Search Results (88)

Background/Objectives: Pharmaceutical preparation technologies can enhance the bioavailability of poorly water-soluble drugs. Ursolic acid (UA) has been found to possess anti-cancer and hepatoprotective properties, demonstrating its potential as a therapeutic agent; however, its hydrophobicity and low solubility present challenges in the development of drug formulations. This study investigates the preparation of a nano-UA suspension by wet grinding, researches the influence of process parameters on particle size, and explores the rules of particle breakage and agglomeration by combining model fitting. Methods: Wet grinding experiments were conducted using a laboratory-scale grinding machine. The particle size distributions (PSDs) of UA suspensions under different grinding conditions were measured using a laser particle size analyzer. A single-factor experimental design was employed to optimize operational conditions. Model parameters for a population balance model considering both breakage and agglomeration were determined by an evolutionary algorithm optimization method. By measuring the degree to which UA inhibits the colorimetric reaction between salicylic acid and hydroxyl radicals, its antioxidant capacity in scavenging hydroxyl radicals was indirectly evaluated. Results: Wet grinding process conditions for nano-UA particles were established, yielding a UA suspension with a D50 particle size of 122 nm. The scavenging rate of the final grinding product was improved to three times higher than that of the UA raw material (D50 = 14.2 μm). Conclusions: Preparing nano-UA suspensions via wet grinding technology can significantly enhance their antioxidant properties. Model regression analysis of PSD data reveals that increasing the grinding mill’s stirring speed leads to more uniform particle size distribution, indicating that grinding speed (power) is a critical factor in producing nanosuspensions. Full article

Background/Objectives: Among different milling techniques, spiral air jet milling can produce finer particles without the use of solvents or additives, thereby improving the bioavailability and content uniformity of the final dosage form. However, milling can complicate downstream processability of active pharmaceutical ingredients (APIs) due to reduced bulk powder flowability and post-milling lump formation. Process settings are often optimized only for particle size reduction, without sufficient consideration of manufacturability, largely because of limited API availability and a lack of knowledge about influential material properties. This study aimed to investigate the impact of material properties and process settings on milling performance and downstream manufacturability. Methods: Four APIs, examined in a total of eight grades, were characterized for their bulk mechanical properties and compression energy parameters using a compaction simulator. These grades were subjected to milling experiments within a design-of-experiments framework. Statistical analyses were performed, and population balance models (PBMs) were developed and calibrated for each experiment to link material properties and process settings to milling outcomes. Results: A higher gas flow rate was identified as the most significant contributor to particle size reduction. The influence of mechanical properties, particularly Young’s modulus and Poisson’s ratio, was evident and correlated with unmilled particle sizes. PBM analyses showed that a higher gas feed rate decreased the critical particle size for breakage, while intrinsic mechanical properties affected the breakage rate function. Conclusions: By integrating material properties and process settings into PBM analyses, specific breakage mechanisms could be identified. These findings provide a framework for optimizing jet milling not only for particle size reduction but also for downstream processability of APIs. Full article

GHz-burst laser polishing is as a promising technique for improving the surface quality of metallic materials, offering key advantages over conventional methods. In this study, two distinct approaches are investigated: a single-step polishing process, and a double-step process consisting of an initial laser milling step followed by a finishing/polishing pass. This distinction is critical in evaluating the performance of GHz-burst regimes under different surface conditions and roughness levels. Initial proof-of-concept trials confirm that GHz-burst irradiation can significantly reduce the surface roughness with minimal thermal damage, provided that process parameters are carefully optimized. Further analysis of spot-to-spot overlap reveals that the deposited energy density plays a crucial role in achieving uniform surface quality without inducing surface defects. The number of passes is also studied, showing that while multiple passes can improve surface finish, the benefit strongly depends on the initial roughness state of the substrate. Scalability is demonstrated by increasing both the repetition rate and scan speed proportionally while maintaining processing quality across larger areas. These results support the viability of GHz-burst laser polishing for high-throughput manufacturing. Applications in aerospace, biomedical implants, and precision optics highlight the technique’s potential for industrial adoption in demanding surface finishing contexts. Full article

