The major objective of this work is to study the hardness data at the domain of ferrite and Austenite phases. Nanoindentation and microhardness study has been conducted on austenite and ferrite present in the microstructure of hot rolled and heat treated duplex stainless steel (2205 DSS). Furthermore, Optical microscopy and field emission scanning electron microscope (FE-SEM) were used to identify the microstructural distribution and phases present. Austenite reveals higher nanohardness data value than ferrite, as oppose to ferrite average elastic modulus which is higher than that of austenite. Also, higher value of microhardness data was observed for austenite in comparison with the ferrite at different load application.

Powder metallurgy (PM) technology is an ideal manufacturing process to produce near net shape parts i.e. part that requires little or no machining, examples of PM processes are spark plasma sintering, isostatic pressing and additive manufacturing. PM allows maximisation of materials and produces part with optimised mechanical and physical properties. Also, PM process provides the possibility to further increase the industrial use of PM parts by fabricating it into complex geometrical shapes via joining. Joining is the most important mechanical process necessary for PM parts to perform in actual service conditions expected in automobile parts and structural parts. Despite apparent advantages of PM processes, joining PM parts has been a tedious process, due to challenges associated with inherent characteristics, like porosity, chemical composition and impurities like oil or grease, which tend to impair the weldments property. In this document, a review of PM process is presented, focusing on different welding methods that can be used to effectively join PM components.

This paper presents an artificial neural network (ANN) approach to the estimation of the Vickers hardness parameter at the weld zone of laser-welded sintered duplex stainless steel. The sintered welded stainless-steel hardness is primarily determined by the sintering conditions and laser welding processing parameters. In the current investigation, the process parameters for both the sintering and welding processes were trained by ANNs machine learning (ML) model using a TensorFlow framework for the microhardness predictions of laser-welded sintered duplex stainless steel (DSS 2507 grade). A neural network is trained using a thorough dataset. The mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R² for the train and test data were calculated. The predicted values were in good agreement with the measured hardness values. Based on the results obtained, the ANN method can be effectively used to predict the mechanical properties of materials.

An Improved Prediction of Transparent Conductor Formation Energy using PyCaret: An Open-Source Machine Learning Library

The effect Spark Plasma Sintering (SPS) parameters and Nd:YAG laser welding parameters such as sintering temperature, sintering time, welding power, welding speed, on Vickers hardness mechanical properties of the Weld Zone(WZ) was statistically investigated. Rectangular plates of 2507 Duplex Stainless Steel (DSS) fabricated by SPS were joined by Nd: YAG laser welding process. Linear regression analysis was performed on the data, followed by Pearson correlation analysis to relate hardness with other predicting variables and show their statistical significance to hardness value of the weld zone.The evaluated R2 which is 49%% was used to measure the closeness of the data to the regression fitted line.