Magnesium and its alloys, as the metal materials with the lowest density among structural materials, have attracted much attention due to their excellent properties such as high specific stiffness, good electromagnetic shielding properties and good vibration damping effects. However, its low strength, inherent brittleness and poor corrosion resistance limit its application in various industries. By adding a reinforcing phase to the magnesium matrix, a magnesium-based composite material having excellent properties has become one of the effective ways to realise the industrial application of magnesium alloys. This article reviews the recent research progress of magnesium-based composites, including the reinforcing phases and preparation methods of magnesium-based composites, and looks forward to the future development and research directions of magnesium-based composites.

Due to the existence of high-temperature and high-pressure zinc vapor in the gas metal arc welding (GMAW) process of galvanized steel, it is difficult to achieve accurate and real-time detection of weld defects, which brings great challenges to robotic welding manufacturing. In order to realize the automatic classification and prediction of weld defects, a method of galvanized steel weld defects detection based on active visual sensing and machine learning was proposed. First, Gabor filter was used to remove arc light, noise, and other interference signals to obtain the weld centerline image. Then, according to the five different weld defects of the geometric and spatial distribution characteristics in weld centerline image were analyzed with the principle of sub-pixel level. Finally, using the eight feature parameters extracted from the weld feature points, a variable learning rate and momentum factor back propagation (VL-MFBP) neural network model was designed. The model introduced a variable learning rate and momentum factor to quickly find the optimal solution in a short time, its performance was better than traditional machine learning algorithms. The experimental results show that the accuracy of weld defect recognition is 98.15%, and the average processing time of a single image is only 183.74 ms.

This work investigates a Bayesian network model (BNM) to implement the autonomous decision-making process of welding positions in gas metal arc multipass welding with T-joints for automated manufacturing. The laser vision sensor is used to profile the weld seam, and the weld seam profile (WSP) is extracted with a novel scheme based on scale-invariant feature transform and orientation feature detection. The feature points of the extracted WSP are effectively identified through slope mutation detection. A BNM is built with these points and the determined welding state online, and the priori probabilities of the model variables are acquired in a computational manner integrated with welding experience. The feature point with the maximum posteriori probability is selected as the current welding position using the evidence prepropagation importance sampling inference algorithm. The analytic hierarchy process and the C4.5 decision tree algorithm are used to compare with the proposed BNM regarding decision effectiveness. Experimental results show that the proposed BNM can give the effective decision-making results of welding positions with T-joints of different thicknesses and show great potential for higher manufacturing efficiency and automatic levels.

Weld bead geometry features (WBGFs) such as the bead width, height, area, and center of gravity are the common factors for weighing welding quality control. The effective modeling of these WBGFs contributes to implementing timely decision making of welding process parameters to improve welding quality and enhance automatic levels. In this work, a dynamic modeling method of WBGFs is presented based on machine vision and learning in multipass gas metal arc welding (GMAW) with typical joints. A laser vision sensing system is used to detect weld seam profiles (WSPs) during the GMAW process. A novel WSP extraction method is proposed using scale-invariant feature transform and machine learning. The feature points of the extracted WSP, namely the boundary points of the weld beads, are identified with slope mutation detection and number supervision. In order to stabilize the modeling process, a fault detection and diagnosis method is implemented with cubic exponential smoothing, and the diagnostic accuracy is within 1.50 pixels. A linear interpolation method is presented to implement sub pixel discrimination of the weld bead before modeling WBGFs. With the effective feature points and the extracted WSP, a scheme of modeling the area, center of gravity, and all-position width and height of the weld bead is presented. Experimental results show that the proposed method in this work adapts to the variable features of the weld beads in thick plate GMAW with T-joints and butt/lap joints. This work can provide more evidence to control the weld formation in a thick plate GMAW in real time.