Magnesium alloys are popular in the aerospace and automotive industries due to their light weights and high specific strengths. The major disadvantages of magnesium alloys are their weak wear and corrosion resistances. Surface coating is one of the most efficient methods of making material surfaces resistant to wear. Experimental determination of wear loss is expensive and time-consuming. These disadvantages can be eliminated by using machine learning algorithms to predict wear loss. This study used experimentally obtained wear loss data for AZ91D magnesium alloy samples coated via two different spray coating methods (plasma and high velocity oxy-fuel spraying) using various parameters. Support vector regression (SVR) and extreme learning machine (ELM) methods were used to predict wear loss quantities. In models tested using 10-k cross-validation, R² was calculated as 0.9601 and 0.9901 when the SVR and ELM methods were applied, respectively. The ELM method was more successful than SVR. Thus, the ELM method has excellent potential to support the production of wear-resistant parts for various applications via spray coating.

Steel fiber reinforced self-compacting concrete (SFRSCC) is a special type of concrete that is widely researched in literature due to its superior properties. As it is difficult to provide its high workability qualities, SFRSCC is thought to be in need of an economic and quick design process. In this study, it is aimed to predict the fresh properties of SFRSCC mixtures following with the standards at the preliminary design stage. With this aim, two different classification methods were applied successfully to a comprehensive dataset collected from international publications. The models used to classify the fresh performance of SFRSCC were Weighted K-Nearest Neighbors (W-KNN) and Quadratic Support Vector Machine (Q-SVM). Consequently, acceptable success rates were obtained from the models. For the prediction of slump-flow, the accuracy values were 0.76 and 0.84 for the W-KNN and Q-SVM models, respectively. For the V-funnel time, the accuracy values were 0.90 and 0.92 for the W-KNN and Q-SVM models, respectively. Owing to the recommended methods, it is expected to reduce the number of trial mixtures in the preliminary design stage of SFRSCC.

Magnesium (Mg) alloy parts have become very interesting in industries due to their lightness and high specific strengths. The production of Mg alloys by conventional manufacturing methods is difficult due to their high affinity for oxygen, low melting points, and flammable properties. These problems can be solved using nontraditional methods such as wire electric discharge machining (WEDM). The parts with a quality surface have better properties such as fatigue, wear, and corrosion resistance. Determining the surface roughness (SR) by analytical and experimental methods is very difficult, time-consuming, and costly. These disadvantages can be eliminated by predicting the SR with artificial intelligence methods. In this study, AZ91D was cut with WEDM in different voltage (V), pulse-on-time (µs), pulse-off-time (µs), and wire speed (mm s⁻¹) parameters. The SR was measured using a profilometer, and a total of 81 data were obtained. Multilayer perceptron, ensemble neural network and optimization-based evolving product-unit neural network (EPUNN) were used to predict the SR. It was observed that the EPUNN method performed better than the other two methods. The use of this model in industries producing Mg alloys with WEDM expected to provide advantages such as time, material, and cost.