Advances in Manufacturing最新文献

Owing to their brittleness and heterogeneity, achieving carbon fiber-reinforced silicon carbide ceramic (C_f/SiC) composites with ideal dimensional and shape accuracy is difficult. In this study, unidirectional C_f materials were subjected to orthogonal grinding experiments using different fiber orientations. Through a combined analysis of the surface morphology and grinding force after processing, the mechanism underlying the effect of the fiber orientation on the surface morphology of the material was explained. The surface roughness of the material was less affected by the process parameters and fluctuated around the fiber radius scale; the average surface roughness (R_a) in the direction of scratching parallel (SA) and perpendicular (SB) to the fiber direction was 4.21‒5.00 μm and 4.42‒5.26 μm, respectively; the material was mainly removed via the brittle removal mechanism; and the main defects of the fiber in the SA direction were tensile fracture and extrusion fracture; the main defects of the fiber in the SB direction were bending fracture, shear fracture, and fiber debonding. The grinding parameters influenced the grinding force in the order: depth of cut > feed rate > wheel speed. The grinding force increased with an increase in the feed rate or depth of cut and decreased with an increase in the wheel speed. Moreover, increasing the depth of cut was more effective in decreasing the grinding force and improving the material removal efficiency than adjusting the rotational speed of the workpiece and the rotational speed of the grinding wheel. The specific grinding energy decreased with an increase in the feed rate or depth of cut, and increased with an increase in the grinding wheel speed.

Electrical arc machining (EAM) is an efficient process for machining difficult-to-cut materials. However, limited research has been conducted on sloped surface machining within this context, constraining the further application for complex components. This study conducts bevel machining experiments, pointing out that the surface quality becomes unsatisfactory with the increasing bevel angle. The discharge condition is counted and analyzed, while the flow field and the removed particle movement of the discharge gap are simulated, demonstrating the primary factor contributing to the degradation of surface quality, namely the loss of flushing. This weakens both the plasma control effect and debris evacuation, leading to the poor discharge condition. To address this issue, the magnetic field is implemented in blasting erosion arc machining (BEAM). The application of a magnetic field effectively regulates the arc plasma, enhances debris expulsion, and significantly improves the discharge conditions, resulting in a smoother and more uniform sloped surface with a reduced recast layer thickness. This approach provides the possibility of applying BEAM to complex parts made of difficult-to-cut materials in aerospace and military industries.

The precision, lifespan, and stability of the electro-hydraulic servo valve sleeve are significantly impacted by the edge burrs that are easily created when honing the valve sleeve. The existing deburring process mainly rely on manual operation with high cost and low efficiency. This paper focuses on reducing the burr size during the machining process. In this paper, a single-scratch test with a finite element simulation model is conducted to study the mechanism of burr generation. The tests were carried out under ultrasonic vibration and non-ultrasonic vibration conditions to explore the effect of ultrasonic vibration on burrs. Besides, a honing experiment is conducted to verify the conclusions. The results at various cutting parameters are analyzed, and the mechanism of burr generation is revealed. The stiffness lacking of the workpiece edge material is the main reason for the burr generation. The cutting depth shows a significant effect on burr size while the cutting speed does not. The inhibition mechanism of ultrasonic vibration on burrs is also revealed. The separation of the burr stress field under ultrasonic vibration and the higher bending hinge point is the reason for burr fracturing. The re-cutting effect of ultrasonic vibration reduces the burr growth rate. The results of the honing experiment verified these conclusions and obtained a combination of honing parameters to minimize the burr growth rate.

High precision and minimal noise are considered critical performance measures for top-tier gear transmission systems. To ensure optimum gear trans mission performance, the tooth surface texture should be enhanced without comparing the gear precision. By integrating the principle of internal gearing power honing with tooth surface topology modifications, the adjusted honing texture can be forecasted, and proactive control can be achieved, both of which are considered as crucial for the reduction of gear vibration and noise. In this study, a manufacturing technique for high-order modified helical gears is introduced. The formation rules and modeling of the honing texture are explored, leading to a novel method for three-dimensional modeling and control of the altered honing texture. The direction of the cutting speed of abrasive grains at the contact point between the honing wheel and working gear tooth surface was examined. Using the discrete abrasive grain motion trajectory method, the honing texture was produced, through which the formation mechanisms and control strategies of the curved honing texture were illuminated. Based on these findings, a method for flexible topology modifications of the tooth surface is suggested. This is achieved by adjusting the motion coefficients of each axis of the honing machine and adding additional motion in the form of higher-order polynomials to three motion axes, including the radial feed and oscillation axes of the honing wheel and the interleaved axes of the work gear and honing wheel. A least-squares estimation method, based on a sensitivity matrix, was employed to determine the additional motion coefficients. By this method, the texture of the modified tooth surface can also be predicted and controlled. In a numerical example, the efficacy of the flexible topology modification method was confirmed. In this case, the altered honing texture was managed by modifying the axis intersection angle, while the accuracy of tooth surface modifications was maintained. This study has theoretical and application value in the field of gear manufacturing, oriented to the demand for gear vibration and noise reduction functions.

