mg-Al based alloy systems have been developed for high temperature applications, which do not contain the Mg17Al12 phase. It has been proved that the addition of a high percentage of zinc (which depends upon the percentage of Al) to binary Mg-Al alloys also ensures the complete removal of the Mg17Al12 phase and hence exhibits superior high temperature properties.ZA84 alloy is one such system, which has 8%Zn in it (Mg-8Zn-4Al-0.2Mn, all are in wt %) and shows superior creep resistance compared to AZ and AM series alloys. These alloys are mostly used in die casting industries. However, there are certain large and heavy components, made up of this alloy by sand castings that show lower mechanical properties because of their coarse microstructure. Moreover, further improvement in their high temperature behaviour through microstructural modification is also an essential task to make this alloy suitable for the replacement of high strength aluminium alloys used in automobile industry. Grain refinement is an effective way to improve the tensile behaviour of engineering alloys. In fact, grain refinement of Mg-Al based alloys is well documented in literature. However, there is no grain refiner commercially available in the market for Mg-Al alloys. It is also reported in the literature that the microstructure of AZ91 alloy is modified through the minor elemental additions such as Sb, Si, Sr, Ca, etc., which enhance its high temperature properties because of the formation of new stable intermetallics. The same strategy can be used with the ZA84 alloy system to improve its high temperature properties further without sacrificing the other properties. The primary objective of the present research work, “Studies on grain refinement and alloying additions on the microstructure and mechanical properties of Mg-8Zn-4Al alloy” is twofold: 1. To investigate the role of individual and combined additions of Sb and Ca on the microstructure and mechanical properties of ZA84 alloy. 2. To synthesis a novel Mg-1wt%Al4C3 master alloy for grain refinement of ZA84 alloy and investigate its effects on mechanical properties.

Development of Polybutadiene (BR)-Polyethylene (LDPE) Blend Based Microcellular Soles for Low-Temperature Applications

Microcellular (MC) soles based on polybutadiene (BR) and low-density polyethylene (LDPE) blends for low-temperature applications were developed. A part of BR in BR-LDPE blend was replaced by natural rubber (NR) for property improvement. The BR-NR-LDPE blend-based MC sole shows good technical properties. Sulphur curing and DCP curing were tried in BR-LDPE and NR-BR-LDPE blends. Study shows that sulphur-cured MC sheets possess better technical properties than DCPcured MC sheets. 90/10 BR-LDPE and 60/30/10 BR-NR-LDPE blend combinations are found to be suitable for low-temperature applications.

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.