A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

Abstract

In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC). We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.

Current Trends and Applications of Machine Learning in Tribology—A Review

“…Some successful studies using ML and AI can also be found for composites with soft metals as matrix [54], for example aluminum, copper or zinc and their alloys [55][56][57][58][59]. As such, Stojanović et al [60] investigated the friction and wear behavior of aluminum hybrid composites with Al-Si alloy matrix and 10 wt.% silicon carbide (SiC) as well as 0, 1, and 3 wt.% graphite.…”

“…Most of the works also comprised forward ML models, which were developed to predict the tribological behavior as output based on various input parameters such as material or test conditions. In principle, however, inverse models to characterize the materials and surfaces [54] or physics-informed ML approaches [134] can also be applied. With a closer assessment of the intentions and objectives of the studies, as well as the overrepresentation of ANNs, one might get the impression that ML is in many cases being used to serve its own ends.…”

Surface Stoichiometry and Roughness of a Degraded A380 Alloy after Casting, Technical Cleaning and Packaging

“…This native oxide layer is highly defective and cannot provide effective protection from corrosion, especially when different types of heterogeneities are incorporated within it [ 16 , 19 , 20 , 21 , 22 ]. Therefore, the corrosion resistance of cast Al–Si alloys strongly depends on the surface condition, predominantly on the silicon content and the surface roughness [ 23 , 24 ]. The surface roughness has a significant effect on reducing the corrosion resistance, as does a change in the surface chemistry.…”

“…The surface roughness has a significant effect on reducing the corrosion resistance, as does a change in the surface chemistry. Here, the constant trade-off between these two influential surface parameters must be considered [ 13 , 23 , 24 ].…”

“…Due to the increased need for applications that require high strength and wear resistance, additions of various alloying elements, e.g., Mg, Cu, and Zn on the microstructure, and mechanical and tribological properties are investigated [ 4 , 30 ], and parallel to this, additions of various particles in the case of Al-based, metal-matrix composites (MMCs) [ 13 , 24 ]. For an assessment of the wear behaviour, machine-learning models currently available are a very supportive tool in predicting the friction and wear of Al alloys from material and tribological test data [ 7 , 8 , 13 , 24 ]. A proper topography characterization and further calculations of the representative surface parameters are essential to correlate manufacturing process parameters and key quality factors.…”