Search Results (686)

Chitinases catalyze the hydrolysis of β-1,4-glycosidic bonds in chitin, a structural biopolymer synthesized by numerous organisms. Although these enzymes have been widely investigated, studies focusing on insect-derived chitinases remain limited. In this study, three recombinant chitinases from the leaf-cutter ant Atta sexdens were cloned, expressed in Pichia pastoris, and biochemically characterized. The enzymes-AsChtII-C2B3 (one catalytic and three chitin-binding domains), AsChtII-C3C4 (two catalytic domains), and AsChtII-C5B1 (one catalytic and one binding domain), exhibited optimal activity at pH 4–5 and 50 °C using colloidal chitin as substrate. Chitinase activity on colloidal α-chitin was confirmed by ¹H NMR (proton nuclear magnetic resonance) spectroscopy, revealing GlcNAc concentrations of 0.41, 0.48, and 0.56 mmol L⁻¹ for AsChtII-C3C4, AsChtII-C2B3, and AsChtII-C5B1, respectively. Their antifungal activities were evaluated against the human pathogens Candida albicans and Aspergillus fumigatus, as well as the phytopathogen Lasiodiplodia theobromae. Distinct inhibition profiles were observed: AsChtII-C5B1 (150 µg/mL) showed the highest activity against C. albicans (87.6% inhibition), while AsChtII-C3C4 (25 µg/mL) was most effective against A. fumigatus (60% inhibition). Notably, only AsChtII-C2B3 inhibited L. theobromae growth, inducing severe hyphal deformations observed by scanning electron microscopy (SEM). These findings demonstrate that recombinant A. sexdens chitinases exhibit species-specific antifungal properties, underscoring their potential as biotechnological tools for medical and agricultural applications. Full article

Due to the unique microstructure and mechanical heterogeneity of austenitic stainless steel made via wire arc additive manufacturing (WAAM), its machinability differs significantly from that of rolled material. Accordingly, this study systematically investigates the influence of milling strategies on key process responses (cutting forces, surface roughness, vibration displacement, and temperature) to reveal the mechanisms of machining parameters during the milling of WAAM-fabricated austenitic stainless steel. The material used in this study is ER321 austenitic stainless steel. During deposition, the fusion zone cools more slowly than the transition zone; consequently, the fusion zone exhibits a hardness approximately 20 HV_0.1 lower than that of the transition zone. Surface roughness is primarily reduced by decreasing the primary feed per tooth. However, when the primary feed per tooth is small, ploughing is induced, which not only increases surface roughness by 25% but also causes abnormal increases in temperature and vibration displacement. Nevertheless, ploughing has little effect on the total milling force, and the feed per tooth shows a positive correlation with the total milling force. Tool run-out and an increase in the uncut chip thickness lead to a positive correlation between the radial depth of cut and the key process responses. Moreover, ploughing also occurs when the radial depth of cut is small. The axial depth of cut has almost no effect on the machining process. Moreover, a small-diameter mill leads to severe ploughing, and at a high table feed, climb milling leads to cutter offset. Full article

The tool-point frequency response function (FRF) of a spindle–tool system plays a crucial role in predicting machining stability. Among the factors influencing the FRF, the interface characteristics between the spindle and the tool holder are particularly significant, especially when different holder designs are used. This study focused on identifying these interface characteristics for two common tool holder types—BT and BBT—to improve FRF prediction accuracy. The receptance coupling substructure analysis (RCSA) method was employed in conjunction with finite element modeling (FEM) to characterize the spindle–tool holder interfaces without needing extensive experimental tapping tests. Finite element models were developed to generate receptance components for various tool holder–tool assemblies, enabling efficient and accurate coupling within the RCSA framework. The identified interface parameters were applied to predict the tool-point FRFs of the cutter clamped in a BT tool holder with different overhang lengths. The predicted and measured tool compliances differed by 3–6.4%, demonstrating high agreement and reliability. The proposed methodology provides a powerful tool for predictive modeling of dynamic behavior in spindle–tool systems under varying tooling conditions, enhancing process planning and evaluation of the cutting stability in high-precision machining. Full article

