Search Results (2,177)

Ultra-precision machining of ferrous alloys remains challenging because conventional diamond tools suffer severe thermochemical wear, whereas ultrasonic vibration-assisted cutting requires complex and costly equipment. This study investigates single-crystal sapphire as an alternative cutting-tool material for ultra-precision machining of both non-ferrous and ferrous metals. A sapphire tool was fabricated from a polished wafer, laser-shaped into an equilateral triangular insert, vacuum-brazed onto a tungsten carbide carrier, and finished by ultra-fine grinding to yield a well-defined cutting edge. Ultra-precision turning experiments were conducted on copper and 420 stainless steel using a Moore Nanotech 350FG lathe, and the performance of the sapphire tool was benchmarked against conventional diamond (copper) and cubic boron nitride (CBN) tools (stainless steel) under comparable cutting conditions. Surface roughness (Ra) and topography were characterized using an optical surface profiler, while scanning electron microscopy and atomic force microscopy were employed to assess tool wear and cutting-edge geometry. The sapphire tool produced mirror-like surfaces with average surface roughness (Ra) values of 6.4 nm on copper and 39.1 nm on 420 stainless steel, compared with 1.3 nm for diamond on copper and 92.9 nm for CBN on stainless steel. Across both materials, sapphire generated regular, stable tool marks and exhibited minimal wear, with no catastrophic edge degradation or clear evidence of severe chemical interaction with the steel workpiece. These results demonstrate that sapphire is a viable tool material for extending diamond turning-level surface quality to stainless steel without ultrasonic assistance. Full article

The application of digital twin (DT) technology to intelligent machining shows promise, but its effectiveness in predicting vibration and assessing surface quality has not been thoroughly validated for widespread industrial use. This study presents a physics-informed predictive digital twin framework operating in an offline or near-real-time predictive configuration for vibration prediction and surface roughness modeling in milling processes. Impact hammer testing was conducted to extract the dominant modal properties of the spindle–tool assembly, which were embedded into a Simulink-based dynamic framework to predict tool vibration under varying cutting conditions. Full-immersion slot milling experiments on AL6061 were performed for validation. Within all datasets, including training phase and validation phase, the predicted vibration amplitudes exhibit a coefficient of determination $R^2=0.94$ with measured values. The overall MAPE and RMSE are about 10.39% and 0.234, respectively. Power-law regression-based surface roughness prediction models were subsequently established using cutting parameters and both measured and DT-predicted vibration features through logarithmic transformation and least-squares fitting. The results show that the roughness prediction model using vibration features predicted by the digital twin model achieved a correlation coefficient of approximately $R^2=0.84$, with MAPE = 9.57% and RMSE = 0.16 μm, which is comparable to the predictive model based on experimentally measured vibration. These results indicate that, within the investigated machining conditions, the digital twin can provide vibration features suitable for surface roughness prediction, demonstrating its potential as a virtual sensing approach. This work advances digital twin applications from process monitoring toward predictive, quality-oriented machining systems and provides a foundation for adaptive parameter updating in intelligent manufacturing environments. Full article

Ultra-low-expansion (ULE) glass serves as a critical material in high-precision optical devices and semiconductor manufacturing; however, its inherent hardness and brittleness pose significant challenges for machining processes. During the diamond cutting of ULE glass, severe tool wear emerges as the primary factor limiting machined quality, which not only shortens tool life but also prolongs subsequent polishing time, thereby increasing processing costs and hindering sustainable manufacturing. To address this challenge, in situ laser assisted diamond cutting (LADC) has emerged as a promising technique for the sustainable machining of difficult-to-machine materials. In this study, for achieving sustainable machining of ULE glass, the effects of cutting speed on surface roughness and tool wear were systematically investigated. To determine the optimal parameter combination for minimizing surface roughness and tool wear simultaneously, an integrated optimization approach combining artificial neural network (ANN) and non-dominated sorting genetic algorithm II (NSGA-II) was employed. The experimental results indicated that a spindle speed of 2900 rpm and a feed speed of 1.1 mm/min was ascertained as the optimum combination to attain the desired outcomes for in situ LADC of ULE glass. Under the optimum machining parameters, in situ LADC resulted in a 70.08% reduction in surface roughness and 61.24% reduction in tool wear compared to conventional diamond cutting (CDC). This study demonstrates that in situ LADC can be recognized as a promising sustainable machining technique for machining of ULE glass. Full article

