Search Results (466)

Molybdenum (Mo), owing to its excellent properties, is widely used as a plasma-facing material and is recognized as a typical difficult-to-machine material. Achieving high-quality, low-damage machining is essential for ensuring the service reliability of Mo components. However, studies on the milling of Mo remain limited. Therefore, this study investigates a high-quality, low-damage milling technique for Mo based on analyses of milling force, machined surface roughness, and white layer formation. First, the effects of machining parameters, including radial depth of cut (a_e), spindle speed (n), and feed per tooth (f_z), on the responses, namely milling force (F) and surface roughness (R_a), were investigated. The relationships between milling force, surface roughness, and white layer formation were analyzed. Subsequently, the response surface methodology (RSM) was employed to reveal the influence mechanisms of the machining parameters and their interactions on the response variables. Finally, a Kriging surrogate model integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was adopted to identify the optimal machining parameter combination for high-quality, low-damage milling. The results indicate that the milling force and white-layer thickness exhibit consistent increasing trends with increasing feed per tooth under the investigated conditions, demonstrating that controlling the milling force is an effective approach for achieving high-quality, low-damage milling of Mo. For the simultaneous minimization of milling force and surface roughness, the optimal machining parameters were determined to be a radial depth of cut of 0.2101 mm, a spindle speed of 10,090.7 rpm, and a feed per tooth of 0.01 mm/z. These findings provide valuable process parameter guidance for the precision machining of Mo components. Full article

To address severe PCD tool wear during the milling of high-volume-fraction SiC_p/Al composites, a synergistic milling process coupling pulsed laser pretreatment with ultrasonic vibration was proposed. Five-factor, four-level orthogonal experiments were conducted on 70 vol.% SiC_p/Al composites to investigate the effects of milling speed, feed per tooth, cutting depth, laser power, and ultrasonic amplitude on milling forces and tool wear, and a tool wear prediction model was established. The results showed that the factors influencing tool wear, in descending order, were feed per tooth, cutting depth, milling speed, laser power, and ultrasonic amplitude. Appropriate laser power and ultrasonic amplitude reduced cutting loads and suppressed tool wear. The model achieved a coefficient of determination of 0.7887 and was statistically significant overall. The optimal parameter combination was 50 m/min, 0.02 mm/z, 0.1 mm, 60 W, and 3.5 μm, under which the tool wear loss was 1.0 mg, representing a reduction of 61.54% compared with the maximum-wear condition. The main wear modes of the PCD tool included rake-face grooving and fatigue spalling, flank-face abrasive wear, and cutting-edge micro-chipping. These findings provide a useful reference for the precision milling of SiC_p/Al composites. Full article

To achieve the multi-objective collaborative optimization of milling processes, orthogonal experiments are conducted to develop prediction models for vibration acceleration and milling force, and range analysis together with variance analysis are adopted to reveal the sensitivity of each milling parameter to machining performance. Taking low vibration, small milling force and high material removal rate (MRR) as optimization objectives, the Multi-Objective Whale Optimization Algorithm (MOWOA) is employed to tackle this multi-criteria optimization problem, and a set of Pareto non-dominated solutions with balanced trade-offs are acquired. By integrating the weight assignment of the Analytic Hierarchy Process (AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), comprehensive decision-making for all candidate schemes is implemented in accordance with practical machining requirements of users, and the optimal milling process parameters are determined. The results indicate an inherent trade-off among machining efficiency, milling load and machine tool vibration. An increase in the material removal rate will inevitably lead to simultaneous rises in milling force and machine tool vibration magnitude. The optimal combination of process parameters screened to meet comprehensive multi-objective requirements is spindle speed n = 12,000.00 r/min, feed rate v_f = 1048.26 mm/min, and axial milling depth a_p = 3.00 mm. Full article

