Lei Li

In aviation lubrication systems, changes in lubricant flow and spatial distribution under different operating conditions can affect lubricant delivery and lubrication performance. Existing studies have used flow variables such as velocity and pressure, together with instantaneous spatial distributions, to characterize flow states. Although these quantities can effectively characterize flow states and their evolution, they are often difficult to directly translate into quantitative evaluation results for structural design, comparison of different configurations, and engineering decision-making. To address this issue, a local lubricant availability assessment and candidate-region screening approach is developed based on smoothed particle hydrodynamics (SPH) simulation results. Low-percentile availability A₅, minimum availability A_min, and the longest continuous low-oil duration L₅₀ are jointly used to assess the local lubricant state at different spatial locations. An aviation lubricant tank is considered as the application case, and local lubricant conditions under different initial oil volumes and baffle configurations are compared under identical maneuvering conditions. The results show that, within the investigated volume range, the initial oil volume is closely related to the local lubricant state and the extent of the candidate region. As the oil volume decreases, local lubricant deficiency becomes more pronounced, and the differences among baffle configurations become more evident. At an initial oil volume of 10 L, the cross-baffle configuration provides the largest candidate-region area ratio among the four configurations, reaching 59.39%, compared with 4.19% for the no-baffle case. The proposed framework translates transient flow differences under different operating conditions and structural configurations into spatially explicit candidate regions, providing a quantitative basis for preliminary oil suction port placement.

The structural optimization of internal flow channels in hydraulic systems is critical for enhancing overall performance and achieving lightweight design. However, most existing optimization studies focus primarily on improving flow performance while neglecting manufacturability constraints, resulting in designs that are often infeasible to fabricate and thus hindering their practical application. To address this issue, this study proposes a level set-based process planning strategy tailored for additive–subtractive hybrid manufacturing (ASHM), aiming to resolve the manufacturability challenges associated with flow channels generated by machine learning-based optimization algorithms. The proposed method segments the flow channel into linear and curved sections based on internal wall features. By converting the 3D model into representative 2D feature images, the level set functions of each section are evolved individually and integrated with toolpath constraints to enable automatic identification of manufacturing regions and assignment of appropriate additive or subtractive operations. Through iterative calculations involving geometric relationships and processing trajectories, the start and end positions of additive and subtractive processes are determined, ultimately yielding a comprehensive process plan that includes manufacturability-constrained subdivisions and corresponding fabrication strategies. A case study on an optimized flow channel within a simplified hydraulic manifold demonstrates that the proposed method reduces the pressure drop by 79.41%, thereby validating its effectiveness in terms of machining accuracy and toolpath planning feasibility.

This paper performs orthotropic constitutive modeling for short carbon fiber-reinforced polyetheretherketone (CF-PEEK) composites fabricated using material extrusion 3D printing technology. A variety of specimens for tensile, compressive, and shear tests are 3D printed under different deposition path patterns. The related experimental results disclose the strong directional mechanical properties, including tensile/compressive modulus and strength. The Tsai–Wu failure criterion is also developed based on the experimental data. The tensile–compressive behavior models of different orthotropic materials were constructed by importing the experimental data into COMSOL, followed by a compression simulation of the S-shaped specimen. The experimental results of the S-shaped compression test were compared with the COMSOL-based simulation analysis, which validated the effectiveness of the Tsai–Wu failure prediction. The predicted failure timings, locations and load–displacement curves all show a good agreement with experimental observations. Furthermore, the Tsai–Wu failure index is incorporated as a stress constraint in structural topology optimization, showing the effect of significantly reduced stress concentration. These findings and data will be supportive for the design and optimization of 3D printed CF-PEEK composites.

To enhance the mechanical properties of bioceramic composite bone scaffolds, a composite paste of polyethylene glycol diacrylate (PEGDA) and hexagonal boron nitride (h‐BN) with a solid content of up to 42 wt% was developed in this study. The part was produced using stereolithography (SLA). The SLA printing parameters and material ratios of the paste were optimized through a homogeneous design and mechanical property tests. The results indicate that the single‐line curing width of the PEGDA/h‐BN paste in the XY plane ranged from 172 to 209 μm, while the curing depth of a single layer along the Z‐axis ranged from 99 to 154 μm. Laser power was identified as the primary factor influencing both the width and depth of curing. The compressive strength of the printed sample of PEGDA/h‐BN ceramic composite paste was measured at 222.4 ± 5.6 MPa, which is 61 times greater than the compressive strength of a pure PEGDA structure and comparable to that of cortical bone (100‐230 MPa). Finally, the superior biocompatibility of PEGDA/h‐BN ceramics was confirmed through cytotoxicity experiments. Consequently, PEGDA/h‐BN ceramic composites meet the mechanical properties and biocompatibility requirements necessary for bone tissue applications, indicating promising potential in the field of bone tissue engineering.