Since the first successful synthesis of borophene in 2015, this atomically thin boron allotrope has attracted extensive attention due to its polymorphic structures, metallic conductivity, and outstanding mechanical flexibility. As a new member of the two-dimensional (2D) materials family, borophene exhibits a unique triangular lattice with tunable hexagonal vacancies, leading to rich structural diversity and anisotropic physical properties. Recent breakthroughs in synthesis—particularly molecular beam epitaxy (MBE), chemical vapor deposition (CVD), and solvothermal-assisted liquid-phase exfoliation (S-LPE)—have significantly expanded the accessible structural phases and improved control over film quality and stability. Meanwhile, borophene’s distinctive combination of structural and electronic characteristics has enabled its rapid development in both energy and biomedical applications. In energy storage, borophene serves as a promising anode material for lithium/sodium-ion batteries and a lightweight medium for hydrogen storage and supercapacitors, owing to its metallic conductivity, high surface charge density, and large adsorption capacity. In biomedicine, borophene-based nanoplatforms exhibit excellent photothermal conversion efficiency, enabling multifunctional roles in cancer diagnosis and therapy. Despite these advances, several challenges—such as environmental instability, oxidation susceptibility, and limited scalable synthesis—continue to restrict practical implementation. Future progress will depend on chemical functionalization, surface passivation, and machine-learning-assisted materials design to achieve oxidation-resistant, large-area, and biocompatible borophene derivatives. This review summarizes recent advances in borophene synthesis, structural engineering, and multifunctional applications, while outlining key scientific challenges and future opportunities for the realization of borophene-based materials in next-generation energy and biomedical systems. Full article

Picosecond laser drilling is characterized by a minimal heat-affected zone (HAZ) and superior surface quality, making it widely utilized for fabricating film-cooling holes in aeroengine turbine blades. However, maintaining consistent drilling quality remains a significant challenge. This study conducts picosecond laser trepanning drilling experiments on a GH4169 nickel-based superalloy to investigate the quality of inclined holes. Due to its excellent high-temperature resistance, creep resistance, and corrosion resistance, GH4169 is a primary material for turbine blades. A control variable method was employed to evaluate the effects of power ratio (60–95%), number of scanning passes (5–40), and defocus amount (−0.2 mm to 0.2 mm) on the quality of inclined holes with tilt angles of 7° and 15° and a sample thickness of 0.5 mm. Entrance diameter, exit diameter, and taper angle were utilized as the key quality indicators. The results indicate that due to the distribution of laser energy flux, both the geometric dimensions and taper angles of 15° inclined holes are significantly larger than those of 7° holes. As the power ratio increases, the entrance and exit diameters exhibit non-linear expansion; a “topographic stability window” is achieved at a 75% power ratio due to the equilibrium in energy coupling. An increase in the number of scanning passes leads to larger diameters; however, excessive scanning slows down the expansion of the exit diameter due to multiple reflection losses within the hole and the accumulation of slag, thereby intensifying taper evolution. The defocus amount exerts a bidirectional regulatory effect: positive defocusing increases the entrance diameter while decreasing the exit diameter, whereas negative defocusing facilitates the expansion of the exit. Optimal hole wall quality is observed at zero defocusing. This work provides data support for parameter optimization and the selection of inclination angles in subsequent laser machining of inclined holes. Full article