The milling process is a critical technological step that regulates wheat flour characteristics and ultimately determines end-product quality. This study systematically evaluated the effects of three key milling parameter adjustments in a laboratory-scale roller mill—double sifting (2S), double break milling (2BM), and increased roll gap (IRG)—on the physicochemical properties of wheat flour and the quality of microwave freeze-dried non-fried instant noodles. The results demonstrated that milling processes significantly influenced the particle size and composition of flour. The 2BM-IRG process increased the volume mean diameter of flour to 86.38 μm, while significantly improving flour extraction rate (69.80%), protein content (10.98%), and ash content (0.54%). In contrast, the 2S process significantly reduced the volume mean diameter (65.27 μm). These changes in flour properties directly affected noodle quality—noodles made from 2BM-IRG flour exhibited the highest rehydration ratio but also the greatest cooking loss, along with the lowest expected glycaemic index (eGI); noodles produced from 2S flour showed the highest hardness, while the 2BM process endowed noodles with superior elasticity. A correlation analysis revealed that the digestibility characteristics of noodles (eGI) were predominantly and significantly influenced by flour protein and ash content (p < 0.01), while also being significantly affected by particle size (p < 0.05). The study confirmed distinct quality trade-offs between different milling strategies. Therefore, by optimizing combinations of break milling and sifting processes, it is possible to develop specialized flour tailored for specific quality requirements. Full article

This study introduces a real-time acoustic emission (AE) monitoring system for the micro-milling of fluorine-doped tin oxide (FTO) thin films, a critical transparent conductive oxide (TCO) material. The system uses AE sensors to capture high-frequency elastic waves generated during the micro-milling process. We combine experimental and theoretical analyses to investigate how various milling parameters influence the AE signals. To address the crucial challenge of ensuring ductile mode cutting in brittle materials like FTO, we employed a convolutional neural network (CNN) to identify the transition between ductile and brittle machining modes. A CNN was trained on energy-based features extracted from the AE signals, achieving a classification accuracy of 97.37%. This high accuracy demonstrates the effectiveness of integrating AE sensing with deep learning for interpreting complex micro-machining data. The results confirm that this combined approach offers a powerful, non-destructive, and intelligent monitoring solution for improving process control and understanding in the micro-milling of fragile conductive thin films. Full article

Cassiterite polymetallic sulfide ore exhibits a complex mineral composition and significant variations in mineral properties, which frequently lead to issues such as the over-grinding of cassiterite and under-grinding of sulfide minerals during the grinding process. These issues consequently impair liberation performance in subsequent beneficiation stages. Among these factors, the grinding media ratios stand as one of the critical factors influencing grinding efficiency. Based on these, the paper adopts the single-factor test method to systematically study the influence law of factors such as grinding time, mill rotational rate, and mill filling rate on the particle size composition of ore grinding products and the grinding technology efficiency under different media conditions; in addition, it is compared with the influence law of different conditions of media ratios on the grinding efficiency of ore. The results show that the optimal parameters of the grinding operation are obtained at the grinding time of 4 min, the mill rotational rate of 60%, and the filling rate of 35%. The grinding time and mill filling rate have a relatively more significant effect on the product particle size distribution, while the effect of the mill rotational rate is relatively less significant. When the parameters of grinding operations are optimal, the yield of qualified particle size and grinding technical efficiency are used as the evaluation indices, respectively. Overall, the order of the grinding effect of different media conditions was as follows: steel ball combination of Φ20 mm and Φ25 mm > steel balls of three single sizes > steel ball combination of Φ20 mm and Φ30 mm. The optimal grinding media ratios are Φ20 mm and Φ25 mm (the percentage of the Φ20 mm ball is 90%). The reasonable media ratios will effectively coordinate the optimal grinding effect between different media. The research results can provide the necessary basic data for the subsequent grinding optimization of cassiterite polymetallic sulfide ores. Full article

