Search Results (151)

Cermet tools possess favorable mechanical and tribological properties and are widely adopted for machining hard-to-cut materials. However, their performance can further be enhanced with different cooling and lubrication techniques. In this study, the tool wear mechanisms of cermet tools during hard turning of AISI 4340 alloy steel are investigated under dry and minimum quantity lubrication (MQL) environments to identify the prevalent causes of tool failure through comprehensive analysis of tool wear progression, chip temperature, and chip morphological analysis. The results revealed that the application of MQL exhibited prolonged and stable steady-state tool wear progression with retained cutting-edge geometry, thus demonstrated 30.27% improvement in tool life compared to dry cutting. On the contrary, a rapid increase in tool wear due to excessive friction and higher thermal load is noticed with dry cutting in the absence of any heat-dissipating medium. Chip temperature measurements supported these observations, as chip temperature increases sharply from 358 °C (with a fresh tool) to about 1090 °C (with a worn tool) under a dry environment. Conversely, with MQL, the corresponding increase was in the range between 294 °C and 843 °C with a fresh and worn tool, respectively. Chip analysis revealed a serrated type of chip morphology. Dry cutting exhibited intensified feed marks, indicative of severe tool–chip friction, whereas MQL demonstrated smoother morphology with closely spaced saw-tooth patterns. Tool wear mechanisms indicate abrasion, adhesion, and edge chipping as dominant wear mechanisms under both environments; however, in the absence of any lubricant, these mechanisms were more intensified with higher crater formation. Full article

Winter pruning is a key management practice in viticulture that directly affects vine architecture, yield balance, and grape quality. At the same time, it is a highly labor-intensive operation, and the selective identification of appropriate cutting locations remains one of the main challenges limiting the automation of pruning in vineyards. Advances in machine vision provide new opportunities to support the development of robotic pruning systems. The objective of this study was to develop and evaluate a vision-based method for estimating grapevine pruning points and cutting lines using instance segmentation outputs generated by YOLO models. A dataset of 1500 RGB images of dormant grapevines was collected under field conditions in the Nobilis vineyard located in southeastern Poland. Two annotation strategies were implemented to define pruning regions. YOLO-based instance segmentation models were trained and evaluated for detecting cutting-related structures. Based on the predicted segmentation masks, a geometry-based method termed PCAcutSeg-V was developed to estimate class-dependent cutting points and cutting lines using principal component analysis applied to object contours. The results indicate that YOLOv8 and YOLO11 architectures achieved the highest segmentation performance among the evaluated models. The simplified annotation strategy provided more stable geometric inputs for the PCAcutSeg-V method, enabling more reliable estimation of cutting points and cutting lines compared with the extended annotation approach. When combined with the PCAcutSeg-V method, the proposed perception–geometry pipeline achieved high effectiveness in pruning decision estimation. The method was further implemented in a real-time processing pipeline using an RGB camera and an edge computing platform, where it maintained performance consistent with the results obtained from offline image analysis. These findings demonstrate that combining deep learning-based instance segmentation with deterministic geometric reasoning enables accurate and interpretable estimation of grapevine pruning locations and provides a promising foundation for future autonomous pruning systems. Full article

The face of cutting tools serves as the critical interface for chip–tool interaction and wear initiation, significantly influencing tool performance and service life. By implementing micro-groove structures on the face to reduce the chip–tool contact area, the cutting mechanics of the tool are altered. Theoretical analysis indicates that the cutting equations of the grooved tool have changed, with the modified tool exhibiting a larger shear angle compared to the original design. Finite element simulations and experiments demonstrate that grooved tool exhibit optimized cutting mechanics, characterized by a larger shear angle and improved edge sharpness. The shear angle of grooved tool is increased by about 3 degrees and the chip thickness is reduced by about 0.05 mm. Cutting tests confirm that the grooved tool reduces the main cutting force by more than 18%, with a smaller wear area on the face and improved wear conditions near the cutting edge. Due to materials such as stainless steel and titanium alloy, which have similar difficult-to-machine properties. The present results are based on AISI 201 and the specific groove geometry used in this study, and further work is required before generalizing to other difficult-to-cut materials and groove designs. In summary, based on the experimental data, the micro-groove cutting tool outperforms the original tool in terms of shear angle, cutting force, and durability. Specifically, the shear angle of the micro-groove cutting tool is larger, the cutting force is reduced, and the wear on the face is decreased. Full article