Reliable potato quality monitoring during postharvest handling requires compact sensing systems that can acquire chemically relevant information while operating on irregular tuber surfaces. In this study, a Flexible Spectral Sensing Gripper (FSSG) was developed by integrating a low-cost 12-channel visible/near-infrared (Vis/NIR) spectral sensor array, electronic components, and an ESP32-S microcontroller onto a flexible printed circuit (FPC) substrate encapsulated with PDMS. By embedding the sensing units into the grasping interface, the FSSG enables conformal, multi-point spectral acquisition during potato handling, reducing optical-coupling uncertainty associated with unstable contact. Spectral reflectance data were collected from potato tubers, and dry matter content (DMC) and starch content (SC) were determined by standard chemical analysis as reference values. Multiple linear regression (MLR) and partial least squares regression (PLSR) models were compared under Norm, SNV, MSC, SNV-Norm, and MSC-Norm preprocessing conditions, and support vector machine (SVM) classification was used to distinguish healthy and artificially induced deteriorated samples. Normalization combined with MLR provided the best performance among the evaluated regression approaches, achieving cross-validation coefficients of determination ($R_{CV}^2$) of 0.847 and 0.817 and RPD values of 2.557 and 2.345 for DMC and SC, respectively. The SVM model achieved 98.67% accuracy for healthy versus artificially induced deteriorated potato samples. Overall, the FSSG demonstrates the value of combining gripper-integrated spectral sensing with interpretable chemometric modeling for potato quality screening. The FSSG enables real-time non-destructive quality prediction and disease-detected classification of potatoes, improves sorting accuracy and production efficiency, and provides general sensing solutions for controlled-environment agriculture, cold-chain logistics, and value-added processing of agricultural products. Full article

Machine vision plays a pivotal role in precision engineering for high-precision measurement that relies on high-resolution images. The highly reflective nature of metal surfaces and the need for high-quality images pose significant challenges in image processing. Although existing research has made significant progress in enhancing the resolution of natural images, super-resolution methods specifically tailored for multi-view metal images remain unexplored areas. To fill this gap, this paper focuses on developing a deep learning-based super-resolution algorithm, focusing on detail recovery on under multi-view metal images. The proposed super-resolution model utilizes a hybrid-resolution input that combines light field super-resolution at the image level and reference-based super-resolution at the feature level, demonstrating the effectiveness for achieving a large-scale multi-view metal image super-resolution. An experiment using a public metal object image dataset is conducted, and a comparison has been carried out with Bicubic, LFhybridSR and ERVSR. The proposed method demonstrates superior SSIM and achieves average PSNR improvements of 4.45 dB and 1.18 dB on synthetic data and real-world data. The results demonstrate that the method can improve the resolution and detail representation of metal images in terms of PSNR/SSIM and address the problem of super-resolution in multi-view metal images. Furthermore, applying the proposed SR method as preprocessing reduces the absolute relative error in depth estimation from approximately 0.5 to 0.1. Full article

Additive manufacturing by Fused Filament Fabrication (FFF) enables the fabrication of complex polymer components, although limitations in surface quality and dimensional accuracy often require post-processing. Abrasive water jet machining (AWJM) is a non-thermal technique suitable for improving surface integrity in polymers and composites without inducing thermal damage. This study investigates the AWJM performance on FFF-printed polylactic acid (PLA) and carbon-fiber-reinforced PLA (PLA–CF), focusing on the influence of water pressure (WP), traverse feed rate (TFR), and abrasive mass flow rate (AMFR). A full factorial design was implemented, and surface integrity was evaluated through surface roughness (Ra) and kerf taper (T), considering their variation across characteristic cutting regions: initial damage region (IDR), smooth cutting region (SCR), and rough cutting region (RCR). Results show that WP and TFR are the dominant parameters, while AMFR has a limited effect within the studied range. The SCR exhibits the lowest roughness, whereas the RCR shows significant degradation due to energy loss. Both materials present similar behavior, with only minor improvements in PLA–CF. ANOVA confirms that process parameters have a stronger influence than material type, providing useful criteria for AWJM optimization in FFF polymers. Full article

