快点 | CJME2021年4月在线刊发论文速览

发布于 2021-05-12 11:23 ，所属分类：论文学习资料大全

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DOI：10.1186/s10033-021-00553-8

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Hosameldin O. A. Ahmed & Asoke K Nandi

Abstract：

Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

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https://cjme.springeropen.com/articles/10.1186/s10033-021-00553-8

DOI：10.1186/s10033-021-00554-7

Laser Additive Manufacturing on Metal Matrix Composites: A Review

Neng Li, Wei Liu, Yan Wang, Zijun Zhao, Taiqi Yan, Guohui Zhang & Huaping Xiong

Abstract：

Important progresses in the study of laser additive manufacturing on metal matrix composites (MMCs) have been made. Recent efforts and advances in additive manufacturing on 5 types of MMCs are presented and reviewed. The main focus is on the material design, the combination of reinforcement and the metal matrix, the synthesis principle during the manufacturing process, and the resulted microstructures as well as properties. Thereafter, the trend of development in future is forecasted, including: Formation mechanism and reinforcement principle of strengthening phase; Material and process design to actively achieve expected performance; Innovative structure design based on the special properties of laser AM MMCs; Simulation, monitoring and optimization in the process of laser AM MMCs.

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https://cjme.springeropen.com/articles/10.1186/s10033-021-00554-7

DOI：10.1186/s10033-021-00557-4

Fatigue Resistance and Failure Behavior of Penetration and Non-Penetration Laser Welded Lap Joints

Xiangzhong Guo, Wei Liu, Xiqing Li, Haowen Shi & Zhikun Song

Abstract：

Penetration and non-penetration lap laser welding is the joining method for assembling side facade panels of railway passenger cars, while their fatigue performances and the difference between them are not completely understood. In this study, the fatigue resistance and failure behavior of penetration 1.5+0.8-P and non-penetration 0.8+1.5-N laser welded lap joints prepared with 0.8 mm and 1.5 mm cold-rolled 301L plates were investigated. The weld beads showed a solidification microstructure of primary ferrite with good thermal cracking resistance, and their hardness was lower than that of the plates. The 1.5+0.8-P joint exhibited better fatigue resistance to low stress amplitudes, whereas the 0.8+1.5-N joint showed greater resistance to high stress amplitudes. The failure modes of 0.8+1.5-N and 1.5+0.8-P joints were 1.5 mm and 0.8 mm lower lap plate fracture, respectively, and the primary cracks were initiated at welding fusion lines on the lap surface. There were long plastic ribs on the penetration plate fracture, but not on the non-penetration plate fracture. The fatigue resistance stresses in the crack initiation area of the penetration and non-penetration plates calculated based on the mean fatigue limits are 408 MPa and 326 MPa, respectively, which can be used as reference stress for the fatigue design of the laser welded structures. The main reason for the difference in fatigue performance between the two laser welded joints was that the asymmetrical heating in the non-penetration plate thickness resulted in higher residual stress near the welding fusion line.

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https://cjme.springeropen.com/articles/10.1186/s10033-021-00557-4

DOI：10.1186/s10033-021-00556-5

Experimental Investigation on Micro Deep Drawing of Stainless Steel Foils with Different Microstructural Characteristics

Jingwei Zhao, Tao Wang, Fanghui Jia, Zhou Li, Cunlong Zhou, Qingxue Huang & Zhengyi Jiang

Abstract：

In the present work, austenitic stainless steel (ASS) 304 foils with a thickness of 50 µm were first annealed at temperatures ranging from 700 to 1100℃ for 1 h to obtain different microstructural characteristics. Then the effects of microstructural characteristics on the formability of ASS 304 foils and the quality of drawn cups using micro deep drawing (MDD) were studied, and the mechanism involved was discussed. The results show that the as-received ASS 304 foil has a poor formability and cannot be used to form a cup using MDD. Serious wrinkling problem occurs on the drawn cup, and the height profile distribution on the mouth and the symmetry of the drawn cup is quite non-uniform when the annealing temperature is 700℃. At annealing temperatures of 900 and 950℃, the drawn cups are both characterized with very few wrinkles, and the distribution of height profile, symmetry and mouth thickness are uniform on the mouths of the drawn cups. The wrinkling becomes increasingly significant with a further increase of annealing temperature from 950 to 1100℃. The optimal annealing temperatures obtained in this study are 900 and 950℃ for reducing the generation of wrinkling, and therefore improving the quality of drawn cups. With non-optimized microstructure, the distribution of the compressive stress in the circumferential direction of the drawn foils becomes inhomogeneous, which is thought to be the cause of the occurrence of localized deformation till wrinkling during MDD.