Sharpness and cutting edge retention are critical performance metrics for kitchen knives. Their combined effectiveness is governed by the synergistic effects of edge geometry and material microstructure. The present study selected six representative knife steels, namely 3Cr13, 1.4116, 9Cr18MoV, T10, GCr15, and CPM 3V, to fabricate the experimental knives with edge inclusive angles of 18°, 24°, and 30°. Standardized CATRA cutting tests were conducted to evaluate the effects of material microstructure and edge geometry on initial cutting performance (ICP) and total card cut (TCC), serving as the direct metrics for sharpness and cutting edge retention, respectively. The underlying mechanisms responsible for the cutting behavior were elucidated through scanning electron microscopy, quantitative analysis of carbides, and measurements of edge wear volume. The roles of carbide number, size, and morphology in ICP and TCC were systematically analyzed. Furthermore, multivariate linear regression models were established to quantitatively correlate ICP and TCC with edge inclusive angle, material hardness, average carbide diameter, and edge width. The results indicate that the edge inclusive angle predominantly determines ICP, while TCC is primarily controlled by the synergistic interaction between carbide characteristics and matrix hardness. Although a smaller edge inclusive angle significantly enhances ICP, it also accelerates edge wear and reduces cutting efficiency. By comprehensively considering both ICP and TCC, an optimal edge inclusive angle range was identified for each material to achieve balanced cutting performance. This work provides experimental evidence and quantitative guidance for the material selection and edge geometry design of high-performance kitchen knives. Full article

The synthesis of continuous non-linear metal nanostructures at the micro and nanoscale remains a challenging frontier in nanotechnology due to inherent synthetic constraints. This study introduces an innovative chemical methodology for fabricating continuous rings and diverse geometries via the chemical fusion of gold nanorods (AuNRs) on a solid substrate. Initially, aqueous solutions of cetyltrimethylammonium bromide (CTAB)-coated AuNRs were deposited and dried on a solid substrate, resulting in the self-assembly of ring-like arrays. Subsequent chemical growth of the AuNRs in all dimensions was achieved using an aqueous solution of Au(I)/CTAB/Ascorbic Acid (AA), enabling their fusion into continuous structures. This approach permits the formation of arbitrary shapes by pre-arranging AuNRs, thereby opening new avenues for the exploration of non-linear nanostructures with potentially novel plasmonic and electronic properties. The capability to engineer such complex nanostructures is pivotal for advancing fields such as photonics, electronics, and sensing, where the unique optical and electronic properties of gold nanostructures can be exploited for cutting-edge applications. Furthermore, this technique shows a significant promise for the fabrication of various micro- and nanodevices and the seamless interconnection of components in integrated electronic circuits, potentially leading to more efficient and miniaturized electronic systems. The broader implications of this research are significant, offering a potential pathway to the development of nanomaterials and devices that could benefit various industries and technological processes. Full article

Point cloud technology, characterized by its high-precision 3D geometric acquisition in complex railway environments, has become a cornerstone for the intelligent detection, monitoring, and maintenance of railway infrastructure. This paper provides a systematic review of point cloud applications across critical railway scenarios, encompassing track geometry extraction, infrastructure component identification, tunnel and bridge modeling, clearance and encroachment analysis, and structural condition monitoring. We evaluate various mobile and stationary acquisition platforms alongside their typical data processing workflows. Furthermore, this review synthesizes cutting-edge advancements in processing algorithms, with a focus on feature extraction, semantic segmentation, and the transformative impact of deep learning and artificial intelligence on data fusion. Notably, the paper explores the synergy between point clouds and computational mechanics, specifically the construction of high-fidelity digital twins through multi-physics coupling to enable real-time simulation of structural stress distribution and damage evolution. We critically analyze persistent technical bottlenecks, such as acquisition efficiency, monitoring precision, data fragmentation, environmental interference, and the complexities of multi-modal data fusion. Finally, the paper outlines future research trajectories, focusing on autonomous intelligent sensing, multi-sensor integration, and the comprehensive digital transformation of railway infrastructure management, aiming to provide a robust theoretical framework and technical roadmap for the sustainable intelligentization of global railway systems. Full article