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are prone to surface defects during grinding owing to the heterogeneous deformation of the aluminum matrix and SiC particles, rendering conventional two-dimensional roughness parameters inadequate for precise surface characterization. In this study, three-dimensional surface roughness parameters were adopted to assess the ground surface quality of SiCp/Al composites. Orthogonal grinding experiments were carried out with four key process parameters (grinding wheel grit size, spindle speed, feed speed, and grinding depth), and the quantitative relationships between processing parameters and 3D roughness parameters, including arithmetical mean height (Sa), root mean square height (Sq), skewness (Ssk), kurtosis (Sku), surface bearing index (Sbi), core fluid retention index (Sci), and valley fluid retention index (Svi), were analyzed. The results reveal that the machined surface presents typical features including grooves from abrasive–matrix interaction, pits induced by SiC particle pull-out, scratches caused by dragged SiC particles, and tailing phenomena due to aluminum matrix melting under grinding heat. Grinding parameters exert distinct effects on surface topography: grinding wheel grit size shows the most significant influence on the Sa index, with its weight decreasing from 34% to 13% as grit becomes finer, while the combined influence weight of spindle speed, feed speed and grinding depth increases from 22% to 29%. Based on the comprehensive 3D roughness evaluation index, the optimal grinding parameter combination is determined as 320# grinding wheel, 4000 r/min spindle speed, 20 mm/min feed speed and 20 μm grinding depth. Additionally, the PSO-BP neural network achieves higher accuracy and better stability in predicting Sa and Sci than the conventional BP neural network. Full article

Background/Objectives: Dental metallic implants cause severe streaking artifacts in kilovoltage CT (kVCT), compromising dose calculation in radiotherapy (RT) treatment planning. The purpose of this study is to assess the dosimetric agreement of synthetic MVCT (sMVCT) images generated from artifact-affected kVCT using a deep learning network with respect to true MVCT (tMVCT) acquired at the treatment machine. Methods: Nineteen head and neck cancer patients with dental metallic implants treated with RT were included. Planning kVCT images were converted to sMVCT using Metal Artifact Reduction through Domain Transformation Network (MAR-DTN), a UNet-inspired deep learning network. The sMVCT images were rigidly registered to true MVCT (tMVCT) acquired on the Hi-Art II Tomotherapy system. Mean Hounsfield Unit (HU) values were compared across seven structures (thyroid, bilateral parotids, brainstem, spinal cord, GTV, PTV70) using pairwise Wilcoxon tests and Two One-Sided Tests (TOST) for statistical equivalence within a pre-specified margin of ±20 HU (corresponding to a 2% deviation in physical density). Dose distributions were recalculated on sMVCT using the AAA algorithm and compared to reference tMVCT-based plans via dose–volume histogram (DVH) metrics, evaluated for equivalence by TOST within a margin of ±2% of the prescribed dose (±142 cGy of 70.95 Gy), and via 3D gamma index, evaluated by one-sided non-inferiority test against the clinically accepted thresholds of 90% (2 mm/2%) and 95% (3 mm/3%). A pre-specified sensitivity analysis was performed by repeating all comparisons on the strictly independent sub-cohort (n = 16) excluding three patients drawn from the MAR-DTN training set. Results: All seven anatomical structures showed statistical equivalence between sMVCT and tMVCT under the ±20 HU margin (TOST p < 0.05; mean HU differences in the range −1.1 to +8.4 HU; all Wilcoxon p > 0.05). All nine DVH metrics achieved formal dosimetric equivalence within ±2% of the prescribed dose (TOST p < 0.05). Mean 3D gamma pass rates were 94.3% (95% CI: 89.3–97.1) for the 2 mm/2% criterion and 97.6% (95% CI: 94.8–99.0) for the 3 mm/3% criterion, both formally non-inferior to the respective clinical thresholds (p < 0.0001). Residual gamma failures were concentrated at the patient surface, consistent with inter-session repositioning uncertainty rather than errors in synthetic image generation. Sensitivity analysis on the n = 16 sub-cohort confirmed all conclusions, with mean HU and DVH differences smaller than in the full cohort for the structures showing the largest mean differences, and comparable for the remaining structures, with all TOST equivalence and gamma non-inferiority tests confirmed in both cohorts. Conclusions: sMVCT images generated via MAR-DTN show dosimetric agreement with physically acquired tMVCT in head and neck patients with dental implants, formally demonstrated by TOST equivalence within ±2% of prescribed dose for all DVH metrics. The combined HU and gamma index framework presented here represents a promising quality assurance approach for AI-based synthetic imaging tools in radiotherapy, pending validation in larger prospective multicentre cohorts. Full article

