Predicting the quality of materials after they are subjected to plasma sintering is a challenging task because of the non-linear relationships between the process variables and mechanical properties. Furthermore, the variables governing the sintering process affect the microstructure and the mechanical properties of the final product. Therefore, an artificial neural network modeling was carried out to correlate the parameters of the spark plasma sintering process with the densification and hardness values of Ti-6Al-4V alloys dispersed with nano-sized TiN particles. The relative density (%), effective density (g/cm³), and hardness (HV) were estimated as functions of sintering temperature (°C), time (min), and composition (change in % TiN). A total of 20 datasets were collected from the open literature to develop the model. The high-level accuracy in model predictions (>80%) discloses the complex relationships among the sintering process variables, product quality, and mechanical performance. Further, the effect of sintering temperature, time, and TiN percentage on the density and hardness values were quantitatively estimated with the help of the developed model.

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• Application of Machine Learning Algorithms and SHAP for Prediction and Feature Analysis of Tempered Martensite Hardness in Low-Alloy Steels
  Junhyub Jeon, Namhyuk Seo, Seung Bae Son, Seok-Jae Lee, Minsu Jung
  Metals.2021; 11(8): 1159.     CrossRef

The properties of powder metallurgy products are related to their densities. In the present work, we demonstrate a method to apply artificial neural networks (ANNs) trained on experimental data to predict the bulk density of barium titanates. The density is modeled as a function of pressure, press rate, heating rate, sintering temperature, and soaking time using the ANN method. The model predictions with the training and testing data result in a high coefficient of correlation (R² = 0.95 and Pearson’s r = 0.97) and low average error. Moreover, a graphical user interface for the model is developed on the basis of the transformed weights of the optimally trained model. It facilitates the prediction of an infinite combination of process parameters with reasonable accuracy. Sensitivity analysis performed on the ANN model aids the identification of the impact of process parameters on the density of barium titanates.

In the present work, spheroidization of angular vanadium powders using a radio frequency (RF) thermal plasma process is investigated. Initially, angular vanadium powders are spheroidized successfully at an average particle size of 100 μm using the RF-plasma process. It is difficult to avoid oxide layer formation on the surface of vanadium powder during the RF-plasma process. Titanium/vanadium/stainless steel functionally graded materials are manufactured with vanadium as the interlayer. Vanadium intermediate layers are deposited using both angular and spheroidized vanadium powders. Then, 17-4PH stainless steel is successfully deposited on the vanadium interlayer made from the angular powder. However, on the surface of the vanadium interlayer made from the spheroidized powder, delamination of 17-4PH occurs during deposition. The main cause of this phenomenon is presumed to be the high thickness of the vanadium interlayer and the relatively high level of surface oxidation of the interlayer.

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