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, remains underexplored in the literature. This study addresses this gap by systematically evaluating the influence of key process parameters—cutting speed (V_c), axial depth of cut (a_p), and tool diameter (D_t)—on hole quality attributes, including surface roughness, burr formation, and nominal diameter accuracy. A full factorial experimental design (2³) was employed, coupled with analysis of variance (ANOVA), to quantify the effects and interactions of these parameters. The results reveal that, with a higher V_c, it is possible to reduce surface roughness (R_a) by 30% to 40%, while an increased a_p leads to a 50% increase in R_a. Additionally, D_t emerged as the most critical factor for nominal diameter accuracy, reducing geometrical errors by 1% with a larger D_t. Burr formation was predominantly observed at the lower end of the hole, highlighting challenges specific to this technique. These findings provide valuable insights into optimizing helical milling for low-carbon steels, offering a foundation for broader industrial adoption and further research. Full article

As manufacturing demands for challenging-to-machine metallic materials continue to evolve, the performance of cutting tools has emerged as a critical limiting factor. The synergistic application of micro-texture and coating in cutting tools can improve various properties. For the processing of existing micro-texture, because of the fast cooling and heating processing method of laser, there are defects such as remelted layer stacking and micro-cracks on the surface after processing. This study introduces a preheating-assisted technology aimed at optimizing the milling performance of textured coated tools. A milling test platform was established to evaluate the performance of these tools on titanium alloys under thermally assisted conditions. The face-centered cubic response surface methodology, as part of the central composite design (CCD) experimental framework, was employed to investigate the interaction effects of micro-texture preparation parameters and thermal assistance temperature on milling performance. The findings indicate a significant correlation between thermal assistance temperature and tool milling performance, suggesting that an appropriately selected thermal assistance temperature can enhance both the milling efficiency of the tool and the surface quality of the titanium alloy. Utilizing the response surface methodology, a multi-objective optimization of the textured coating tool-preparation process was conducted, resulting in the following optimized parameters: laser power of 45 W, scanning speed of 1576 mm/s, the number of scans was 7, micro-texture spacing of 130 μm, micro-texture diameter of 30 μm, and a heat-assisted temperature of 675.15 K. Finally, the experimental platform of optimization results is built, which proves that the optimization results are accurate and reliable, and provides theoretical basis and technical support for the preparation process of textured coating tools. It is of great significance to realize high-precision and high-quality machining of difficult-to-machine materials such as titanium alloy. Full article

Next to solid wood, laminated particleboard is the most widely used wood-based material in the furniture industry. Ensuring the high quality of the laminate surface after machining is of critical importance for furniture manufacturers, particularly prior to the edge banding process, as this process significantly influences the final aesthetic and functional quality of panel elements. The objective of this review article is to gather and evaluate the current state of knowledge regarding the influence of machining process parameters and the physical and mechanical properties of laminated particleboard on machining quality. Particular emphasis is placed on the occurrence of laminate damage, commonly referred to as delamination, a prevalent defect in the furniture manufacturing sector. Both categories of influencing factors—process-related and material-related—are analyzed within the context of the three primary technological processes employed in the woodworking industry, namely drilling, cutting, and milling. The analysis revealed that a persistent research gap concerns the relationship between machining quality and material parameters, particularly in the case of milling—a process of critical importance in the furniture industry. Full article

This study investigates the applicability and advantages of using additive manufacturing to moderate heat generation in dry milling. Grinding medium balls of different sizes were designed and fabricated using computer-aided design (CAD) and a stereolithographic 3D printer. Milling processes with particle size distribution and warmup measurements were employed with the printed medium balls. The results were compared with the measurements executed with conventional stainless-steel balls. Differential scanning calorimetry (DSC) was employed to evaluate the effect of the warmup of the system during the milling process. A two-variable, three-level experimental design was used for the measurements. We selected two grinding parameters considered critical: speed and time. The effect of these two independent variables on heating was examined. The results show that if printed balls are applied with the same total mass as that of metal balls, the particle size reduction is increased. The greater the number of balls used, the greater the particle size reduction. In this process, where additively manufactured milling bodies were used, the temperature of the system increased by less than when stainless-steel balls were used. The use of 3D-printed medium balls demonstrated beneficial warmup behavior. Full article