Historically, pharmaceutical formulation development relied heavily on trial-and-error experimentation, which was useful for empirical progress but often provided limited mechanistic understanding and insufficient efficiency for increasingly complex drug products. The introduction of Design of Experiments (DoE) and Quality by Design (QbD) established a more systematic framework for studying formulation variables, manufacturing parameters, and Critical Quality Attributes (CQAs). Approaches such as factorial designs, response-surface methodology, and mixture designs have therefore become central to modern pharmaceutical development because they improve experimental efficiency and support the definition of design space. However, as formulations become more nonlinear, high-dimensional, and multi-objective, these classical approaches may no longer be sufficient on their own. This review examines the evolution of experimental design in pharmaceutical research, from one-factor-at-a-time experimentation to structured DoE/QbD strategies, and then to emerging intelligent optimization methods. Its central objective is to clarify when conventional DoE/QbD remains appropriate and when it should be complemented by machine learning, Bayesian optimization, digital twins, and closed-loop experimental systems. The review first summarizes the foundations and strengths of classical experimental design; then, it discusses its practical limitations in complex formulation settings, and finally evaluates how data-driven and hybrid approaches can extend pharmaceutical development. Evidence from tablets, capsules, nanocarriers, transdermal patches, and biotherapeutic systems suggests that intelligent optimization can improve predictive performance and experimental efficiency when used alongside, rather than instead of, established pharmaceutical development principles. Full article

Titanium alloy is difficult to cut, with tools prone to adhesion and diffusion wear that reduces life and surface quality. Traditional coatings fail to meet precision machining demands. Based on TiAlN, Ce-doped coatings were prepared via magnetron sputtering at varying powers to investigate mechanical and tribological properties. The results show that with the increase in Ce doping amount, the hardness, elastic modulus, H/E, and H³/E² ratios of the coating increase first and then decrease, and the friction coefficient decreases first and then increases. The performance is optimal at 50 W, the friction coefficient is 0.676, and the film-based adhesion is 113.8 N. Compared with the TiAlN coating, the hardness increased by 12%, the wear loss decreased by 24%, and the H/E and H³/E² increased by 31% and 95%, respectively. The mechanism analysis shows that the appropriate amount of Ce doping can improve the toughness of the coating by grain refinement and solid solution strengthening and significantly inhibit adhesive wear and oxidative wear. Ce-modified tools were further prepared for titanium alloy turning experiments. Compared with uncoated and traditional TiAlN-coated tools, Ce doping can effectively reduce tool wear and improve the surface quality of the workpiece and has significant advantages under high-speed and large cutting depth conditions. This study systematically reveals the adaptive lubrication mechanism of Ce-doped TiAlN coating in the cutting process of titanium alloy and provides theoretical support and engineering guidance for the preparation of special tool coatings for difficult-to-machine materials. Full article