Purpose: The aim of this in vitro study was to evaluate the degree of surface wear in implant drills from four commercial systems subjected to standardized osteotomy cycles. Materials: Four implant systems (Osstem, Megagen, Straumann, and Bego) were evaluated using sets of three drills of increasing diameters. A total of 120 osteotomies were performed in standardized porcine rib specimens under controlled drilling conditions (1200 rpm, continuous 4 °C saline irrigation, 32:1 reduction handpiece). After each drilling series, drills were cleaned, sterilized, and analyzed using SEM in three orientations. Wear was assessed using a seven-parameter scoring system. Multifactorial ANOVA, Pearson correlation, and hierarchical clustering were employed to evaluate the effects of drill brand, diameter, and wear patterns. Results: Both drill brand and diameter significantly influenced total wear scores (p < 0.001). Small-diameter pilot drills exhibited the highest wear, while large-diameter drills showed minimal degradation. Among the systems tested, Bego drills demonstrated the greatest overall wear, whereas Osstem drills—particularly the 2.0 mm drill—displayed unusually low wear for their size. A strong negative correlation between drill diameter and wear score was observed. Cluster analysis identified distinct wear patterns associated with specific drill sizes, with small drills showing prominent guide-face nicks and accumulation formation, medium drills exhibiting chipping and rake angle cleavage, and large drills presenting minimal wear. SEM imaging confirmed progressive surface deterioration, including edge rounding, microchipping, and irregular surface defects. Conclusions: Implant drill wear is strongly dependent on drill diameter, and cutting geometry. Small-diameter drills are most susceptible to surface degradation, which may increase friction and thermal load during osteotomy. Systems with enhanced material properties or optimized geometries demonstrated superior wear resistance. These findings highlight the importance of monitoring drill condition, adhering to recommended reuse limits, and considering advanced drill coatings or materials to ensure safe and predictable implant site preparation. Further research incorporating real-time thermal measurements and extended drilling cycles is needed to establish evidence-based guidelines for drill longevity and clinical performance. Full article

The micro-geometry of the cutting edge plays a crucial role in material flow ahead of the cutting edge and chip formation, primarily influencing chip formation mechanisms and the minimum cutting thickness. In the context of turning 304 stainless steel, however, existing research still lacks a unified quantitative framework linking “cutting edge micro-geometry—material separation behavior (separation point/minimum uncut chip thickness)—microstructural evolution of the machined surface.” This gap hampers mechanistic optimization design aimed at enhancing machining quality. This study examines the turning of 304 stainless steel by integrating analytical modeling, finite element simulation, and experimental validation to develop a predictive model for minimum cutting thickness. It analyzes the effects of tool nose radius and asymmetric edge morphology, and a microstructure evolution prediction subroutine is developed based on dislocation density theory. The results indicate that the minimum cutting thickness exhibits a positive correlation with the tool nose radius, and their ratio remains stable within the range of 0.25 to 0.30. Under asymmetric edge conditions, the minimum cutting thickness initially increases and then decreases as the K-factor varies. The developed subroutine, based on the dislocation density model, enables accurate prediction of dislocation density, grain size, and microhardness in the machined surface layer. Among the factors considered, the tool nose radius demonstrates the most pronounced influence on microstructure evolution. This research provides theoretical support and a technical reference for optimizing cutting-edge design and enhancing the machining quality of 304 stainless steel. Full article

Cellular structures are well-established in biological materials and are often mimicked in many kinds of structural designs applicable to engineering. This results from their lightweight designs and good mechanical properties. Cellular designs in nature have extremely complex configurations. As a result, the deformation behaviour models for bioinspired hollow parts based on these geometries, that are presently available in the literature, are limited in their capacity to provide detailed descriptions of the mechanisms resulting in deformation. Extensions to the existing deformation behaviour mechanisms of cellular parts are proposed in this paper. First, a general outlook on cellular designs is given. This is followed by a review of the commonly recognised two-stage stress–strain curve for cellular parts and its comparison with the new curve suggested in this paper, which incorporates suggestions more fully accounting for the deformation mechanisms of these structures. Further, analytical models that are available in the literature, outlining the behaviour of cellular parts, are highlighted, together with new models developed here for predicting failure of lattice structures based on the Tresca and von Mises criterion. Next follows a discussion of proposed strategies that could be adopted in deformation behaviour models for optimising the design of hollow structures to improve their mechanical properties. Finally, the anticipated challenges for and future insights into the incorporation of the cellular behaviour models suggested here, in cutting-edge structural design for additive manufacturing (AM), are highlighted. 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

