Journal of Mechanical Science and Technology最新文献

The advantage of 3D printing is that it can create creative shapes that cannot be manufactured through existing subtractive manufacturing. It can also produce products with improved functionality. In this study, a radial structure with TPMS is applied to improve the temperature uniformity of the wafer prober lower chuck used in semiconductor inspection equipment. The gyroid solid-TPMS structure, the most basic of TPMS structures, is considered, and a 1/9 analysis model is constructed considering that it is an axis-symmetric structure, and optimization is performed through thermal flow analysis. Based on the proposed design, it was confirmed that when the optimization result offset was +0.57 mm, the temperature standard deviation was 0.01 °C and the pressure was 0.40 bar. Specimens were manufactured using the proposed optimal design, and the optimization results were verified through lab-based experiment.

Capsules applied in large-volume and unstructured organs, such as stomach and colon, should perform multimodal motion to effectively accomplish gastrointestinal tract diagnosis. For this purpose, a magnetic navigated dual-hemisphere capsule robot (DHCR) actuated by the spatial universal rotating magnetic field (SURMF) is proposed, utilizing its passive and active modes for fixed-point posture adjustment and rolling locomotion, respectively. The DHCR exhibits a distinctive physical property—self-standing characteristics, which can specify vertically upward orientation as the starting posture and benchmark for multimodal motion after the DHCR undergoes complex multimodal (pseudo-active, active, and passive modes) conversion. On the basis of the momentum moment theorem, a general dynamic model is devised to reveal the mechanism of multimodal transition through stability and posture response. Results show that the DHCR can flip spontaneously after applying SURMF and is finally in a stable passive mode with the vertically upward DHCR axis. Finally, the multimodal conversion of the DHCR is tested in turning navigation within the curved intestine, providing a new method of endoscopic diagnosis.

Accurate tool wear state identification models are essential to ensure manufacturing reliability and efficiency. Tool wear state recognition systems establish a mapping relationship with the tool state by extracting signal features. Therefore, this paper proposes an architecture for identifying the actual wear state of data unbalanced machining tools by applying the power of minimalism in deep learning networks, namely, VanillaNet, combined with recurrence plot encoding technology (RP). In this paper, the signal is preprocessed by RP, and the nonlinear one-dimensional time-sequential digital signal embedded in variable time-lag delay coordinate space in the phase space is converted into a two-dimensional (2D) color texture image, thereby achieving the feature extraction of tool wear. Then, the data-enhanced 2D recurrence coded image is used as the input to VanillaNet, and its minimalist network architecture is applied to establish the mapping relationship between tool wear states and wear features. This process reduces the state recognition time and achieves the fast recognition of tool wear states. The model in this paper achieves more than 95 % on all four classification metrics: accuracy, recall, F1 score, and precision in three sets of crossover experiments while reducing misclassification in the sharp wear phase. The proposed model also outperforms three DL-based methods, namely, CNN-Attention, AlexNet, and ResNet.

Generative design refers to a methodology that not only simulates the characteristics of a given data or system but also creates artificial data for various purposes. It’s a significant research area encompassing diverse issues such as privacy preservation, data distribution analysis, and the development of surrogate models. Initially, research in this field primarily employed stochastic models or basic machine learning methods. However, with the advancement of deep learning technology, numerous studies have emerged, showcasing developed mechanisms using artificial neural network-based methods like variational autoencoders (VAEs) and generative adversarial networks (GANs). These studies extend across different data types, including images and texts, tailored to specific objectives. This paper presents a systematic review of generative design research focused on tabular data. We begin by elucidating the characteristics of tabular data within generative design, followed by a discussion on the goals and challenges in this area. Subsequently, the paper introduces various generative design studies on tabular data, categorized according to their methodological development and unique objectives. Finally, we address the benchmark methods used in generative design for tabular and how their performance is evaluated.

Traditional fabrication of ceramic parts face limitations due to hardness and brittleness, despite of having good mechanical properties. Digital light processing (DLP) additive manufacturing technology offers promising way to fabricate intricate geometry of ceramic parts. To fabricate high performance, maximizing solid loading of ceramic powder is important to reduce the shrinkage and distortion during post-processing. However, it increases viscosity dramatically and makes difficult with material supply during printing process. Therefore, not only increasing the solid loading of ceramic powder but also minimizing random defects during printing process is essential for us to achieve high-quality ceramic parts. In this study, vision-based defect monitoring system using Mask R-CNN model was employed. We classified two types of defects called pinhole and un-even paste and quantify the defect characteristics such as number and size with pixel level. This method provides us the basis of a feed-back system for controlling the process parameters in real time.

