Search Results (1,549)

SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables. This procedure allowed for the identification of the modeling approaches that provide the most robust performance across varying operational regimes. The results show that XGB achieved the best predictive performance, with test RMSE and R² values of 87.98 and 97.35% for SAG Production, and 431.11 and 95.11% for SAG Power, respectively. Stochastic approaches provided complementary uncertainty quantification, supporting risk-informed decision making under variable operating conditions. The analysis by operational regime indicates that XGB presents better fit in the Thick hydraulic regime, for both responses’ variables, which could be explained why a dense pulp operation provides more predictable grinding dynamics. The comparative analysis reveals trade-offs between model complexity, interpretability, computational requirements, and predictive performance, offering practical guidance for selecting appropriate modeling frameworks based on specific operational objectives and data availability in mineral processing applications. Full article

The paper explores the use of artificial neural networks for surface roughness parameter Ra prediction when milling the finishing of flat surfaces with toroidal milling on C45 steel. The experiments were conducted on a 5-axis CNC center, varying three main parameters: cutting speed, feed per tooth, and tool axis tilt angle. In total, 70 surfaces were processed, with multiple measurements of Ra roughness. The data were preprocessed in MATLAB (noise reduction by Z-score and augmentation to 630 values) and used to train an artificial feedforward neural network with Bayesian regularization. The resulting model showed good performance on the dataset and was experimentally validated on three new parameter combinations, processed and measured independently with a 3D scanner. The results confirm the network’s ability to estimate Ra roughness based on varying process parameters. The paper proposes the model as a useful tool for assessing surface quality in finishing milling and recommends extending the experimental base as the main direction of continuation. Full article

Classifying which operation a Computer Numerical Control (CNC) machine is executing, not just detecting whether it is functioning correctly, is a monitoring challenge that existing sensor-based studies rarely address. Unlike tool wear estimation, operation-type classification must resolve toolpath strategies and cutting conditions within heterogeneous, noisy sensor streams in which modalities differ widely in their discriminative value. Which sensors are genuinely necessary, and how many can be removed before performance degrades, directly informs retrofit cost and monitoring system design. We present a systematic cross-validated ablation study for a nine-class CNC toolpath and condition classification task, using 120 operation files collected from a desktop CNC mill instrumented with six distributed sensor units spanning inertial, acoustic, environmental, and electrical modalities. To handle multimodal fusion under sensor noise, we introduce the Multimodal Denoising Temporal Attention Encoder with Long Short-Term Memory (MM-DTAE-LSTM), which combines learned modality weighting, cross-modal attention, and a self-supervised denoising objective, followed by recurrent temporal modeling for classification. We evaluate MM-DTAE-LSTM against five baseline model families across five cumulative sensor-ablation levels and ten temporal resolutions, using file-level cross-validation to prevent data leakage from overlapping windows. MM-DTAE-LSTM maintains 92.5% classification accuracy when nearly half the sensor channels are removed (56 of 110 features), whereas simpler baselines degrade by up to 10.7 percentage points under the same reduction. Analysis of variance reveals that pressure channels encode session-level atmospheric variation rather than machining dynamics, exposing how models that cannot suppress uninformative modalities rely on environmental confounds rather than machining physics. Together, these findings translate into concrete sensor-selection and deployment recommendations for cost-effective CNC process monitoring at under USD 500 in hardware, though generalization to industrial machines, diverse materials, and production environments requires further validation. Full article

