Search Results (128)

The entanglement entropy of a random tensor network (RTN) is studied using tools from free probability theory. Random tensor networks are simple toy models that help in understanding the entanglement behavior of a boundary region in the anti-de Sitter/conformal field theory (AdS/CFT) context. These can be regarded as specific probabilistic models for tensors with particular geometry dictated by a graph (or network) structure. First, we introduce a model of RTN obtained by contracting maximally entangled states (corresponding to the edges of the graph) on the tensor product of Gaussian tensors (corresponding to the vertices of the graph). The entanglement spectrum of the resulting random state is analyzed along a given bipartition of the local Hilbert spaces. The limiting eigenvalue distribution of the reduced density operator of the RTN state is provided in the limit of large local dimension. This limiting value is described through a maximum flow optimization problem in a new graph corresponding to the geometry of the RTN and the given bipartition. In the case of series-parallel graphs, an explicit formula for the limiting eigenvalue distribution is provided using classical and free multiplicative convolutions. The physical implications of these results are discussed, allowing the analysis to move beyond the semiclassical regime without any cut assumption, specifically in terms of finite corrections to the average entanglement entropy of the RTN. Full article

Face milling with end-mill tools represents a solution for woodworking applications on small-scale or complex surfaces, but information regarding the surface quality per specific tool type, wood material, and processing parameters is still limited. Therefore, this study examined the surface quality of tangential oak and maple CNC face-milled with two end-mill tools—straight-edged and helical—for three values of stepover (5, 7, 9 mm) and two cutting depths (1 and 3 mm). The surface quality was analyzed with roughness parameters, roughness profiles, and stereomicroscopic images and was referenced to that of very smooth surfaces obtained by super finishing. The helical end mill caused significant fiber tearing in maple and disrupted vessel outlines, while prominent tool marks such as regular ridges across the grain were noticed in oak. The best surface roughness was obtained in the case of the straight-edged tool and minimum stepover and depth of cut, which came closest to the quality of the shaved surfaces. An increase in the cutting depth generally increased the core surface roughness and fuzziness, for both tools, and this trend increased with an increase in the stepover value. The species-dependent machining quality implies that the selection of tool geometry and process parameters must be tailored per species. Full article

The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Marquardt Back Propagation Algorithm (LMBP). The efficacy of these models was evaluated using RMSE, revealing that the LMBP model yielded the lowest RMSE for surface roughness (Ra), nose radius wear, and resultant cutting force, hence demonstrating superior predictive capability within the trained dataset. Additionally, a Genetic Algorithm (GA) was employed to ascertain the optimal machining settings, revealing that the ideal parameters include a cutting speed of 85 m/min, a feed rate of 0.07 mm/tooth, and a rounding radius of 7 mm. Moreover, the detachment of the coating layer resulted in alterations to the tooltip cutting edge on the machined surface as the circular loop distance increased. The initial arc radius fluctuated by 33.82% owing to tooltip defects that alter the edge micro-geometry of machining. The measured and expected values of the surface roughness, resultant cutting force, and nose radius wear exhibited discrepancies of 6.49%, 4.26%, and 4.1%, respectively. The morphologies of the machined surfaces exhibited scratches along with laces, and side flow markings. The back surface of the chip structure appears rough and jagged due to the shearing action. Full article

Skin is composed of three layers: the epidermis, dermis, and hypodermis. It is enriched with skin appendages, including hair follicles, sweat glands, and sebaceous glands, which play essential roles in regulating fluid exchange, controlling body temperature, and providing protection against pathogens. Currently, skin regeneration treatments rely on transplantations. However, this approach has several disadvantages, including hemostasis at the recipient site, limitations in donor area closure, increased graft contraction, and hypertrophic scarring. Recent advancements in three-dimensional (3D) bioprinting technologies have enabled the fabrication of structures that closely mimic native tissues, with the aim of enhancing tissue regeneration. Bioprinting offers several advantages, such as high reproducibility, precision, and the ability to create complex geometries. The most promising bioinks combine excellent biocompatibility and biodegradability, with mechanical and rheological stability. This review highlights the most recent and innovative studies on 3D-printed bioinks in the field of skin tissue engineering. In particular, considering the growing interest in the regenerative potential of exosomes, we discuss cutting-edge research involving exosome-loaded bioinks and their potential to support skin regeneration and repair. Full article