The automated inspection of aluminum profile surface defects, which heavily relies on data acquired by machine vision sensors, is a critical task in industrial quality control. Addressing the current challenges of intense background texture interference and the difficulty in detecting defects with extreme aspect ratios on aluminum profiles, this research puts forward a complete end-to-end defect detection algorithm named WMC-DFINE (WIFA-MKSS-CSFF-DFINE) based on the DFINE framework. First, a Wavelet-Integrated Frequency Attention (WIFA) module is introduced, which utilizes a discrete wavelet transform to decouple features into the frequency domain, thereby dynamically suppressing high-frequency background noise and enhancing defect edge responses. Second, a Cross-Scale Feature Fusion (CSFF) module based on dual-channel pooling is designed to ensure the continuity of defect features, thereby resolving the semantic misalignment issue in traditional fusion. Third, a Multi-Kernel Strip Shuffle (MKSS) module is incorporated, utilizing decomposed convolution kernels to capture the geometric features of slender scratches. Finally, a knowledge distillation strategy is employed to transfer structured knowledge from a complex teacher model to a lightweight student model. Experiments on the Tianchi aluminum defect dataset demonstrate that WMC-DFINE achieves a mAP of 82.1%, which surpasses algorithms including YOLOv12, RT-DETR, and the baseline model DFINE. Furthermore, the distilled student model, WMC-DFINE-distill, improves the mAP by 3.2% compared to DFINE, reduces parameter count by 47%, and achieves an inference speed of 59.75 FPS on the experimental equipment. The proposed method effectively resolves the problem of balancing background suppression and defect detail feature preservation, offering a practical and efficient scheme for real-time industrial defect inspection. Full article

Blasting-induced ground vibrations, typically quantified by peak particle velocity (PPV), pose one of the most critical environmental challenges in surface mining and can damage nearby structures and disrupt surrounding ecosystems. Consequently, the development of reliable and accurate predictive models is essential for designing safe, environmentally responsible, and sustainable blasting operations. This study develops a robust predictive framework using a harmonized database of 506 blasting events, from which 386 high-quality records were retained after preprocessing to model PPV as a function of charge per delay (Q), monitoring distance (R), and rock mass rating (RMR). Several machine learning (ML) algorithms, including artificial neural networks trained using the Levenberg–Marquardt algorithm (ANN-LM), adaptive neuro-fuzzy inference systems (ANFIS), Gaussian process regression (GPR), and decision trees (DT), were evaluated alongside conventional empirical models such as the USBM, Ambraseys–Hendron, Langefors–Kihlstrom, and BIS. To further enhance predictive capability, two optimization strategies, Bayesian optimization (BO) and differential evolution (DE), were applied to the GPR model, producing optimized BO-GPR and DE-GPR variants. Model performance was assessed using the correlation coefficient (r), variance accounted for (VAF), mean absolute error (MAE), and relative root mean square error (RRMSE). Results indicate that the BO-GPR model achieved the best predictive performance during testing for both the two-input (Q, R) and three-input (Q, R, RMR) configurations, with r values of 0.97426 and 0.98381, respectively, and VAF values exceeding 94%. SHAP analysis revealed monitoring distance as the dominant attenuating factor controlling PPV. The optimized framework provides an accurate, interpretable tool for vibration prediction and precision blast design, supporting environmentally responsible, sustainable mining operations. Full article

Surimi manufacturing involves complex, multi-step operations in which small changes in raw material condition, formulation, and heating history can markedly alter texture, water retention, and visual quality. This review critically examines peer-reviewed studies that apply artificial intelligence to surimi and surimi-based products, focusing on work validated directly in surimi systems. Current evidence mainly supports non-destructive quality evaluation and integrity screening using imaging and vibrational spectroscopy. These applications include deep learning for classifying gel surface images, as well as chemometric and machine learning analysis of infrared, near-infrared, and hyperspectral data for quality prediction and adulteration detection. Process-linked monitoring during thermal treatment is also beginning to emerge, with one time-resolved hyperspectral imaging study demonstrating quality tracking during heating. Major barriers to industrial adoption include limited and narrowly sampled datasets, batch effects and validation designs that may overestimate predictive performance, and practical deployment challenges such as stable sensing in wet environments, instrument drift, and calibration transfer across devices and sites. The review also outlines forward-looking directions, including digital twins, adaptive control strategies, and automation, and identifies data standardization, external validation, and maintenance strategies as priorities for translating laboratory demonstrations into reliable industrial applications. Full article