The growing demand for cost-effective, high-quality protein ingredients in the meat industry highlights the need for advanced processing methods capable of producing uniform, functional meat–bone pastes from poultry by-products. This study investigates the optimization of colloid milling parameters for the fine grinding of chicken meat–bone by-products, with a focus on improving particle size distribution, rheological properties, and processing efficiency. A modified rotor–stator system with teeth inclined at 20° and a reduced pitch (0.5 mm) was compared to a conventional configuration (45° inclination, 1.5 mm pitch). Experiments were conducted at rotor speeds ranging from 1000 to 4000 rpm, with a fixed clearance of 0.1 mm. The results showed that the modified design significantly enhanced grinding efficiency, reducing the proportion of bone fragments > 1 mm and yielding over 70% of particles under 0.1 mm at 3000 rpm. Viscosity and shear stress measurements indicated that grinding at 3000 rpm yielded a dynamic viscosity of 71,507 Pa·s and a shear stress of 43,531 mPa·s, values that were significantly lower (p < 0.05) than those observed at other tested speeds, thereby producing a paste consistency with the most favorable balance of elasticity and flowability. At 4000 rpm, the temperature rise (up to 32 °C) led to partial denaturation of muscle proteins, accompanied by emulsion destabilization and disruption of the protein gel matrix, resulting in reductions in the viscosity and water-binding capacity of the paste. Comparative analysis confirmed that tool geometry and rotor speed have critical effects on grinding quality, energy use, and thermal load. The optimal operating parameters, 3000 rpm with modified rotor–stator teeth, achieve the finest, most homogeneous bone paste while preserving protein functionality and minimizing energy losses. These findings support the development of energy-efficient grinding equipment for the valorization of poultry by-products in emulsified meat formulations. Full article

Rare earth elements (REEs), particularly neodymium (Nd), dysprosium (Dy), and praseodymium (Pr), are critical in the production of neodymium–iron–boron (NdFeB) magnets used in electronic devices, wind turbines, and electric vehicles. Due to the limited availability of these metals, their recovery from waste electronic equipment such as hard disk drives (HDDs) offers a promising solution. The aim of this study was to develop a method to grind NdFeB magnets obtained from the physical recycling of HDD. The recycled magnets were ground using a planetary mill. A review of the literature highlights the limitations of the currently used grinding methods, which require energy-intensive pretreatment processes, specialised conditions, or expensive equipment. This study employed a Fritsch planetary mill, tungsten carbide grinding balls, and ethanol as a grinding medium. NdFeB magnet samples (120 g) were ground for different durations (0.5 h–15 h) at a speed of 300 rpm, using a cyclic operating mode to minimise material heating. The resulting powders were analysed using a laser particle analyser, an optical microscope, and an X-ray diffractometer. The results enable the determination of optimal grinding parameters, achieving an average particle size (d₅₀) below 5 μm, which is essential for further processing and new magnet production. Finally, the economic and environmental aspects of producing the neodymium alloy were analysed. Full article