This study examines the impact of PVD coatings on cutting tool wear during the milling of 316L stainless steel under air cooling conditions. In the experiment, a carbide milling cutter coated with a nanocomposite nACo3 (AlTiSiN) coating was used. The coating was deposited using a next-generation device, the PLATIT π411PLUS, which features one central and three lateral rotating cathodes. The nanocomposite nACo3 coating obtained with this method exhibits exceptionally high structural density and excellent mechanical properties. The new generation of the nACo3 coating demonstrates improved surface properties and a lower friction coefficient compared to previous generations. The findings indicate that PVD nACo3 coatings significantly enhance wear resistance, extending tool life while maintaining acceptable surface quality. The optimal cutting time was determined to be approximately 90 min, after which a sharp increase in surface roughness and tool wear was observed. After 120 min of machining, substantial deterioration of surface quality parameters was recorded, suggesting increasing cutting forces and cutting edge degradation. SEM and EDS analyses revealed the presence of adhered material on the tool and sulfide inclusions in the microstructure of 316L stainless steel, which influenced the machining process. The nACo3 coating demonstrated high thermal and wear resistance, making it an effective solution for machining difficult-to-cut materials. This study suggests that selecting appropriate cutting parameters, tool geometry, protective coatings, and cooling strategies can significantly affect tool longevity and machining quality. The novelty of this research lies in the application of innovative nanocomposite PVD coatings during the milling of 316L stainless steel under air cooling conditions. These studies indicate potential future research directions, such as the use of minimum quantity lubrication (MQL) or cryogenic cooling as methods to reduce tool wear and improve post-machining surface quality. Full article

The majority of engineering structures are subjected to intricate loading scenarios or possess intricate geometries, resulting in a mixed-mode stress within the component. This study aims to investigate the fracture behavior of these components under mixed-mode loading conditions by examining the relationship among the fatigue stress ratio (R), loading angle, and geometry thicknesses in compact tension shear (CTS) specimens. Using advanced ANSYS simulation techniques, this research explores how these factors affect the fatigue life cycles of engineering materials. To simulate real-world loading scenarios and study various mixed-mode configurations, compact tension shear (CTS) specimens were subjected to three specific loading angles: 30°, 45°, and 60°. These angles were applied in combination with various stress ratios (0.1–0.5) to capture a wide range of loading conditions. This study employed ANSYS Workbench 19.2, featuring cutting-edge technologies such as separating, morphing, and adaptive remeshing (SMART), to precisely model crack growth, calculate fatigue life, and analyze stress distribution. A comparative analysis with experimental data revealed that the loading angle has a profound effect on both the trajectory of fatigue crack growth (FCG) and the number of fatigue life cycles. The results demonstrate that the loading angle significantly influences the trajectory of FCG and the number of fatigue life cycles. Specifically, a loading angle of 45 degrees resulted in the maximum principal and shear stresses, indicating a state of pure shear loading. The findings reveal critical insights into the interaction between stress ratios, geometry thicknesses, fatigue life cycles, and loading angles, enhancing the understanding of engineering components’ behavior under mixed-mode stress situations. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

Gate-All-Around (GAA) Nanosheet (NS) transistors have been identified as the device architecture for 3 nm and beyond as they provide additional scaling benefits. The Hot Carrier (HC) effect cannot be ignored in the development of metal oxide semiconductor field effect transistors (MOSFETs). In this article, we present a comprehensive review of Hot Carrier Degradation (HCD) studies on GAA NS transistors including geometry dependencies, surface orientation impacts, corner effects, characterization methodologies, process impacts and self-heating impacts from different researchers, together with the challenges and outlook, providing an insightful and valuable HCD reliability discussion and review on the cutting-edge technology in continuous MOSFET scaling. Full article

