With the rapid development of visual inspection technology, computer technology, and image processing technology, machine vision technology has become more and more mature, and the role of quality inspection and control in the steel industry is becoming more and more obvious and important. Defects on the surface of the strip are a key factor affecting the quality inspection process. Its inspection plays an extremely important role in improving the final quality. For a long time, traditional manual inspection methods cannot meet actual production needs, so in-depth research on steel surface defect inspection systems has become the consensus of today’s steel companies. The accuracy and low performance of traditional detection methods can no longer meet the needs of people and society. The surface defect detection method based on machine vision has the characteristics of high accuracy, fast processing speed, and intelligent processing, which is the main trend of surface defect detection. We select a steel plate; take the invariant moment features of the cracks, holes, scratches, oil stains, and other images on it; extract the data results; and analyze them. Then, we read the texture features of these defect images again, extract the data results, and analyze them. The experimental results prove that after the mean value filter and Gaussian filter process the image, the mean variance value MSE is relatively large ( 46.276 > 31.2271 ), and as the concentration of salt and pepper noise increases, the rate of increase of MSE increases obviously, and as the peak signal-to-noise ratio and the mean variance value MSE increase continuously ( 32.2271 < 33.3695 ), the image distortion is more serious. The method designed in this paper is extremely effective. Improving the surface quality of steel is of great significance to improving market competitiveness.

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In order to explore the mapping recognition of arc welding molten pool characterisation and penetration state, according to the idea of embedded system construction, this article adopts the idea of software and hardware co-design to find the zero-crossing point of the second derivative in welding image edge detection, and give a threshold. When the absolute value of the first-order derivative exceeds the threshold and has a different sign with the first-order derivative of the previous edge, it is judged as a valid edge. The welding current adopts a symmetrical pulsed AC square wave, and the proportion of heat flow input is high. At the base current, the arc light is darker, so a clear image is obtained. This article designs a simulation experiment to verify the effect of the embedded system in this article. From the experimental research, it can be known that the embedded system constructed in this article can play a certain role in the mapping recognition of the arc welding molten pool characterisation and penetration state.

In this paper, in situ SiC-reinforced Al-Zn-Mg-Cu composites were fabricated by laser powder bed fusion (LPBF). The effects of SiC content on the microstructure, phase composition, microhardness, and wear resistance of as-printed composites were preliminarily investigated. Results show that the microstructure was regulated, the matrix grains were refined, and the tendency to orientation grain growth was suppressed. SiC particles reacted in situ with the Al matrix to produce Si, Al4C3, and Al4SiC4 phases. The microhardness and wear resistance of as-printed composites increased with SiC content due to the fine grain strengthening of the matrix and the second phase strengthening of precipitates and reinforcements.