Advances in Manufacturing最新文献

This paper presents an accurate and efficient method for computing machined part geometry and determining cutter-workpiece engagement (CWE) in multi-axis milling. The proposed method is based on volumetric models, with three types of three-level data structures proposed to represent a solid workpiece voxel model for a sparse and memory-efficient implementation. At each cutter location, every coarse workpiece voxel is efficiently updated from the top to the lower level, and the vertex states and edge intersection points inside each bottom-level voxel crossed by the cutter envelope surface continue to be updated using the dynamic marching cube algorithm. Meanwhile, the finest intersecting voxels are projected onto the cutter surface such that the projected engagement patches connect to form the required engagement map. Finally, according to the lookup table, a triangular mesh of the machined part is built by reconstructing and fusing the approximation polygons inside the bottom-level workpiece surface voxels. Quantitative comparisons of the proposed method against the two-level grid and the tri-dexel model demonstrated the high accuracy and considerable ability of the proposed method to provide more significant and stable efficiency improvement without being affected by a large branching factor owing to its more efficient spatial partitioning.

Microfluidic chips prepared from quartz glass are widely used in medical diagnoses, biochemical analyses, and drug screening. The performance of microfluidic chips is directly determined by the quality of the machined microchannels on high-performance quartz glass. In this study, ultrasonic vibration-assisted grinding (UVAG) is proposed to fabricate quartz glass microchannels with high efficiency and accuracy. A motion model for the trajectory of a single abrasive grain was established, and the intermittent cutting mode of a single abrasive grain was analyzed. Additionally, experiments were conducted to compare the features of UVAG with those of conventional grinding (CG) to investigate the influence of process parameters such as spindle speed, feed speed, grinding depth, and ultrasonic power on the surface roughness and morphology of the ground samples, geometric precision, edge chipping of the microchannels, and wear condition of the grinding tools. Furthermore, the UVAG process parameters were optimized. The results demonstrate that UVAG provides better machining quality and minimizes grinding tool wear. After UVAG, on average, the ground surface roughness, maximum width of edge chipping, wear volume of the grinding tool, and value of the root mean square (RMS) involving geometric precision decreased by 28.107%, 27.464%, 38.072% and 27.212%, respectively. After optimizing the process parameters of UVAG, the surface roughness of the processed quartz glass microchannels reached 0.151 μm, with a geometric precision of 6.152 μm and the maximum edge chipping width of 9.4 μm.

Gallium nitride (GaN) is a third-generation semiconductor and an important optical material requiring high surface integrity. In this study, molecular dynamics simulations were conducted to investigate the machining mechanism of single-crystal GaN during nanometric cutting. The stress distribution and generation/motion of dislocations in GaN during nanometric cutting were found to be closely related to slip systems. The relationship between the crystal phase transformation and dislocations during cutting was also identified. Microcracks occur during the unloading of stress perpendicular to the (0 0 0 1) plane. The fluctuation of the cutting forces during cutting was explained from the perspective of crystal phase transformation. This study helps understand the deformation mechanism of materials with hexagonal close-packed crystal structures in nanometric cutting and promotes the development of relevant mechanical processing technologies.

Self-piercing riveting (SPR) is widely used in thin-walled structures and the automotive industry to join aluminum alloy sheets. Lightweight vehicles are a common trend in the automotive industry. To further reduce vehicle weight and ensure the strength of the AA5052 aluminum alloy thin-sheet joint, the optimization of the amount of material used in the joint should be considered. The effect of the riveting position on the joint strength was investigated using riveting methods with different edge distances. Five edge distances (4.5, 6.5, 8.5, 10.5 and 12.5 mm) along the longitudinal direction were used in the investigations. In addition, a shear test was performed to analyze the mechanical properties of the joint. The results showed that as the edge distance decreased, the damage pattern of the joint changed from rivet pulling out of the plate to tearing at the upper plate edge, and as the edge pitch increased, the lap shear strength gradually increased. The minimum edge distance required to meet the deformation strength of the joint was 8.5 mm. This study provides a reference for reducing the amount of joint material, achieving lightweight production of automobiles, and failure repair of joints.

High-quality development in the manufacturing industry is often accompanied by high energy consumption. The accurate prediction of the energy consumption of computer numerical control (CNC) machine tools, which plays a vital role in manufacturing, is of great importance in energy conservation. However, the existing research ignores the impact of multi-factor energy losses on the performance of machine tool energy consumption prediction models. The existing models must be selected and verified several times to determine the appropriate hyperparameters. Therefore, in this study, a machine tool energy consumption prediction method based on a mechanism and data-driven model that considers multi-factor energy losses and hyperparameter dynamic self-optimization is proposed to improve the accuracy and reduce the difficulty of hyperparameter tuning. The proposed multi-factor energy-loss prediction model is based on the theoretical prediction model of machine-tool cutting energy consumption. After creating the model, a hyperparameter search space embedding a tree-structured Parzen estimator (TPE) was designed based on Hyperopt to dynamically self-optimize the hyperparameters in the deep neural network (DNN) model. Finally, two sets of experiments were designed for verification and comparison with the theoretical and data models. The results showed that the energy consumption prediction performances of the proposed hybrid model in the two sets of experiments were 99% and 97%.

Silicon carbide fiber-reinforced silicon carbide composites are preferred materials for hot-end structural parts of aero-engines. However, their anisotropy, heterogeneity, and ultra-high hardness make them difficult to machine. In this paper, 2.5-dimensional braided SiC_f/SiC composites were processed using a nanosecond pulsed laser. The temperature field distribution at the laser ablated spot is analyzed through finite element modeling (FEM), and the ablation behavior of the two main components, SiC fiber and SiC matrix, is explored. A plasma plume forms when the pulse energy is sufficiently high, which increases with growing energy. The varied ablation behavior of the components is investigated, including the removal rate, ablative morphology, and phase transition. The ablation thresholds of SiC matrix and SiC fiber are found to be 2.538 J/cm² and 3.262 J/cm², respectively.