To address the challenges of wear resistance for shield cutterheads and cutters in high-abrasion sandy cobble strata, this study uses the Beijing Metro Line 19 tunnel Niujie–Jinrongjie section as an engineering case study. It employs the DEM to develop a crushable sandy cobble model, evaluate the stress characteristics of fishtail cutters, rippers, and scrapers, and analyze load distribution in the cutterhead and cutters—including underlying causes. Based on simulations, the study proposes and implements targeted wear-resistant designs for field application. The results indicate that the stress variation patterns of fishtail cutters, rippers, and scrapers with respect to time and installation radius are similar. The cutterhead’s opening distribution significantly influences the intensity of normal and lateral stresses. Caused by cutting resistance, high-stress loads in cutters accumulate at the cutting edge, while those in the cutterhead localize to the soil-facing side of its spokes. Meanwhile, hindered muck flow and cutting failure of gauge cutters also cause stress concentration in the cutterhead’s transition zones and outer side of the large ring. Adopting a DEM-based method that characterizes the stress of the cutterhead and cutters to develop targeted wear-resistant designs can effectively control the wear of cutters and cutterheads in in situ engineering. The rate of abnormal cutter damage was limited to merely 5.84%, while the observed wear of the ripper cutters remained consistently below the values predicted by the IHI empirical model. This study provides a scientific basis for wear-resistant design of cutterheads in similar high-abrasion strata. Full article

This study establishes a systematic and reproducible workflow for topology optimization (TO) of indexable face milling cutter bodies with integrated internal coolant channels, designed for Additive Manufacturing (AM) of metallic parts. Grounded in Design for Additive Manufacturing (DfAM) principles, the workflow combines displacement-based TO and computational fluid dynamics analysis to generate simulation-driven tool geometries tailored to the constraints of AM. By leveraging iterative design knowledge, the proposed methodology enhances the scalability and repeatability of the design process, reducing development time and supporting rapid adaptation across various tool geometries. AM is explicitly exploited to integrate support-free internal coolant channels directed toward the insert cutting edge, thereby achieving a 20% mass reduction relative to the initial milling tool designs, and improving material usage efficiency at the design stage. The workflow yields numerically optimized geometries that maintain simulated global stiffness under the considered loading conditions and exhibit coolant flow distributions that effectively target the exposed cutting edges. These simulation results demonstrate the feasibility of an AM oriented, workflow-based approach for the numerical design of milling tools with internal cooling, mass reduction and provide a focused basis for subsequent experimental validation and comparison with conventionally manufactured counterparts. Full article

Tool wear degrades sharpness and durability, causing poor surface quality, dimensional errors, and high costs. Precise RUL prediction optimizes production, reduces rework, and prevents downtime. Conventional replacement relies on experience and risks inaccuracy. Real-time monitoring enables optimal intervals. Predictive maintenance cuts tooling costs and ensures quality. Industry 4.0 integrates sensors for intelligent wear management. This study applies GRNN to predict RUL with minimal TMD. A C#-based system with intuitive HMI was validated in real machining. Full article

The milled surface topography of NiTi SMA critically affects its frictional behavior, corrosion resistance, and biocompatibility, which are essential for biomedical and aerospace applications. This study combines simulation and single-factor experiments to investigate the coupling behavior among surface topography evolution, work hardening, plastic deformation, and residual stress evolution. Results showed that increasing feed per tooth led to a significant rise in surface residual height and an improvement in surface isotropy. With the increase in feed per tooth, the error between the experimental and simulated heights gradually decreased from 105.6% to 30.9%, indicating that both material properties and feed per tooth strongly affect residual profile formation in the feed direction. In addition, larger feed per tooth intensifies work hardening and plastic deformation but reduces surface residual stress, thereby increasing microhardness. These effects can mitigate material rebound and improve surface profile accuracy. The results provide a direct basis for controlling the surface integrity of NiTi SMA components through machining parameter optimization, enabling precise tailoring of functional surface characteristics, such as wear performance, chemical stability, and biological response, which is of critical importance for high-end biomedical implants and aerospace systems. Full article

This paper presents a simulation-based artificial neural network (ANN) model to predict bit-rock interaction forces during drilling. Drill string vibration poses a significant challenge in the oil, gas, and geothermal industries, leading to non-productive time and substantial financial losses. This research addresses the challenge of modelling bit-rock interaction excitation forces, which is crucial for predicting vibration and component fatigue life. For a PDC bit with multiple cutters, the cutter tangential velocities at various drilling speeds are calculated, and individual cutter forces are predicted with a two-dimensional discrete element method simulation in which a single cutter moves in a straight line through rock modelled as bonded particles. This data is then used to train an ANN model that characterizes the bit-rock force time series in terms of frequency, amplitude, and distribution of force peaks. Once inserted into a dynamic simulation of the drill string, the algorithm reconstructs the expected bit-rock force time series. A case study using a rigid segment axial and torsional drill string model was used to show that the bit-rock model outputs lead to the expected bit-bounce and stick-slip under certain drilling conditions. Next, the model was implemented in a 3D deviated well drill string simulation with non-linear friction and contact, generating complex stress states with good computational efficiency. Full article