Selective laser melting (SLM) offers an innovative manufacturing approach compared to traditional methods. It has found diverse applications in various fields, ranging from medical devices to aerospace, using a variety of metals. However, products manufactured with the metal AlSi10Mg using the SLM technique have a significant surface roughness defect. Consequently, products produced through SLM necessitate post-processing. In this study, electro-polishing was conducted on AlSi10Mg specimens manufactured at four different inclinations. Surface roughness measurements and 3D scans were performed at various time intervals to observe changes in roughness. It was observed that an increase in electro-polishing time effectively reduced surface roughness. This research provides data on post-processing for three-dimensional structures manufactured using the SLM technique.

Rotating machinery is critical functional part of industrial mechanical equipment, and health status of rotating machinery is closely related to equipment stability, reliability and safety. Vibration signals for health prediction are often collected under operating conditions with variable loads and speeds, which makes health prediction more challenging. STFT-based time-frequency representation methods are widely used for the health prediction of rotating machinery. However, these methods lack specific learning mechanisms to effectively distinguish the time-frequency representations at different time points and frequency bands and highlight important feature information. To vanquish the weakness, this paper develops a novel dynamic adaptive time-frequency attention residual network (TFARNet) for rotating machinery intelligent health prediction. Firstly, a new adaptive STFT time-frequency attention (TFA) unit is developed to recalibrate time-frequency features, thereby enhancing important information and suppressing redundant information. Secondly, the TFA unit is inserted into the residual network, by stacking multiple residual blocks and TFA units to establish TFARNet and efficiently learn more discriminative features. Thirdly, label smoothing regularization and dynamic learning rate are employed to accelerate model convergence and optimize the model training process. Finally, three cases are carried out to evaluate the developed method. Compared with the other seven health prediction methods, the developed method can achieve higher prediction accuracy.

For the development of a digital valve, modularization, miniaturization, and low-temperature rise are crucial factors that need to be balanced against each other. A digital valve with integrated multiple coils based on a rectangular section magnetic sleeve is proposed to improve performance. To predicate the thermal performance of the proposed digital valve accurately and rapidly, a nonlinear thermal equivalent circuit (NTEC) model was developed. The most significant distinction of this model is its consideration of the changes in convective thermal resistance based on the operating temperature and installation state. This model has been derived and evaluated by simulation and experiments. The experiment results show that the temperature responses of four key test types obtained from the NTEC model have fairly good correspondence with to those from the experiment and the maximum temperature rise deviation is below 17 %. Finally, a comparison study with a C-type sleeve is demonstrated, indicating that the maximum temperature decline of the coil is 26.1 °C under a vertical state.

As a type of flexible seal, finger seal has attracted wide attention due to its low cost and leakage rate. Its potential in engineering applications has been demonstrated by numerous experiments. The deformation of the pad under load during the operation of non-contact finger seal is a critical factor that limits the performance. To investigate the change of the seal force of finger beams and pads, it has been focused on the non-contact finger seal, and the force on low-pressure finger beam has been simulated and analyzed. By differentiating the force acting on finger pad, the simulation on different regions of the finger pad has been considered. The seal force distribution coefficient has been introduced, the results are unified to obtain the final seal force, and its rationality is verified by comparing it with existing results. Subsequently, the relationship between pressure drop, eccentricity, rotor speed, friction coefficient, finger pad size, and finger seal force is explored. It has demonstrated that, at the same eccentricity, the seal force decreases as the pressure difference increases. A larger eccentricity leads to better seal performance. Additionally, it has been observed that the size of the finger pad affects seal force of the beam. Specifically, an increase in the axial dimension of the pad results in a larger force. Through the analysis of the resultant force on the rotor on the whole ring finger seal, the result is closer to the engineering project. The deformation of finger pad is studied, and the stress on pad is analyzed by analytical method, which lays a foundation for further study of finger seal performance.

This article addresses butt joining of 1.2 mm thick Ni-Cr superalloy 80 A sheets using a 3.5 kW CO₂ laser beam welding (LBW) process, which is critical for gas turbine components, nuclear tube supports and automotive valves in high temperature applications. LBW, chosen for its low heat input and minimal heat-affected zones, was optimized using Taguchi-based gray relationship analysis. The L9 orthogonal array experiment identified the optimal parameter sets for welding speed, laser power, focal length, and shielding gas flow rate that affect tensile strength, microhardness, penetration depth and weld bead. The results were validated using ANOVA analysis, fractography, hardness testing, micrographs and tensile tests. Microstructural variations in fusion and heat affected zones. The optimized parameters resulted in a tensile strength of 817 MPa and a microhardness of 292 HV, demonstrating improved weld quality.