This study investigates vibration signals generated during end milling of thin-walled EN AW-7075 aluminum alloy components using a set of 24 tools with distinct cutting edge microgeometries. Five characteristic parameters describing the dynamic response of the process, including both energy-related and statistical indicators, were extracted and analyzed. The results clearly demonstrate the critical influence of tool microgeometry on process dynamics. In particular, the introduction of an additional zero-clearance flank land at the cutting edge proved decisive in suppressing vibrations. For the most favorable geometries, the root mean square (RMS) value of vibration was reduced by more than 50%, while the spectral power density (PSD) decreased by up to 70–75% compared with the least favorable configurations. Simultaneously, both time- and frequency-domain responses exhibited complex and irregular patterns, highlighting the limitations of intuitive interpretation and the need for multi-parameter evaluation. To enable a synthetic comparison of tools, the Vibration Severity Index (VSI), which integrates RMS and kurtosis into a single composite metric, was introduced. VSI-based ranking allowed the clear identification of the most dynamically stable geometry. For the selected tool, additional analysis was conducted to evaluate the influence of cutting parameters, namely feed per tooth and radial depth of cut. The results showed that the most favorable dynamic behavior was achieved at a feed of 0.08 mm/tooth and a radial depth of cut of 1.0 mm, whereas boundary conditions resulted in higher kurtosis and a more impulsive signal structure. Overall, the findings confirm that properly engineered cutting-edge microgeometry, especially the formation of additional zero-clearance flank land significantly enhances the dynamic of thin-wall milling, demonstrating its potential as an effective strategy for vibration suppression and process optimization in precision machining of lightweight structural materials. Full article

Granite wet edge finishing is widely adopted to improve surface durability and aesthetics while reducing dust dispersion compared to dry processes. However, even under flooded lubrication, fine particles (FP, 0.5–20 µm) and ultrafine particles (UFP, <100 nm) containing crystalline silica are emitted, posing health risks such as silicosis and pulmonary or cardiovascular diseases. This study investigates particle emissions during CNC edge finishing of black (containing 0% quartz) and white granites (containing 41% quartz) using two industrially relevant profile tools: Half-Beveled (HB) and Ogee (OG). A full factorial design evaluated the effects of granite type, tool geometry, abrasive grit size, spindle speed, and feed rate. Particle concentrations were measured with Aerodynamic and Scanning Mobility Particle Sizers. Results show that spindle speed (N) is the dominant factor, explaining up to 92% of variance in emissions, whereas feed rate (V_f) played a minor role. Tool geometry had a pronounced effect on UFP release: sharp-edged geometries (HB) promoted localized micro-fracturing and higher emissions, while curved geometries (OG) distributed stresses and reduced particle detachment. White granite generated higher mass emissions due to its high quartz content, while black granite exhibited more stable emission behavior. These findings highlight the dual necessity of optimizing cutting kinematics and selecting appropriate tool profiles to balance surface quality and occupational health in granite processing. Full article

This paper deals with the consumption of energy during the turning of low-alloyed steel 16MnCr5. The study employs the earlier reported methodology for the decomposition of energy in cutting during turning. The energy for chip formation, as well as the energy consumed in the interface between the tool flank and produced surfaces, can be singled out. The paper investigates the turning process as a function of the cutting conditions as well as the variable cutting edge geometry. It was found that the integration of a chip former valuably contributes to the lower chip ratios, as well as the more favourable shape of chips. The lower energy consumed in the tool flank region for the tool with the integrated chip former results in lower normal and shear forces despite the higher cutting edge radius. However, the differences in the surface strain accumulation expressed in terms of the dislocation density and residual stress depth profiles are only subtle. Full article

Indexable face milling tools are often used to machine workpieces with large axial and radial depth of cuts, and thus, the inserts quickly wear out in machining. A kernel technique of smart machining is tool wear compensation, which is to regularly and automatically measure the insert radius/length with a laser tool setter on the machine table during machining, and compensate them in the subsequent machining. Another technique is tool condition monitoring, which is to calculate the insert flank wear width for tool condition and compare with its threshold. When it is less than but close to its threshold of invalid inserts, the cutting tool is automatically changed right before it becomes invalid. On-machine laser tool setters have been equipped in CNC machine tools for several years; however, they cannot conduct cutting tool condition monitoring. The main reason is that the insert flank wear width cannot be measured on the on-machine laser tool setter, and the status quo is that the cutting tool is replaced either too early or too late. To address this problem, a novel tool condition monitoring technique of determining the insert flank wear width of indexable face milling tool using on-machine laser tool setters is proposed. According to the insert geometry, the worn cutting edge and a new workpiece milling mechanism proposed in this work, the insert flank wear width can be calculated. In machining, the insert radius wear is measured on the on-machine laser tool setter, and the insert flank wear width is calculated to evaluate whether it is invalid soon. The results indicate that the optimal height for radius measurement is located near the intersection of the corner and side edges point MR₃, and close to the cutting depth point MR₅. A wear land width threshold of 0.10 mm is established to define tool failure. The proposed calculation method achieves high accuracy, maintaining calculation errors within 14.00%. The inserts can be used in good condition with the maximum lifespan. This method has been verified in machining applications and can be directly applied in industry. Full article

Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article