Optimizing nitrogen (N) fertilizer application within conventional rice production systems remains essential for improving grain quality while avoiding inefficient resource use. This study examined how different N application levels influence rice quality, starch structure, and physicochemical properties in two japonica rice types cultivated under cold-region conditions in Northeast China. Using two cultivars, common japonica rice ‘Putian 1498’ and glutinous japonica rice ‘Longjing 57’, four nitrogen levels were established under machine-transplanting conditions: N0 (0 kg/hm²), N1 (80 kg/hm²), N2 (135 kg/hm²), and N3 (190 kg/hm²). The results indicate that increasing nitrogen application differentially affected the milling quality of the two rice types: it reached its maximum at the N1 level for common japonica rice and at the N3 level for glutinous japonica rice. However, the taste value decreased and chalkiness increased in both types. Regarding starch properties, increased nitrogen application led to rougher starch granule surfaces, a decrease in large granules, and an increase in medium and small granules. Starch content decreased, and the amylose-to-amylopectin ratio declined. Relative crystallinity increased, while the FTIR ratio of 1045/1022 cm⁻¹ decreased. Solubility showed an increasing trend, whereas swelling power exhibited the opposite trend. The gelatinization enthalpy and pasting temperatures were positively correlated with nitrogen rate, whereas retrogradation degree showed a negative correlation. These results demonstrate that nitrogen application regulates rice quality through changes in starch structure and physicochemical properties, with distinct responses between common and glutinous japonica rice. Moderate nitrogen input improves milling quality, but excessive application reduces eating quality, indicating a trade-off between processing performance and consumer-oriented quality. This study provides mechanistic evidence to support more precise nitrogen management in conventional rice systems, contributing to improved resource-use efficiency without overstating broader sustainability claims. In conclusion, moderate nitrogen application optimizes rice quality by balancing milling performance and eating quality through its effects on starch structure, whereas excessive nitrogen input leads to quality deterioration and inefficient resource use. Full article

Machining Nomex honeycomb structures presents a major challenge due to their thin-walled architecture, orthotropic behavior, and sensitivity to adhesion and delamination. This study develops a three-dimensional numerical model using Abaqus/Explicit to analyze ultrasonic vibration-assisted milling in longitudinal and longitudinal-torsional modes. The model incorporates orthotropic behavior with progressive damage based on Tsai-Wu and experimental friction calibration to accurately reproduce tribological conditions. A parametric analysis examines the effect of vibration mode, amplitude (5–25 µm), frequency (21–22.5 kHz), cutting width, and tool geometry on stresses, bond wear, and material buildup. An optimal coefficient of friction ensures excellent simulation–experiment agreement. Compared to conventional milling, the longitudinal-torsional configuration reduces cutting forces by up to 50%, while frequency optimization allows for gains of 40 to 60%. Hybrid vibration coupling establishes intermittent contact and oscillatory micro-shearing, limiting adhesion and build-up. Thus, longitudinal-torsional assistance improves tribological stability, tool life and wall integrity, offering a validated digital strategy to optimize ultrasonic milling of composite honeycomb structures. Full article

Minimum quantity lubrication (MQL) is a promising green technology for high-speed milling of GH4169. However, the full-chain oil mist penetration mechanism remains unclear, limiting precise parameter regulation. Based on a cross-scale mechanism, this study develops a semi-empirical oil mist penetration efficiency model coupling four key parameters and conducts single-factor and orthogonal high-speed milling experiments to validate the model and analyze the regulation mechanism using milling force and surface roughness. The experimental results show relative deviations below 6%, demonstrating good model validity and robustness. The influence hierarchy is spindle speed > nozzle orientation > nozzle angle > nozzle distance. Spindle speed and nozzle orientation are strongly coupled dominant parameters with a “drive-adaptation” mechanism, while nozzle distance and nozzle angle are weakly coupled, only notable under extreme conditions. The optimal parameters obtained via BP neural network and NSGA-II are nozzle orientation −X, angle 22.43°, distance 14.96 mm, and spindle speed 16,581 rpm. Under this combination, minimum Surface Roughness Ra of 0.17 μm and milling force of 24.27 N are achieved, reducing surface roughness by 85.32% and milling force by 53.52% versus the worst condition and reducing roughness by 28.57% versus the baseline while maintaining milling force within a reasonable range. This study clarifies the physical mechanism of MQL oil mist penetration, extending conventional macroscopic parameter optimization. The proposed cross-scale framework offers theoretical and engineering guidance for MQL parameter design in green precision machining of nickel-based superalloys. Full article

Extra-virgin olive oil (EVOO) is a chemically complex lipid matrix whose minor constituents—especially phenolic secoiridoids—drive sensory quality, oxidative stability, and health benefits. However, these bioactives are vulnerable to heat, light, oxygen, and pro-oxidant metals during processing and distribution, while the high cost of EVOO often makes it a target for adulteration and mislabeling. This review critically assesses nano-enabled, food-grade strategies that (i) preserve phenolics and aroma compounds through nanoencapsulation, inclusion complexes, Pickering stabilization, and structured lipid systems; (ii) control their release and bioaccessibility during digestion; and (iii) enhance authenticity verification via sensor-ready packaging, spectroscopy/chemometrics, and digital traceability systems (IoT, machine learning, blockchain). We align these innovations with the “product identity constraints” of the EVOO category and with official quality standards used in routine control (IOC/EU). Finally, we explore circular valorization of olive-mill by-products within food-centered biorefineries, outlining pathways to convert biomass into ingredients, materials, and energy, thus reducing environmental impacts. Research priorities are proposed to develop scalable, regulation-compliant nanotechnologies that extend shelf life and increase consumer trust without compromising EVOO category standards. Full article

