Hybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel

Abstract

A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation Hardened (OPH) alloys, has recently been developed by the authors. The excellent mechanical properties can be improved by optimizing the chemical composition in combination with heat treatment. However, the behavior of such materials requires the consideration of a large number of variables, nonlinearities, and uncertainties in the analyses, and the modeling of such alloys by analytical methods is not accurate enough. Therefore, artificial intelligence (AI) methods, such as machine learning (ML), can be beneficial to alleviate the problems associated with the complexity of these alloys. In this work, three different hybrid ML techniques have been employed to estimate the ultimate tensile strength (UTS) and elongation in these special alloys. The proposed methods include a feedforward artificial neural network (FF-ANN) trained using particle swarm optimization (PSO) and two adaptive neuro-fuzzy inference system (ANFIS) methods trained using both fuzzy C-means (FCM) clustering and subtractive clustering (SC). Since OPH alloys are mainly produced via mechanical alloying (MA) of a mixture of powder components followed by consolidation and hot rolling, a series of standard tensile tests were performed on the different variants of the OPH alloy. In this way, some critical parameters such as UTS and elongation could be extracted from the experimental results. The main contribution of the present study is to estimate these important parameters based on some material properties including Aluminum (Al), Molybdenum (Mo), Iron (Fe), Chromium (Cr), Tantalum (Ta), Yttrium (Y) and Oxygen (O), MA and the heat treatment conditions. The results show that the proposed strategies are not only able to accurately determine the complex behavior of OPH alloy with an accuracy of about 95%, but they can also help the designer to benefit from these powerful tools to design new versions of such materials without analytical calculations.

Keywords

• Oxide precipitation hardened (OPH) steels
• tensile strength
• artificial neural network (ANN)
• particle swarm optimization
• ANFIS
• Fe–Al–O