Automated crack detection of train rivets using fluorescent magnetic particle inspection and instance segmentation.

The railway rivet is one of the most important and easily damaged parts of the connection. If rivets develop cracks during the production process, their load-bearing capacity will be reduced, thereby increasing the risk of failure. Fluorescent magnetic particle flaw detection (FMPFD) is a widely used inspection method for train fasteners. Manual inspection is not only time-consuming but also prone to miss detection, therefore intelligent detection system has important application value. However, the fluorescent crack images obtained by FMPFD present challenges for intelligent detection, such as the dense, multi-scaled and uninstantiated cracks. In addition, there is limited research on fluorescent rivet crack detection. This paper adopts instance segmentation to achieve automatic cracks detection of rivets. A decentralized target center and low overlap rate labeling method is proposed, and a Gaussian-weighted correction post-processing method is introduced to improve the recall rate in the areas of dense cracks. An efficient channel spatial attention mechanism for feature extraction is proposed in order to enhance the detection of multi-scale cracks. For uninstantiated cracks, an improvement of crack detection in uninstantiated regions based on multi task feature learning is proposed, thoroughly utilizing the semantic and spatial features of the fluorescent cracks. The experimental results show that the improved methods are better than the baseline and some cutting-edge algorithms, achieving a recall rate and mAP0.5 of 86.4% and 90.3%. In addition, a single coil non-contact train rivet composite magnetization device is built for rivets that can magnetize different shapes of rivets and has universality.

[Ag4Br6] cluster-based 3D bromoargentate hybrid: crystal structure, optical/photoelectrical performance and theoretical study.

The self-assembly of cluster-based halide framework materials has been a matter of great interest but with great challenges. Herein, by exploiting hexamethylenetetramine (Hmta) with Td symmetry as a structural modifier, we successfully constructed and systematically characterized an unusual three-dimensional (3D) hybrid bromoargentate, namely K[NH4][Ag4Br6(Hmta)] (1), bearing a diamond-type [Ag4Br6(Hmta)]n2n- anionic skeleton built up from adamantane-like units of inorganic [Ag4Br6] clusters and organic Hmta ligands. UV-Vis diffuse reflectance analysis showed that the optical bandgap of the title compound was 2.68 eV, indicating a visible-light-responsive semiconductive behavior. More importantly, upon alternate light illumination, the so-designed compound exhibited remarkable photoelectric switching properties, with photocurrent densities (0.38 and 1.10 µA cm-2 for visible and full-spectrum light, respectively) that compete well with and even exceed those of some high-performance metal halide counterparts. Further theoretical calculations, including band structure, density of states, and wave functions, revealed that compound 1 has a unique valence band and conduction band distribution, rendering it with small effective masses (especially the electrons), which may be responsible for its good photoelectricity. Furthermore, in this work, Hirshfeld surface analysis, thermogravimetric analysis, and X-ray photoelectron spectroscopy (XPS) studies were performed.

A semiconductive copper iodobismuthate hybrid: structure, optical properties and photocurrent response.

Pursuits of new types of Pb-free heterometallic halides adequate for photovoltaic applications are still urgent but challenging. In this study, by using in situ-produced [(Me)2-(DABCO)]2+ (DABCO = 1,4-diazabicyclo[2.2.2]octane; Me = methyl) cations as structure-directing agents, we successfully constructed a non-perovskite copper iodobismuthate hybrid, namely [(Me)2-(DABCO)]2Cu2Bi2I12 (1), which features discrete [Cu2Bi2I12]4- anionic moieties formed by the building units of [CuI4] tetrahedra and [BiI6] octahedra. UV-Vis diffuse reflectance analyses showed that compound 1 possesses semiconductive behaviors with a narrow optical bandgap of 1.80 eV. More importantly, it exhibits excellent photoelectric switching abilities, and its photocurrent density (2.30 µA cm-2) far exceeds those of some high-performance halide-based counterparts. Different from many heterometallic analogues, noteworthily, it also has dispersive band structure and strong electronic coupling near the Fermi level, resulting in a material with small effective masses that may be responsible for the good photoelectricity. This study may offer new guidance for the design and synthesis of eco-friendly heterometallic halides with unique structures and desirable properties.

A new metal complex-templated silver iodobismuthate exhibiting photocurrent response and photocatalytic property.

An organic-inorganic hybrid silver iodobismuthate characteristic of the infrequent [Ag2BiI6L2] cluster (L = I or I3) and with a unique Ag/Bi molar ratio (2/1), namely, [Zn(bipy)3]2Ag2BiI6(I)1.355(I3)1.645 (bipy = 2,2'-bipyridine; 1), was solvothermally synthesized, and structurally, optically, and theoretically studied. Intriguingly, compound 1 exhibited semiconductor behavior with an optical band gap of 2.33 eV, which endowed it with excellent photoelectric and photocatalytic properties. Electronic structure calculations further revealed that the relative separate conduction band (CB) and valence band (VB) in compound 1 may be responsible for the good optical activity. This study also includes the Hirshfeld surface analyses, thermogravimetric measurements and X-ray photoelectron spectroscopy (XPS) characterization.