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations. Full article

Rotational turning is a hybrid machining process that combines features of milling and conventional turning, resulting in altered chip formation and force generation mechanisms. Despite its technological relevance, the predictive modeling of cutting forces and surface roughness in rotational turning has received little attention. This study applies and evaluates a hybrid regression and machine learning modeling for the multi-output prediction of three cutting force components and two surface roughness parameters during rotational turning of normalized C45 steel. The input variables are tool inclination angle, depth of cut, feed, and cutting speed. Three modeling approaches are compared: stepwise polynomial regression, Gaussian Process Regression, and Random Forest regression, using repeated five-fold cross-validation with ten repetitions. The results show that Gaussian Process Regression provides the highest predictive accuracy for most outputs, particularly for axial and radial forces and roughness parameters, while stepwise regression achieves comparable performance for tangential force with greater interpretability. Random Forest regression exhibits lower accuracy under the structured experimental design. The study demonstrates that combining interpretable regression with probabilistic machine learning enables the accurate prediction of process responses in rotational turning. The proposed methodology represents a novel, statistically validated approach for multi-output modeling of this machining process and supports future applications in process optimization and adaptive manufacturing systems. Full article

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. Unlike previous studies that focus primarily on force measurement, this work integrates experimental analysis with machine learning-based predictive modelling to improve process understanding and prediction accuracy. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) and electrical power consumption under varying spindle speeds (14,000, 16,000 and 18,000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected F_x and F_y components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. The Fine Tree algorithm demonstrated the best performance, achieving validation metrics of R² = 0.9 and RMSE = 0.60 (MSE = 0.36, MAE = 0.48), and improved testing performance with R² = 0.95 and RMSE = 0.44 (MSE = 0.20, MAE = 0.36). After model comparison using RMSE, R², training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained data on cutting force and electrical energy consumption are suitable for reliable predictive modelling in CNC milling of MDF boards. However, it is necessary to work with those components that have the greatest dependence on speed, feed, or type of milling, and these are the force components measured on the x and y axes. Full article

Rapid non-destructive evaluation of the moisture content of freshly harvested paddy rice in the field is essential for determining the optimal harvest timing, ensuring high-quality rice production and energy savings. This study developed a non-destructive prediction model for the moisture content of paddy rice using near-infrared (NIR) spectroscopy combined with machine learning and deep learning techniques. Rice samples were collected weekly during the ripening period after heading, and NIR reflectance spectra were acquired in the range of 950–2200 nm. Seven spectral preprocessing techniques were applied; and the prediction models developed, using partial least squares regression, support vector regression, deep neural network, and one-dimensional convolutional neural networks (1D-CNNs) based on VGGNet and EfficientNet architectures. Among these, the EfficientNet-based 1D-CNN combined with Savitzky–Golay 1st order derivative preprocessing showed the highest performance, achieving an ${\mathrm{R}}_{\mathrm{p}}^2$ of 0.999 and an RMSE_P of 0.001 (Friedman test, p < 0.001; Kendall’s W = 0.97), significantly outperforming previous traditional machine learning models. The results demonstrate that the proposed prediction model enables highly accurate estimation of moisture content in freshly harvested paddy rice without requiring drying or milling. The proposed approach can be implemented across various agricultural operations, enabling optimal harvest timing, quality control during storage, energy efficient drying, and real-time monitoring via on-combine sensor systems. Full article

Open-air asphalt milling generates hazardous respirable crystalline silica (RCS), posing severe risks to operators of legacy machines lacking enclosed cabs. This study evaluates a novel, standalone retrofit water spray system designed to intercept fugitive dust. Field validation across 11 road maintenance sites involved particle characterization and paired system-off/on exposure monitoring. Results indicated a Mass Median Aerodynamic Diameter (MMAD) of 6.12 µm, confirming the efficacy of fine-atomizing nozzles (0.3 mm) for capturing respirable fractions. The system achieved RCS suppression efficiencies ranging from 60% to over 85% under low-to-moderate wind conditions (<2.5 m/s). A comparative analysis revealed no significant performance gain from larger 0.5 mm nozzles, supporting the use of smaller orifices for optimal water conservation. However, suppression efficacy degraded significantly when crosswinds exceeded 2.5 m/s, indicating a potential operational boundary. This retrofit solution provides a scientifically validated, cost-effective engineering control for reducing occupational silica exposure in aging road maintenance fleets. Full article

