# Machine Learning-Enabled Prognostication of Tensile Strength in 316L Stainless Steel Through Additive Manufacturing Processes

## TL;DR

This paper presents a machine learning model combining CNN and RF to accurately predict the tensile strength of 316L stainless steel made via additive manufacturing.

## Contribution

A novel hybrid CNN-RF model is proposed, achieving higher accuracy than using CNN alone for tensile strength prediction.

## Key findings

- The hybrid model achieved an MSE of 0.00295 and MAE of 0.0344, outperforming CNN alone.
- The correlation coefficient reached 0.9576, showing high predictive accuracy.
- Adding relative density and Vickers hardness reduced prediction accuracy.

## Abstract

The tensile strength of components fabricated through additive manufacturing processes is of paramount importance for their implementation in practical engineering applications. However, the intricacy of the process parameters renders the prediction of tensile strength a formidable challenge. In this scholarly work, a predictive model for the tensile strength of 316L stainless steel components produced via SLM was developed through the synergistic integration of CNN and RF. The model was trained on a dataset comprising 42 datasets and subsequently validated against 12 sets of experimental data. The model’s predictive performance was quantified using MSE and MAE, which were recorded as 0.00295 and 0.0344, respectively. These values represent a reduction of 3.28% and 31.88% when compared to the predictive accuracy achieved by employing CNN in isolation. Furthermore, the correlation coefficient achieved a substantial increase of 74.18%, reaching a value of 0.9576, which is indicative of a high degree of accuracy in the model’s predictive outcomes. With the same sample size, the incorporation of relative density and Vickers hardness as additional input conditions resulted in a reduction in prediction accuracy. The tensile strength prediction model presented herein demonstrates the capability for high-precision prediction even with small datasets, thereby offering a theoretical framework that may guide future endeavors in the prediction of mechanical properties for a broader spectrum of materials.