This study focused on the dust in the ventilation of the underground coal mine of Ningwu Coalfield, Shanxi Province; the particle-size distribution and the mineralogical and geochemical characteristics of the vent dust were studied. The particle-size distribution of the vent dusts in the exhaust outlets of the four coal mines studied is similar and characterized by a single peak, which occurred at 3.5–4.0 μm. The minerals in the vent dusts are dominantly composed of kaolinite, followed by illite, quartz, calcite, dolomite, bassanite, and anhydrite. Except for the high content of bassanite, the vent dust discharged from the YS coal mine presents a similar mineral composition to the parent coal. Compared with the parent coal (and the Upper Continental Crust), the vent dust is enriched to varying degrees in the major element oxides Fe₂O₃, CaO, K₂O, Na₂O, and MgO, as well as trace elements Sb, Zn, Bi, Cd, Cu, As, W, and Pb, especially the contents of Sb, Zn, W, and As increased by 1177, 84, 15, and 12 times, respectively. The vent dusts emitted from these coal mines mainly come from the mining of coal seams; a small amount comes from the shotcrete and weathering products of the tunnel gallery, dust flame retardant, and the wear of coal cutters and coal transmission belts. Therefore, it is necessary to strengthen the management of coal mine vent dust emission to ensure that the mine vent emissions are pollution-free. Full article

Polycrystalline diamond compact (PDC) bits are widely used in oil, gas, and geological exploration. During rock breaking, most of the work is converted into cutting heat, leading to a rise in cutter temperature and potential damage. However, the influence of formation temperature and rock properties on cutting temperature and thermal stress remains insufficiently understood. This study combined numerical simulation and experimental methods to investigate the temperature rise and thermal stress of a single PDC cutter during rock breaking, focusing on the effects of formation temperature (27–250 °C) and rock strength (sandstone, marble, and granite). The results show that the temperature rise of the PDC cutter adheres to the following three distinct stages: rapid increase, slow increase, and stabilization. Rock strength significantly affects the temperature rise rate and stress; when breaking granite, the cutter temperature reached approximately 131.4 °C, about 2–3 times higher than for marble and sandstone, while the rate of penetration (ROP) decreased by 70.6–75.6%. As formation temperature increased from 27 °C to 250 °C, the internal temperature difference within the cutter decreased from 72.6 °C to 35.6 °C, and the equivalent stress first increased and then decreased, peaking at 2.84 GPa at 50 °C. The ROP initially increased and then decreased with an increase in formation temperature. Numerical simulations and experimental findings are in good agreement. This study provides theoretical and technical guidance for optimizing cutter design and improving the rock-breaking efficiency and service life of PDC bits in deep and high-temperature formations. Full article

Respirable quartz and dust exposures in dusty façade renovation work tasks were investigated. The presumption was that dust-producing work tasks can be performed safely, keeping exposures low, with practical, easily available methods to control dust emissions and exposure. The aim was to identify deficiencies in exposure management and compare exposure limiting methods to find out how to minimize dust emissions and exposures. Average respirable quartz and dust exposures from the 31 work situations, encompassing nine work tasks studied, were 0.082 and 1.3 mg/m³, respectively. Both values exceed the OEL in Finland, pointing to severe deficiencies in managing exposures. All tasks could, however, be executed safely, keeping exposures low. This often required using respirators while working inside façade covers or close to dust emissions. Other key things when planning exposure maintenance were the following: using water sprays and tool-specific exhausts vents; opening façade cover ventilation apertures; ensuring that non-participants in dusty work tasks are not exposed; working upwind from dust emissions; using pre-blended plaster; using grinders with extension handles; replacing diamond saws and angle grinders with hydraulic cutters when dismantling balcony elements; executing façade jackhammering with robots installed on lifting platforms prior to installing scaffolds and façade covers; detaching façade covers from the clean side; and using lifting platforms. Full article