Cutting force is a critical indicator reflecting the interaction between the cutting tool and the workpiece in machining processes. Conventional measurement methods using dynamometers are accurate but costly and challenging for real-time applications. This study proposes a novel transfer learning-based method for estimating milling cutting forces using only spindle vibration signals without direct force sensors. The proposed approach consists of two stages: First, an autoencoder is trained with measured cutting force data to construct a latent feature space. Second, a target encoder aligns spindle vibration signals to this latent space, allowing the decoder to reconstruct estimated cutting forces. To reflect machining parameters into the learning model, the input dataset was constructed by integrating material type, cutting speed, and cutting direction as additional inputs into each model’s inputs. Experiments were conducted on Ti-6Al-4V and STS316L workpieces under various machining conditions. Under normal conditions, the proposed method achieved an average Pearson correlation coefficient (PCC) of 0.9213 ($F_x$) and 0.9072 ($F_y$). Under abnormal transient conditions, robust performance was maintained, with PCC values of 0.8573 ($F_x$) and 0.9202 ($F_y$). The results demonstrate that the proposed method can effectively monitor cutting forces and reflect changes across a variety of machining environments using only vibration signals. Full article

Micro-milling is increasingly recognized as a crucial technique for machining intricate and miniature 3D aerospace components, particularly those fabricated from difficult-to-cut Ti-6Al-4V alloys. However, its practical applications are hindered by significant challenges, particularly the unavoidable generation of burrs, which complicate subsequent finishing processes and adversely affect overall part quality. To optimize the burr formation in the micro-milling of Ti-6Al-4V alloys, this study proposes a novel hybrid-ranking optimization algorithm that integrates Grey Relational Analysis (GRA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This approach innovatively combines GRA and TOPSIS with a random forest regression (RFR) model, facilitating the exploration of nonlinear and complex relationships between input parameters and machining outcomes. Specifically, the effects of spindle speed, depth of cut, and feed rate per tooth on surface roughness and burr width generated during both down-milling and up-milling processes were systematically investigated using the proposed methodology. The results reveal that the depth of cut is the most influential factor affecting surface roughness, while feed rate per tooth plays a critical role in controlling burr formation. Moreover, the GRA-TOPSIS-RFR method significantly outperforms existing optimization and prediction models, with the integration of the RFR model enhancing prediction accuracy by 42.6% compared to traditional linear regression approaches. The validation experimental results agree well with the GRA-TOPSIS-RFR-optimized outcomes. This research provides valuable insights into optimizing the micro-milling process of titanium components, ultimately contributing to improved quality, performance, and service life across various aerospace applications. Full article

The rapid detoxification and mineralization of Cr(VI) in aqueous environments hold critical importance for emergency response and resource recovery yet remain technically challenging. Herein, we report the synthesis of FeS₂/ZVI composites through ethanol-assisted wet ball-milling and their application in Cr(VI) removal under microwave (MW) irradiation. This study systematically investigates the effects of MW irradiation on the removal efficiency of Cr(VI) using FeS₂/ZVI composites, with particular focus on key parameters including composite dosage, initial pH, MW temperature, and Cr(VI) concentration. Notably, 1 g/L FeS₂/ZVI composites achieved near-complete removal (>99%) of 50 mg/L Cr(VI) within 7 min at a MW irradiation temperature of 333 K, which exhibited 5.9-fold and 13.1-fold superior performance compared to pure pyrite and ZVI, respectively. Additionally, there is a 96.1% reduction in reaction time in comparison to non-MW irradiation system. In real electroplating wastewater samples, Cr(VI) concentration was reduced from 38.93 to 0.42 mg L⁻¹ by MW irradiation-assisted treatment, validating its potential for practical applications in industrial Cr(VI) pollution control. The activation energy determined by fitting the Arrhenius equation showed a 39.7% reduction for the MW-assisted FeS₂/ZVI system (16.0 kJ mol⁻¹) compared to conventional thermal heating (from 25.6 kJ mol⁻¹), indicating that MW irradiation induced catalytic enhancement of FeS₂/ZVI, thereby lowering the energy barrier for Cr(VI) reduction. Moreover, MW irradiation-assisted processes facilitated the mineralization of reduced Cr(III) to stable spinel FeCr₂O₄. These findings collectively establish a synergistic mechanism between MW activation and FeS₂/ZVI composites, offering innovative pathways for efficient Cr(VI) detoxification and resource recovery from high-strength industrial wastewaters. Full article