To address over-processing and limited process-control precision in rice manufacturing, this study developed an artificial intelligence (AI)-assisted optimization framework for rice milling. A data-driven predictive model was constructed to characterize the nonlinear relationships between milling conditions and the in vitro starch-digestibility and sensory attributes of rice. Across the nine physicochemical, in vitro starch-digestibility, and sensory indicators, RiceMambaOpt achieved a mean coefficient of determination (R²) of 0.975. Explainability analyses were used to examine process–quality relationships and characterize nonlinear trade-offs among appearance, texture, and starch-digestibility attributes across rice cultivars with different genetic backgrounds. A target-oriented inverse optimization procedure was then developed, which estimates feasible process parameters subject to process-feasibility constraints, tailored to differentiated orientations such as low rapidly digestible starch (RDS) content or high palatability. On the independent 100-sample test set, the inverse predictions achieved a milling-time MAE of 0.960 s with an R² of 0.986 and a milling-speed MAE of 19.522 r/min with an R² of 0.851. In a prospective experimental validation, 10 of the 12 prespecified target quality profiles (83.3%) were attained across 36 independently milled samples, with an RDS mean absolute error of 1.8 percentage points and a joint normalized root-mean-square error of 6.7%. For the Qiuguang cultivar, the model identified a representative processing condition of 38.5 s and 1020 r/min, corresponding to a predicted in vitro RDS content of 21.6% and a palatability score of 26.5. The results demonstrate the feasibility of combining predictive modeling with process-feasibility-constrained inverse optimization and provide a computational approach for investigating moderate rice-milling conditions. Full article

The large aspect ratio micro end mill is a critical tool for microstructure machining, and its performance directly determines processing quality and efficiency. Diamond coatings are commonly applied to cutting edges to enhance wear resistance and extend tool life. However, existing coating techniques often suffer from poor uniformity and inadequate consistency, limiting batch production and process stability. Aerosol jet printing (AJP) offers a cost-effective and highly controllable alternative for the efficient, large-scale deposition of diamond coatings on micro end mills, where precise control of line spacing is essential to achieving coating uniformity. In this study, a transient numerical model of droplet deposition in AJP is developed using computational fluid dynamics (CFD). The volume of fluid (VOF) method and the discrete phase model (DPM) are coupled to track liquid–gas interface deformation and diamond particle motion, enabling the dynamic evolution of droplet deposition to be captured. The effects of inter-droplet distance on deposition, spreading, coalescence, and line uniformity are systematically investigated. Droplet deposition mechanisms are analyzed under low-speed jetting conditions, while high-speed jetting simulations are conducted to reflect industrial processing scenarios. The results show that under low-speed jetting, droplets undergo spreading, contraction, and rebound, eventually forming a uniform cap-like structure. Under high-speed jetting, droplets exhibit a dispersed ring-shaped spreading pattern; although uniformity is slightly reduced, the spreading area and deposition efficiency are significantly increased. These findings provide a theoretical basis for optimizing AJP process parameters to achieve high-quality diamond coatings on micro end mills. Full article

Frozen sand molds exhibit broad application prospects in aerospace, large-scale complex castings, and high-end equipment manufacturing owing to their high-strength particle-bonding structure and excellent low-temperature stability. However, their brittle–plastic characteristics make them susceptible to collapse, spalling, and load fluctuations during conventional milling, resulting in nonlinear and unstable milling force behavior. To address this issue, a longitudinal–torsional resonant ultrasonic-assisted milling method was proposed, and an instantaneous milling force model incorporating the effective cutting time was established based on the elemental cutting theory and the oblique cutting force model. Through a series of milling experiments, the milling force coefficients at different spindle speeds were calibrated using the average milling force coefficient method. The identified milling force coefficient models exhibited high fitting accuracy, with coefficients of determination ($R^2$) exceeding 0.9. The developed model was then employed to investigate the effects of various machining conditions on the milling forces of frozen sand molds. The relative error between the predicted and experimentally measured average milling forces was calculated to evaluate the prediction accuracy. The results show that the relative errors between the predicted and experimental milling forces in the X-, Y-, and Z-directions were 9.76%, 8.43%, and 8.45%, respectively, all below 10%, demonstrating the reliability and accuracy of the proposed model. Cutting depth and cutting width were identified as the dominant factors affecting the milling force, whereas the ultrasonic-assisted milling process effectively reduced the milling force, with the most pronounced load-reduction effect observed for conventionally prepared frozen sand molds. This study provides a theoretical basis and practical guidance for process optimization and parameter selection for the efficient and low-load machining of frozen sand molds. Full article

