Jingbo Gai’s research while affiliated with Harbin Engineering University and other places

Trochoidal machining could significantly improve cutting efficiency, enhance cutting stability, reduce cutting temperature, extend tool life, and reduce the cutting costs. However, in trochoidal machining, there are few studies focusing on modelling the instantaneous cutting power due to overlooking the importance of cutting temperature modelling. Also, instantaneous cutting power is an important basis for the optimization of trochoidal parameters and cutting parameters. In this work, we established a new and efficient method that could predict the instantaneous cutting power in trochoidal machining in high fidelity. First, the specific cutting energy of a given workpiece material, cutting tool, and cutting parameter in milling process was calibrated by cutting experiments. Second, the influence of the radial depth of cut on the specific cutting energy in milling process was quantitatively studied. Third, combining the obtained relationship between the specific cutting energy and radial depth of cut, the specific cutting energy curve in trochoidal machining was obtained. Then, a way to figure out the instantaneous material removal rate was proposed based on the acquired instantaneous 3D un-deform chip in trochoidal machining. Finally, based on the obtained specific cutting energy and instantaneous material removal rate, an accurate and efficient approach to predicting the instantaneous cutting power in trochoidal machining was proposed, and a practical application was demonstrated. The effectiveness of the proposed approach was validated by cutting experiments. The method proposed in this work could be adopted in cutting parameter optimization, tool-path optimization, and cutting temperature prediction in trochoidal machining.

As the primary power source for ships, the reliability of electric propulsion systems directly impacts the safety, stability, and economic efficiency of maritime operations. However, the composition of ship electric propulsion systems is complex and is continuously exposed to the dynamic and variable marine environment, which complicates their reliability modeling and analysis. This paper introduces a novel approach to reliability modeling for electric propulsion systems based on the Modelica language. The aim is to overcome the limitations of traditional reliability modeling methods by considering the heterogeneity, dynamicity, and interactivity of electric propulsion systems. The approach addresses system heterogeneity through multi-domain modeling, captures environmental dynamics through parametric modeling, and establishes device interactions using Modelica language connectors. Additionally, modeling efficiency is enhanced by reusing device model packages, which benefits system optimization. Using a specific ship’s electric propulsion system as a case study, the modeling process and simulation results are presented to demonstrate the effectiveness and flexibility of the proposed approach. This approach offers a new tool for reliability modeling of complex electromechanical systems and contributes to enhancing the accuracy and efficiency of system reliability assessments.

Due to the insufficient feature learning ability and the bloated network structure, the gear fault diagnosis methods based on traditional deep neural networks always suffer from poor diagnosis accuracy and low diagnosis efficiency. Therefore, a small channel convolutional neural network under the multiscale fusion attention mechanism (MSFAM‐SCCNN) is proposed in this paper. First, a small channel convolutional neural network (SCCNN) model is constructed based on the framework of the traditional AlexNet model in order to lightweight the network structure and improve the learning efficiency. Then, a novel multiscale fusion attention mechanism (MSFAM) is embedded into the SCCNN model, which utilizes multiscale striped convolutional windows to extract key features from three dimensions, including temporal, spatial, and channel‐wise, resulting in more precise feature mining. Finally, the performance of the MSFAM‐ SCCNN model is verified using the vibration data of tooth‐broken gears obtained by a self‐designed experimental bench of an ammunition supply and delivery system.

The special spatial structure and the long-term harsh working environment make the transient response of wire rope complicated, so accurate assessment and prediction of its mechanical characteristics are of great significance to ensure the safe and stable operation of related equipment. In this paper, a method is proposed for the analysis of the mechanical characteristics of a 6 × K31WS + FC wire rope with or without wire breakage. Firstly, an accurate parametric geometric model of the wire rope is established based on the Frenet frame method, the material properties of the wire rope are acquired by experiments, and the finite element model of the wire rope is built. Then, a mechanical model of the wire rope is proposed to verify the validity of the finite element model. Finally, the influences of the number, distribution, and location of the broken wires on the mechanical characteristics of the wire rope are thoroughly analyzed. This work proposes a comprehensive framework that can quantitatively analyze the mechanical characteristics of the complex wire rope with or without wire breakage, which provides a practical method for its reliability and maintenance evaluation and has guiding significance for the shape design and structural optimization of the wire rope.