Research and Optimization of the Three-Dimensional Printing Unloading Process for the Flexible Support Platform.

At present, a three-dimensional (3D) printing model is unloaded from the working platform manually, which hinders the automation of 3D printing. In this article, a new type of 3D printing auxiliary equipment called flexible support platform is designed to achieve automatic unloading. Unloading problems, especially its principle and influencing factors, are investigated in detail. We also study the build orientation optimization to find the optimum solution for unloading. The improved particle swarm optimization algorithm is applied to avoid falling into local optimum. Furthermore, we combine optimization for unloading with our previous work, which is in terms of support structure reduction. The unloading condition has been transformed into the calculation of added support structure owning to the process improvement, so that the optimized model with minimum support structure can be unloaded successfully. In a word, this article fulfills the need of 3D printing unloading research and achieves efficient automatic unloading.

Thermo-Fluid-Dynamic Modeling of the Melt Pool during Selective Laser Melting for AZ91D Magnesium Alloy.

A three dimensional finite element model (FEM) was established to simulate the temperature distribution, flow activity, and deformation of the melt pool of selective laser melting (SLM) AZ91D magnesium alloy powder. The latent heat in phase transition, Marangoni effect, and the movement of laser beam power with a Gaussian energy distribution were taken into account. The influence of the applied linear laser power on temperature distribution, flow field, and the melt-pool dimensions and shape, as well as resultant densification activity, was investigated and is discussed in this paper. Large temperature gradients and high cooling rates were observed during the process. A violent flow occurred in the melt pool, and the divergent flow makes the melt pool wider and longer but shallower. With the increase of laser power, the melt pool's size increases, but the shape becomes longer and narrower. The width of the melt pool in single-scan experiment is acquired, which is in good agreement with the results predicted by the simulation (with error of 1.49%). This FE model provides an intuitive understanding of the complex physical phenomena that occur during SLM process of AZ91D magnesium alloy. It can help to select the optimal parameters to improve the quality of final parts and reduce the cost of experimental research.

Building Orientation Determination Based on Multi-Objective Optimization for Additive Manufacturing.

Due to the stratified construction of additive manufacturing (AM), the building orientation of an object greatly affects the manufacturing process and quality of the products. This article proposes an optimization algorithm for AM that comprehensively covers the three main aspects affected by the building direction: efficiency, surface quality, and internal properties. The goal of optimizing efficiency is to cut the manufacturing and postprocessing times by reducing the support volume. In terms of surface quality optimization, a new and comprehensive mathematical model is established. Considering not only the influence of the stepping effect on the whole model but also the supporting contact area, the optimization to the salient area was innovatively proposed. The salient area was defined by cone curvature, which has a significant influence on appearance. Another novel point is the quantitative optimization of the internal properties, which is based on the anisotropic characteristics of AM. The mechanical properties are taken into consideration in this article, and other anisotropic properties can be added to the optimization algorithm by using the same method. Afterward, a particle swarm optimization algorithm was adopted to synchronously optimize the targets just cited, according to the degree of importance of each factor for specific applications. The algorithm was implemented to optimize several different cases with different characteristics. Further, the results in the experiments manifested that the models printed in the optimized orientation performed better than the initial models, and the corresponding comprehensive evaluation scores were improved as well, with an optimization range of 70-90%. And the rate of optimization for support volume, surface roughness, salient area roughness, and maximum tensile strength, respectively, reached 20.9%, 57.3%, 59.5%, and 293.0%.

Self-Sensing of Position-Related Loads in Continuous Carbon Fibers-Embedded 3D-Printed Polymer Structures Using Electrical Resistance Measurement.

Condition monitoring in polymer composites and structures based on continuous carbon fibers show overwhelming advantages over other potentially competitive sensing technologies in long-gauge measurements due to their great electromechanical behavior and excellent reinforcement property. Although carbon fibers have been developed as strain- or stress-sensing agents in composite structures through electrical resistance measurements, the electromechanical behavior under flexural loads in terms of different loading positions still lacks adequate research, which is the most common situation in practical applications. This study establishes the relationship between the fractional change in electrical resistance of carbon fibers and the external loads at different loading positions along the fibers' longitudinal direction. An approach for real-time monitoring of flexural loads at different loading positions was presented simultaneously based on this relationship. The effectiveness and feasibility of the approach were verified by experiments on carbon fiber-embedded three-dimensional (3D) printed thermoplastic polymer beam. The error in using the provided approach to monitor the external loads at different loading positions was less than 1.28%. The study fully taps the potential of continuous carbon fibers as long-gauge sensory agents and reinforcement in the 3D-printed polymer structures.