Aluminum honeycomb structures are widely used in the aeronautical, aerospace, marine, and automotive industries due to their excellent stiffness-to-weight ratio. However, machining these structures remains highly challenging because their thin, highly flexible cell walls are susceptible to plastic deformation and geometric defects. To overcome these limitations, this study proposes an innovative machining approach that combines longitudinal-torsional ultrasonic vibration-assisted machining (LT-UVAM) with a 55-tooth CZD10 cutting tool. A three-dimensional finite element model was developed using Abaqus/Explicit 2017 to simulate the dynamic interactions between the cutting tool and the honeycomb cell walls during the milling process. Following experimental validation on a high-speed machining center, the model was employed to investigate the effects of cutting and vibration parameters on the machining performance. The results demonstrate that longitudinal-torsional ultrasonic vibration coupling significantly reduces the cutting forces, resulting in a 26% to 42% reduction in the axial force component (Fz). Furthermore, vibration assistance effectively limits cell wall deflection, reducing the stress levels by up to 60% in the thinnest walls while maintaining them below the critical Euler buckling load. Furthermore, an ultrasonic vibration frequency of 22.5 kHz almost completely eliminates plastic deformation, while a vibration amplitude of 25 µm significantly reduces tool wear by promoting intermittent tool–workpiece contact, thereby facilitating chip evacuation. Ultimately, the LT-UVAM process produces finer and more uniform chips, leading to improved machining quality, enhanced dimensional accuracy, and extended tool life. Full article

In precision machining, tool wear is one of the primary factors affecting machining quality and production efficiency. This study developed intelligent prediction models for tool wear in the high-speed milling (HSM) of AISI 1045 medium-carbon steel by integrating robust process design with backpropagation neural networks (BPNN) and adaptive neuro-fuzzy inference systems (ANFIS). Robust process design was employed to optimize the machining parameters, while the hyperparameters of both BPNN and ANFIS models were systematically optimized to improve prediction performance. Tool wear was measured after a fixed cutting length and used to establish the prediction models. The optimized machining parameters reduced tool wear by 53% compared with the worst experimental condition. The optimized BPNN model achieved a prediction accuracy of 96.68%, whereas the ANFIS model with Gaussian membership functions achieved 100%, demonstrating superior predictive performance. The proposed approach effectively combines robust process design and intelligent prediction models to accurately predict tool wear using a limited experimental dataset, providing an efficient methodology for tool wear prediction and machining parameter optimization in intelligent manufacturing applications. Full article

This study investigates the critical relationship between machining-induced surface integrity and the effectiveness of subsequent anti-corrosion protection for high-strength Al 7136-T76511 aerospace alloy. Given the alloy’s susceptibility to exfoliation corrosion, ensuring high-quality surface substrates for protective coatings is paramount. The research aims to model the influence of end milling parameters—cutting speed, depth of cut, and feed per tooth— on surface roughness to establish a topographical risk prognosis framework for subsequent coating vulnerability. A comprehensive full-factorial experimental design involving 150 distinct cutting regimes was evaluated on a CNC machining center. Statistical analysis using ANOVA showed that cutting speed is the most significant factor, contributing 83.89% to the variance of longitudinal Ra. A critical resonance zone was identified between 570 and 610 m/min, where the model predicts high instability and surface integrity degradation. The developed mathematical models achieved high precision, with coefficients of determination (R²) ranging between 85% and 88%. The research identifies a critical “danger zone” of dynamic instability between 570 and 610 m/min, where resonance significantly increases data dispersion (standard deviation = 0.112 µm compared to 0.051 µm in stable regimes). Findings demonstrate that even when average R_a values remain within industrial limits, vibration-induced micro-cracks, and severe chatter marks function as geometric precursors that theoretically lower the structural barrier efficiency of subsequent protective films. This study establishes that prioritizing process stability over nominal roughness minimization is essential for the structural integrity of critical aerospace components. Full article

