In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes to assign health resources, and proposes a number of new ways to use these resources at the time of artificial intelligence in order to make human life part of this process and explore the good conditions which are shared by both the medical and computer industries.

In the present work, AISI 904L super austentic steel sheets of 0.4mm thick is butt welded using Micro Plasma Arc Welding. Welding input parameters like peak current, base current, pulse rate and pulse width are considered and output responses like fusion zone grain size, hardness and ultimate tensile strength of the welded joint are considered. 31 experiments are performed as per Central Composite Design (CCD) design matrix of Response Surface Method (RSM) by considering four factors and five levels of weld input parameters. Grey Relational Analysis (GRA) is carried out by minimizing fusion zone grain size and maximizing fusion zone hardness and ultimate tensile strength to find the optimal combination of weld input parameters.  The order of importance of weld input parameters are also identified and improvement in Grey Relational Grade was found.

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