Data mining is one amid the core research areas in the field of computer science. Yet there is a knowledge data detection process helps the data mining to extract hidden information from the dataset there is a big scope of machine learning algorithms. Especially supervised machine learning algorithms gain extensive importance in data mining research. Boosting action is regularly helps the supervised machine learning algorithms for rising the predictive / classification veracity. This survey research article prefer two famous supervised machine learning algorithms that is decision trees and support vector machine and presented the recent research works carried out. Also recent improvement on Adaboost algorithms (boosting process) is also granted. From this survey research it is learnt that connecting supervised machine learning algorithm with boosting process increased prediction efficiency and there is a wide scope in this research element.
In this paper, the influence of friction stir welding process parameters and impact of tool geometry on the microstructural characteristics and tribological properties of AZ80A magnesium alloy are experimentally investigated. Tool with three different pin profiles at a constant tool rotational speed v to feedrate u ratio were employed. Additionally, detailed experimental measurements are also carried out on the hardness and wear losses of joints. The chemical compositions of fabricated joints are analysed using energy dispersive spectrometry. The taper cylindrical pin profiled tool exhibited sound joints under the 750 rev min 21 /75 mm min 21 ratio. It is also found that the tool rotational speed plays a more significant role on the microstructural characteristics and mechanical properties of the joints, compared to feedrate.
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