Advances in artificial intelligence (AI) technology are propelling the rapid development of automotive intelligent cockpits. The active perception of driver emotions significantly impacts road traffic safety. Consequently, the development of driver emotion recognition technology is crucial for ensuring driving safety in the advanced driver assistance system (ADAS) of the automotive intelligent cockpit. The ongoing advancements in AI technology offer a compelling avenue for implementing proactive affective interaction technology. This study introduced the multimodal driver emotion recognition network (MDERNet), a dual-branch deep learning network that temporally fused driver facial expression features and driving behavior features for non-contact driver emotion recognition. The proposed model was validated on publicly available datasets such as CK+, RAVDESS, DEAP, and PPB-Emo, recognizing discrete and dimensional emotions. The results indicated that the proposed model demonstrated advanced recognition performance, and ablation experiments confirmed the significance of various model components. The proposed method serves as a fundamental reference for multimodal feature fusion in driver emotion recognition and contributes to the advancement of ADAS within automotive intelligent cockpits.

It is necessary to improve the surface performance of bearing rings and extend the service life of bearings. In this study, ultrasonic vibration-assisted grinding (UVAG) was applied to process GCr15SiMn bearing steel, considering the effects of grinding-wheel wear, overlap of abrasive motion tracks under ultrasonic conditions, elastic yield of abrasives, and elastic recovery of the workpiece on the machined surface. In addition, a novel mathematical model was established to predict surface roughness (R_a). The proposed model was validated experimentally, and the predicted and experimental results showed similar trends under various processing parameters, with both within an error range of 12%–20%. The relationships between the machining parameters and R_a for the two grinding methods were further investigated. The results showed that increases in the grinding speed and ultrasonic amplitude resulted in a decrease in R_a, whereas increases in the grinding depth and workpiece speed resulted in an increase in R_a. Furthermore, the R_a values obtained using the UVAG method were lower than those of conventional grinding (CG). Finally, the influence of ultrasonic vibration on the surface topography was investigated. Severe tearing occurred on the CG surface, whereas no evident defects were observed on the ultrasonically machined surface. The surface quality was improved by increasing the ultrasonic amplitude such that it did not exceed 4 μm, and a further increase in ultrasonic amplitude deteriorated the surface topography. Nevertheless, this improvement was superior to that of the CG surface and was consistent with the variation in R_a.

In the laser-directed energy deposition (L-DED) process, achieving an efficient temperature evolution prediction of molten pools is critical but challenging. To resolve this issue, this study presents an innovative approach that integrates a high-fidelity finite element (FE) model and an effective machine-learning model. Firstly, a high-fidelity FE model for the L-DED process was developed and subsequently validated through an experimental examination of the cross-sectional geometries of the molten pools and temperature fields of the substrate. Then, a Bi-directional gated recurrent unit (Bi-GRU) was formulated to predict the temperature evolution of the molten pools during L-DED. By training the Bi-GRU model using datasets generated from the FE model, it was deployed to efficiently predict the temperature evolution of the manufactured multi-layer single-bead walls. The results demonstrated that, in terms of the average mean absolute error, this approach outperformed other approaches designed based on the gated recurrent unit (GRU) model, long short-term memory model, and recurrent neural network models by 26.7%, 52.1%, and 65.2%, respectively. The results also showed that the prediction time required by this approach, once trained, was significantly reduced by five orders of magnitude compared with the FE model. Therefore, this approach accurately predicts the temperature evolution of multi-layer single-bead walls in a computationally efficient manner. This approach is a promising solution for supporting the real-time control of the L-DED process in industrial applications.

Particle-reinforced metal matrix composites (PMMCs) exhibit exceptional mechanical properties, rendering them highly promising for extensive applications in aerospace, military, automotive, and other critical sectors. The distinct physical properties of the matrix and reinforcement result in a poor machining performance, particularly owing to the continuous increase in the particle content of the reinforcement phase. This has become a major obstacle in achieving the efficient and precise machining of PMMCs. The grinding process, which is a highly precise machining method, has been extensively employed to achieve precision machining of metal matrix composites. Firstly, the classification of PMMCs is presented, and the grinding removal mechanism of this material is elaborated. Recent studies have examined the impact of various factors on the grinding performance, including the grinding force, grinding temperature, grinding force ratio, specific grinding energy, surface integrity, and wheel wear. The application status of various grinding methods for PMMCs is also summarized. Finally, the difficulties and challenges in achieving high-efficiency precision grinding technology for PMMCs are summarized and discussed.

With the aid of computer aided design (CAD) and building information modelling (BIM), as-built to as-designed comparison has seen many developments in improving the workflow of manufacturing and construction tasks. Recently, evolution has been centred around automation of scene interpretation from three-dimensional (3D) scan data. The scope of this paper is to assess assemblies as the installation process progresses and inferring if arising deviations are problematic (complex task). The adequacy of utilising unorganised point clouds to compliance check are trialled with a real life down-scaled prototype model in conjunction with a synthetic dataset. This work aims to highlight areas where large rework could be avoided, in part by the detection of potential clashes of components early in the pipeline installation process. With the help of an extracted model in the form of a point cloud generated from a scanned physical model and a 3D CAD model representing the nominal geometry, an operator can be made visually aware of potential deviations and component clashes during a pipeline assembly process.