The valorization of agro-industrial by-products is a sustainable approach to recovering high-value bioactive compounds. In this study, Opuntia ficus-indica (L.) Mill. seed press residues were investigated as a source of phenolic and flavonoid compounds using microwave-assisted extraction (MAE). A multi-step optimization strategy was implemented, combining preliminary single-factor experiments (OVAT), response surface methodology based on a Box–Behnken design (BBD), and machine learning modeling using K-nearest neighbors coupled with the dragonfly algorithm (KNN_DA), followed by desirability-based validation. The effects of ethanol concentration (50–100%), microwave power (400–800 W), extraction time (2–4 min), and liquid-to-solid ratio (30–50 mL/g) were evaluated on Folin–Ciocalteu reducing capacity (FCRC), AlCl₃ complexation response, and antioxidant activity assessed by DPPH radical scavenging and reducing power assays. Optimal conditions were identified at 50% ethanol, 800 W microwave power, 4 min extraction time, and a liquid-to-solid ratio of 47.28 mL/g. Under these conditions, FCRC reached 376.85 ± 0.23 mg GAE/100 g DW and 49.16 ± 0.33 mg QE/100 g DW for AlCl₃ complexation response, with prediction errors of 2.80% and 0.82%, respectively. The optimized extracts exhibited enhanced antioxidant activity. These findings confirm MAE as a rapid and environmentally friendly technique and highlight the predictive performance of the KNN_DA model for process optimization. Full article

To the authors’ knowledge, this is the first industrial machine learning (ML) study focused on wet vertical stirred media milling. The study develops and validates machine learning (ML) models to predict the key operating parameters, namely mill discharge product size, mill feed slurry flow rate, mill power draw, and the specific energy consumption of an industrial wet vertical stirred media mill operating at a copper plant. A physics-guided workflow was adapted, combining relief coefficient-based variable screening with fundamental stirred milling principles to define 20 different structured model input scenarios. In the scope, six regression approaches, linear regression (LR), fine tree regression (FTR), support vector regression (SVR), random forest regression (RFR), artificial neural network regression (ANN), and Gaussian process regression (GPR), were trained and validated using plant sensor data and evaluated using R² and RMSE. Overall performance was reasonable, with GPR providing the highest predictive accuracy, followed by RFR/ANN, while LR, SVR, and FTR performed lower. The potential benefit of feed size was also assessed conceptually through an upper-bound sensitivity analysis, representing a best-case scenario where an online feed size measurement would be available. Because the feed size descriptor (F₈₀) was not independently measured but derived from an energy–size relationship, the associated accuracy gains are reported as theoretical upper-bound indications rather than independent predictive capability. Overall, the findings support ML-based decision support in stirred milling operations and motivate future work using independently measured feed size (or reliable proxy sensing). Full article

Ultra-precision diamond milling is a crucial technology for machining precision components with complex-shaped surfaces and microstructure array surfaces. Machining process monitoring is a promising approach to improving machining quality. This paper proposes a spindle-integrated three-axis cutting force measurement method for ultra-precision diamond milling using force piezoelectric force sensors. A spindle-integrated force measurement mechanism utilizing four piezoelectric force sensors arranged symmetrically and diagonally for measuring three-axis cutting forces was designed. Calibration tests showed that the linearity of force detection in three directions was less than 2%. Tool-setting experiments based on force detection signals were conducted, demonstrating the capacity of precision tool-setting in the Z-direction with an accuracy of less than 100 nm. A Wiener filter was employed to eliminate measurement noise from vibration and inertial forces under spindle rotation. Ultra-precision milling experiments were carried out based on the designed spindle-integrated force measurement mechanism, and the measurement results demonstrated that the system could effectively detect cutting forces below 50 mN and exhibited good correlation with the measurement results of commercial standard dynamometers. This paper provides a promising and effective in-process force measurement technology for the ultra-precision milling process. Full article