Smart manufacturing requires real-time monitoring of multidimensional forces at the interface between the tool and workpiece in computer numerical control (CNC) machining. In this study, an innovative iKIT wireless extrasensory toolholder is introduced that is capable of high-fidelity, in situ, high-frequency sensing and monitoring of the cutting force, torque, and two-way bending moments. The hardware design of the system is outlined, highlighting a high-bandwidth miniature wireless transmission method and noncontact power supply and energy storage solution suitable for rotating machining environments. To assess the system performance, comprehensive milling tests were performed on aluminum alloy materials, and the relationship between the process parameters and changes in multidimensional mechanical loads was thoroughly examined. The experimental findings demonstrate that the smart toolholder detects precisely how parameter variations affect the loads. Multidimensional mechanical signals (torque and two-way bending moments) show a strong positive correlation with the feed rate and axial depth of cut, confirming the impact of the material removal rate on the system loads. Conversely, these signals are negatively correlated with spindle speed, accurately reflecting the effects of thermal softening and a reduced friction coefficient in aluminum alloys during high-speed cutting. This study not only offers a dependable hardware framework for integrating miniaturized sensors into toolholders, but also delivers accurate data to support digital twin models and adaptive control in machining processes. Full article

Real-time, non-contact velocity measurement of hot-rolled bars is critical for metallurgical process control, but conventional laser Doppler velocimetry (LDV) systems often fail in these environments. The intense broadband thermal radiation from targets up to 1000 °C, coupled with severe surface depolarization, overwhelms weak scattered signals in high-speed (up to 40 m/s) rolling zones. To address this issue, we developed a fully integrated, thermal-radiation-resistant LDV sensing system. Hardware optimization was achieved by eliminating polarized-light transmission and adopting a parallel-beam design, which significantly enlarges the laser overlap area and increases detection depth. Furthermore, a 1550 nm laser (100 mW) was coaxially combined with a 10 nm narrow-band filter to isolate the thermal background and boost signal strength. A customized workflow utilizing continuous Fourier transform (CFT) spectral refinement and energy centroid estimation was implemented to precisely extract the true Doppler shift. Performance evaluations show the system achieves an excellent signal-to-noise ratio (SNR) of 29,532. Allan variance analysis confirms a stable detection sensitivity of 0.003 m/s (0.1 s integration time), a local short-to-medium-term optimal limit of 1.6 × 10⁻⁴ m/s, and a statistical accuracy of 0.005 m/s. Finally, the system was successfully deployed on an industrial rolling mill production line. It provided reliable velocity feedback for mill speed adjustment, achieving a near-zero-tension rolling process and fundamentally resolving workpiece dragging, squeezing, and steel pile-up. Full article

This study presents a simulation-assisted engineering framework intended to support comparative machining parameter selection for quartz sintered materials. The approach integrates CAD/CAM-based analysis, an illustrative Design of Experiments (DOE) framework, and preliminary experimental validation to improve process planning and machining quality. The analysis focuses on key technological parameters, including cutting speed (vc), feed rate (f), and depth of cut (ap), evaluated across cutting, milling, and finishing stages. The results indicate that feed rate is the dominant parameter influencing process stability, surface quality, and edge integrity. A practical transition region of approximately 1200 mm/min was identified, above which increased vibration, defect formation, and surface degradation occur. The complementary DOE analysis confirms the relative importance of process parameters and reveals interaction effects, particularly between feed rate and depth of cut, which significantly influence defect formation under high-load conditions. Preliminary industrial observations provide trend-oriented support for the simulation-predicted process behavior. Based on the integrated analysis, a preliminary technological operating region was identified (vc = 1080–1320 m/min, f = 800–1200 mm/min, ap = 0.5–1.0 mm), suggesting a practical compromise between machining efficiency and surface integrity. The proposed methodology provides preliminary engineering support for comparative process planning and defect-reduction-oriented parameter selection in the machining of brittle materials. The novelty of this work lies in the integration of CAD/CAM simulation, DOE-based interaction analysis, and experimental validation for supporting the identification of a practical technological operating region for machining brittle materials. The presented results should therefore be interpreted as engineering-oriented comparative process-planning guidelines rather than statistically generalized machining laws. The presented study should be interpreted as an exploratory simulation-assisted engineering investigation intended to support comparative process planning rather than as a fully experimentally validated machining model. Full article