The efficiencies of additive manufacturing (AM) over conventional processes have enabled the rapid production of aluminum (Al) alloys with AM. Because laser beam powder bed fusion (LB-PBF) parts do not offer the surface quality and geometrical accuracy for direct use, the functional surfaces of LB-PBF parts are usually machined by subtractive machining. The machinability of LB-PBF AlSi10Mg was studied in dry, MQL (used corn oil), and cryo-LN₂ cutting environments across distinct speed–feed combinations using CVD-AlTiN-coated carbide inserts, and surface integrity and tool life were quantified in terms of surface roughness (Ra) and flank wear (Vb), respectively. The lowest Ra (0.98–1.107 μm) was obtained with cryo-LN₂, followed by MQL and dry cutting environments, because the trends observed were consistent with the surface mechanisms observed in 3D topography and bearing curves. Similarly, the tool wear results mirrored the Ra results, lowest with LN₂ (0.087–0.110 mm), due to improved thermal management, reduced adhesion and abrasion, and shorter contact length. Cryo-LN₂ provided the best surface finish and tool life among all tested environments. To enable data-driven prediction, the limited dataset was augmented using SMOTE, and machine learning (ML) models were trained to predict Ra and Vb. CatBoost was found to yield the best Ra predictions (R² = 0.9090), while Random Forest and XGBoost yielded the best Vb predictions (R² ≈ 0.878). Full article

Drilling methods have been increasingly employed for shaft excavation in coal mines in western China. However, the rock fragmentation performance of milled-tooth roller cutters remains inadequate under Jurassic strata conditions. To address this issue, a numerical orthogonal simulation study based on the Finite-Discrete Element Method (FDEM) was conducted. Cutter tooth edge geometry, cutter diameter, cone angle, and penetration depth were considered as four factors at three levels. The effects of these factors on average force, specific energy, damage factor, and proportion of shear cracks were investigated. The efficiency coefficient method was then applied to identify the optimal cutter, and the 8^# roller cutter was determined to be the most effective. The results indicated that cutter tooth edge geometry had the most significant influence on average force and specific energy, whereas penetration depth primarily affected the damage factor and proportion of shear cracks. Compared with the prototype cutter, the 8^# cutter, characterized by a 370 mm large cone-end diameter, a 3° cone angle, and V-edged teeth, exhibited superior rock fragmentation efficiency, achieving a maximum improvement of 31%. These results provide a theoretical basis for the structural optimization of cutters used in shaft drilling in coal mines in western China. Full article

In operations involving the replacement of shield machine disc cutters, challenges such as limited space, poor lighting, and slurry contamination frequently lead to occlusions and incomplete data when using direct point cloud-based localization for disc cutter bolts. To overcome these issues, this study introduces an indirect visual localization technique for bolts that utilizes image-point cloud fusion. Initially, an SCMamba-YOLO instance segmentation model is developed to extract feature surface masks from the cutterbox. This model, trained on the self-constructed HCB-Dataset, delivers a mAP50 of 90.7% and a mAP50-95 of 82.2%, which indicates a strong balance between its accuracy and real-time performance. Following this, a non-overlapping point cloud registration framework that integrates image and point cloud data is established. By linking dual-camera coordinate systems and applying filtering through feature surface masks, essential corner coordinates are identified for pose calibration, allowing for the estimation of the three-dimensional coordinates of the bolts. Experimental results demonstrate that the proposed method achieves a localization error of less than 2 mm in both ideal and simulated tunnel environments, significantly enhancing stability in low-overlap and complex settings. This approach offers a viable technical foundation for the precise operation of shield disc cutter changing robots and the intelligent advancement of tunnel boring equipment. Full article

Construction monitoring of cutter suction dredgers (CSD) is of great significance in ensuring dredging efficiency. However, existing models have not taken into account the physical constraints in the physical system of a CSD, which limits further improvements in prediction accuracy. To this end, this paper proposes a physics-assisted deep learning model for improved construction performance monitoring of CSD. The data-driven Loss_u and the physics-driven Loss_r are combined to form an improved physics-assisted loss function (PALF). And then, a physics-assisted deep learning (PADL) model incorporating PALF is developed to predict the construction productivity. In the case application, evaluation across three deep learning models confirms the feasibility and effectiveness of PALF for productivity prediction. The results show that the PALF-based PADL model achieves markedly improved prediction accuracy, reducing the mean absolute error by 20.33–54.33%. Across six training-set sizes (1000–11,000 samples), the improvement is larger in small-data scenarios, highlighting PADL’s strong low-sample robustness. The proposed model can effectively complement physical sensors in monitoring construction parameters and provide reliable decision support for assessing the operational state of CSDs. Full article