A topology optimization approach is proposed for the design of self-supporting structures for digital light projector (DLP) 3D printing. This method accounts for the adhesion forces between the print part and the resin base during DLP printing to avoid failure of the part due to stress concentration and weak connections. Specifically, the effect of the process-related adhesion forces is first simulated by developing a design variable-interpolated finite element model to capture the intricate mechanical behavior during DLP 3D printing. Guided by the process model, a stress-constrained topology optimization algorithm is formulated with both the SIMP and RAMP interpolation schemes. The interpolations on the stress term and the design-dependent adhesion load are carefully investigated. A sensitivity result on the P-norm stress constraint is fully developed. Finally, the approach is applied to several 2D benchmark examples to validate its efficacy in controlling the process-caused peak P-norm stresses. The effects of alternating between the SIMP and RAMP interpolations and changing the stress upper limits are carefully explored during the numerical trials. Moreover, 3D printing tests are performed to validate the improvement in printability when involving the process-related P-norm stress constraint.

Sialon ceramic tool material has become one of the most ideal materials for the high-speed cutting of superalloy materials. However, studies on the geometric structure of sialon ceramic solid end mill is lacking at the present. In this work, the geometric structure of sialon ceramic end mills was designed for difficult-to-machine nickel-based superalloy materials. The cutting force and heat, flank wear and machined surface quality were analyzed to study the effect of the main parameters on tool performance. The results showed that the end mill experienced severe flank wear and chipping, which were the leading cause of its failure during high-speed cutting. The cutting force and temperature decreased gradually with the increase in the helix angle. With the increase in the rake angle, the flank wear and the quality of the machined surface of the specimen first decreased and then increased. With the increase in the relief angle, the cutting temperature of the ceramic end mill gradually decreased, and the cutting force and the machined surface roughness showed an initial decrease and then increased. When the helix angle, rake angle and relief angle were 35°, −15° and 12°, respectively, the sialon ceramic end mill exhibited the best cutting performance and obtained better machined surface quality in the nickel-based superalloys.

When detecting surface defects in the industrial cutting environment, the defects are easily contaminated and covered by many interference factors (such as chips and coolant residue) that exist on the machined surface. These interfering factors hinder the sustainable detection of surface defects. Furthermore, addressing the challenge of detecting surface defects in the presence of interference factors has proven to be a difficult problem in the current detection field. To solve this problem, a sustainable detection method for surface defects is proposed. The method is divided into two steps: one is the identification and removal of interference factors; the other is the detection of surface defects. First, a new FPN-DepResUnet model is constructed by modifying the Unet model from three aspects. The FPN-DepResUnet model is used to identify the interference factors in the image. Compared to the Unet model, the MAP of the FPN-DepResUnet model is increased by 5.77%, reaching 94.82%. The interfering factors are then removed using the RFR-net model. The RFR-net model performs point-to-point repair of interference regions. The repair process is performed by finding high-quality pixels similar to the interference region from the rest of the image. The negative effects of the interfering factors are removed by combining the FPN-DepResUnet model with the RFR-net model. On this basis, the SAM-Mask RCNN model is proposed for efficient defect detection of clean surface images. Compared with the Mask RCNN model, the MAP of the proposed SAM-Mask RCNN model increased by 2.00%, reaching 94.62%. Further, the inspection results can be fed back with a variety of surface defect information including defect types, the number of pixels in the different defect regions, and the proportion of different defect regions in the entire image. This enables predictive maintenance and control of the machined surface quality during machining.

In order to solve the problems of high lower limit and low accuracy of existing flow testing techniques for horizontal well output profiles, a phase-regulated volumetric flow meter was designed and a single-phase flow measurement method was proposed. Firstly, Gambit was used to establish the simulation model of the flowmeter; then the finite element simulation software FLUENT was used to study the dynamic regulation effect of the flowmeter for oil and water two-phase flow under the conditions of different flow rates and water content, which verified the feasibility of the flowmeter; finally, according to the simulation monitoring data, the response value of the array conductivity sensor under the oil and water measurement mode was obtained respectively with the change of liquid level, and finally the single-phase flow rate of oil and water was deduced.