Photovoltaic modules require efficient sunlight modulation, including enhanced visible transmittance and conversion of unused ultraviolet light. This study develops a synthetic optical coating that achieves both functions by integrating down-conversion BAM (BaMgAl₁₀O₁₇:Eu²⁺, Mn²⁺) nanophosphors into a silica anti-reflection sol. The key novelty lies in a synergistic surface engineering strategy that decouples dispersion stabilization from luminescence protection. Five dispersants are systematically compared under combined ball and sand milling. The polyester-modified acrylic long-chain dispersant (DK062) yields a stable nanodispersion with an average particle size of 228 nm and a Zeta potential of −7.61 mV, effectively suppressing re-agglomeration while retaining high photoluminescence. Subsequent surface modification with KH570 grafts a dense silane passivation layer via Si–O–M covalent bonds, further increasing the photoluminescence intensity by 1.39-fold. The optimized nanophosphors are incorporated into a commercial anti-reflection sol and dip-coated onto photovoltaic glass. At a doping concentration of 2‰ and a withdrawal speed of 8 mm/s, the resulting DCSAR coating exhibits an average transmittance of 91.16%—slightly higher than that of the pure anti-reflection coating (90.96%)—while showing strong green emission at 515 nm. Industrial on-site testing further demonstrates an average transmittance of 94.20%–94.31% with uniform green emission. This work provides a scalable route to fabricate highly transparent, light-converting anti-reflection coatings by combining dispersant-assisted milling and silane passivation. Full article

Background/Objectives: The objective of this study was to design and optimize a continuous wet granulation process for Amiodarone hydrochloride tablets using a Design of Experiments approach. The study compared and evaluated the characteristics of granules and tablets produced via a high-shear mixer (batch process) and a twin-screw granulator (continuous process). Methods: For process optimization, a central composite design was applied to establish a design space, defining screw speed and milling size as critical process parameters (X) and dissolution rate, flowability, assay, disintegration time, and friability as dependent variables (Y). Results: Comparative results between the two processes revealed no significant differences in in-process control parameters, and all formulations successfully met the target dissolution profiles. Notably, the similarity factor (f₂) was calculated to be above 50, through which dissolution equivalence was successfully demonstrated with a high level of statistical certainty. Regarding process efficiency, lead time measurements confirmed that the continuous process dramatically reduced manufacturing time by more than 80% compared to the batch process. Conclusions: This study validates the feasibility of converting batch-based drug manufacturing to a continuous platform without altering the formulation, presenting an effective process strategy for enhancing productivity and operational efficiency in the pharmaceutical industry. Full article

High-quality microgrooves obtained in micro-milling titanium alloy Ti6Al4V are still challenging work due to the dependence of burr formation and surface roughness on cutting parameters. In this paper, the systematic analysis of the micro-milling process was conducted to obtain high-quality titanium alloy Ti6Al4V microgrooves, which is based on single-factor experiments, orthogonal experiments, intuitive analysis, range analysis, regression analysis, and multi-objective optimization. The range of factors and factors of orthogonal experiments were determined by single-factor experiments. Orthogonal experiments were conducted with a three-factor three-level design, which regards the total top-burr width and the bottom surface roughness of microgrooves as the response variables, and factors are spindle speed, feed per tooth, and the axial depth of cut. The optimal cutting parameters, which minimize the surface roughness and burr formation, and the main influence factor were determined by intuitive analysis, range analysis, regression analysis, and NSGA-II multi-objective optimization. Simultaneously, high-quality complex microgrooves were achieved with the optimal cutting parameters. The method of systematic experimental design and data analysis in this paper can provide the theoretical guideline and technical support for the processing development of complex parts. Full article