To enhance the performance of aero-engines, honeycomb seals are commonly used between the stator and rotor to reduce leakage and improve mechanical efficiency. Because of the thin-walled and densely distributed honeycomb holes, machining defects are prone to occur during manufacturing. Electrochemical grinding (ECG) can minimize machining deformation because it is a hybrid process involving electrochemical dissolution and mechanical grinding. However, electrolysis will generate excessive corrosion on the honeycomb surface, which affects the sealing capability and operational performance. In this study, an ECG method using an electrolyte of 10% (mass fraction) NaCl is proposed to machine the inner cylindrical surface of the honeycomb seal, and an eco-friendly inhibitor, sodium dodecylbenzene sulfonate (SDBS), is introduced to the electrolyte to inhibit corrosion of the honeycomb structure. A theoretical relationship between the voltage and feed rate during ECG is proposed, and the excessive corrosion of the honeycomb single-foiled segment is used as a measurement of the impact of electrolysis. The corrosion inhibition efficiency of SDBS on the honeycomb material in 10% (mass fraction) NaCl solution is evaluated through electrochemical tests, and the suitable feed rate and optimal concentration of SDBS are determined through ECG experiments. Additionally, the corrosion inhibition effect of SDBS is validated through four groups of comparative experiments. The results indicate that the inhibition efficiency of SDBS increases with increasing concentration, reaching the maximum of 73.44%. The optimal SDBS mass fraction is determined to be 0.06%. The comparative experiments show that excessive corrosion is reduced by more than 40%. This establishes ECG as an effective and environmentally friendly processing method for honeycomb seals by incorporating SDBS into a 10% (mass fraction) NaCl solution.

Micromilling has been extensively employed in different fields such as aerospace, energy, automobiles, and healthcare because of its efficiency, flexibility, and versatility in materials and structures. Recently, nanofluid minimum quantity lubrication (NMQL) has been proposed as a green and economical cooling and lubrication method to assist the micromilling process; however, its effect is limited because high-speed rotating tools disturb the surrounding air and impede the entrance of the nanofluid. Cold plasma can effectively enhance the wettability of lubricating droplets on the workpiece surface and promote the plastic fracture of materials. Therefore, the multifield coupling of cold plasma and NMQL may provide new insights to overcome this bottleneck. In this study, experiments on cold plasma + NMQL multifield coupling-assisted micromilling of a 7075-T6 aluminum alloy were conducted to analyze the three-dimensional (3D) surface roughness (S_a), surface micromorphology, burrs of the workpiece, and milling force at different micromilling depths. The results indicated that, under cold plasma + NMQL, the workpiece surface micromorphology was smooth with fewer burrs. In comparison with dry, N₂, cold plasma, and NMQL, the S_a values at different cutting depths (5, 10, 15, 20 and 30 μm) were relatively smaller under cold plasma + NMQL with 0.035, 0.036, 0.041, 0.043 and 0.046 μm, which were respectively reduced by 38.9%, 45.7%, 45.9%, 47% and 48.9% when compared to the dry. The effect of cold plasma + NMQL multifield coupling-assisted micromilling on enhancing the workpiece surface quality was analyzed using mechanical analysis of tensile experiments, surface wettability, and X-ray photoelectron spectroscopy (XPS).

Owing to the hard brittle phase organization in their matrixes, brittle materials are prone to the formation of pits and cracks on machined surfaces under extreme grinding conditions, which severely affect the overall performance and service behavior of machined parts. Based on the electroplastic effect of pulsed currents during material deformation, this study investigates electroplastic-assisted grinding with different electrical parameters (current, frequency, and duty cycle). The results demonstrate that compared to conventional grinding, the pulsed current can significantly decrease the surface roughness (S_a) of the workpiece and reduce surface pits and crack defects. The higher the pulsed current, the more pronounced the improvement in the surface quality of the workpiece. Compared to traditional grinding, when the pulsed current is 1 000 A, S_a decreases by 46.4%, and surface pit and crack defects are eliminated. Under the same pulse-current amplitude and frequency conditions, the surface quality continues to improve as the duty cycle increases. When the duty cycle is 75%, S_a reaches a minimum of 0.749 μm. However, the surface quality is insensitive to the pulsed-current frequency. By investigating the influence of pulsed electrical parameters on the surface quality of brittle material under grinding conditions, this study provides a theoretical basis and technical support for improving the machining quality of hard, brittle materials.

New insights are proposed regarding the α′-martensite transformation and strengthening mechanisms of austenitic stainless steel 316L fabricated using selective laser melting (SLM-ed 316L SS). This study investigates the effects of annealing on the microstructural evolution, mechanical properties, and corrosion resistance of SLM-ed 316L SS specimens. The exceptional ultimate tensile strength (807 MPa) and good elongation (24.6%) of SLM-ed 316L SS was achieved by SLM process and annealing treatment at 900 °C for 1 h, which was attributed to effective dislocation strengthening and grain boundary strengthening. During tensile deformation, annealed samples exhibited deformation twinning as a result of the migration from high-angle grain boundaries to low-angle grain boundaries, facilitating the α′-martensite transformation. Consequently, a deformation mechanism model is proposed. The contribution of dislocation strengthening (~61.4%) is the most important strengthening factor for SLM-ed 316L SS annealed 900 °C for 1 h, followed by grain boundary strengthening and solid solution strengthening. Furthermore, the corrosion resistance of SLM-ed 316L SS after annealing treatment is poor due to its limited re-passivation ability.