Sustainability has increasingly emphasized the importance of recycling and repairing materials. Cutting tools, such as milling cutters and drills, play a crucial role due to the high demands placed on products used in CNC machining. As a result, the repair and regrinding of these tools have become more essential. The geometric differences among machining tools determine their specific applications: twist drills have spiral flutes and pointed cutting edges designed for drilling, while end mills feature multiple sharp edges around the shank, making them suitable for milling. Taps and form cutters exhibit unique geometries and cutting-edge shapes, enabling the creation of complex profiles. However, measuring and classifying these tools for repair or regrinding is challenging due to their optical properties and coatings. This research investigates how lighting conditions affect the classification of tools for regrinding, addressing the shortage of skilled workers and the increasing need for automation. This paper compares different training strategies on two unique tool-specific datasets, each containing 36 distinct tools recorded under two lighting conditions—direct diffuse ring lighting and normal daylight. Furthermore, Grad-CAM heatmap analysis provides new insights into relevant classification features. Full article

Cutting-edge wear is inevitable in the cutting process of the knife. Studying the relationship between the performance of the knife and the cutting-edge wear is conducive to optimizing the design of the knife and increasing its service life. The micro-morphology of the cutting edge during the cutting process was systematically characterized by optical microscopy (OM) and scanning electron microscopy (SEM). The strength of the material matrix was characterized by nanoindentation, and the microstructure of the material was characterized by X-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). The influence of different microstructures and geometry parameters of the cutting edge on edge wear was investigated. The experimental results show that the cutting knife longevity decreases with the increase in the edge angle. When the edge angle is 19°, the durability of knives 1# and 2# is 747.5 mm and 826.8 mm; when the edge angle is 29°, the durability of knives 1# and 2# is 377.8 mm and 486.8 mm. Under the same edge angle, the durability of knife 2# is higher, mainly due to its higher hardness and the presence of more micro-scale M23C6 carbides and nano-scale MC carbides. The edge wear process can be divided into two stages. In the initial wear stage, the edge curling phenomenon occurs, which is the plastic deformation of the edge. In the stable wear stage, plowing and stacking of worn materials are observed, which is the abrasive wear process. Full article

With the rapid development of Internet of Things (IoT) technology, the number of devices connected to the network is exploding. How to improve the performance of edge devices has become an important challenge. Research on quality evaluation algorithms for brain tumor images remains scarce within symmetry edge intelligence systems. Additionally, the data volume in brain tumor datasets is frequently inadequate to support the training of neural network models. Most existing non-reference image quality assessment methods are based on natural statistical laws or construct a single-network model without considering visual perception characteristics, resulting in significant differences between the final evaluation results and subjective perception. To address these issues, we propose the AM-VGG-IQA (Attention Module Visual Geometry Group Image Quality Assessment) algorithm and extend the brain tumor MRI dataset. Visual saliency features with attention mechanism modules are integrated into AM-VGG-IQA. The integration of visual saliency features brings the evaluation outcomes of the model more in line with human perception. Meanwhile, the attention mechanism module cuts down on network parameters and expedites the training speed. For the brain tumor MRI dataset, our model achieves 85% accuracy, enabling it to effectively accomplish the task of evaluating brain tumor images in edge intelligence systems. Additionally, we carry out cross-dataset experiments. It is worth noting that, under varying training and testing ratios, the performance of AM-VGG-IQA remains relatively stable, which effectively demonstrates its remarkable robustness for edge applications. Full article

The cutting temperature in hard turning is extremely high, which reduces tool life, lowers machined-surface quality, and affects dimensional control. However, hard turning differs greatly from conventional turning in that the cutting process mainly happens at the tool-nose radius due to the extremely shallow depth of the cut. This paper provides a comprehensive and systematic analysis of this issue based on an evaluation of tool geometry in hard turning via finite element analysis (FEA) simulations and experiments. The effect of tool angles on cutting temperature in hard turning is analyzed. The impacts of cutting-edge angle, rake angle, inclination angle, and average local rake angle on the cutting temperature are investigated via central composite design (CCD). The simulated results and the empirically measured cutting temperature exhibit comparable patterns, with a minor 2% difference. Increasing the cutting-edge angle, negative rake angle and negative inclination angle enhances the local negative rake angles of the cutting-edge elements at the tool-nose radius involved in the cutting process. Notably, the most important component influencing cutting temperature in hard turning is the inclination angle, as opposed to normal turning, where the rake angle dominates the heat generation. Following this is the cutting-edge angle and the rake angle, which each contribute 40.75%, 32.39%, and 7.03%. These findings could enhance the application of the hard-turning technique by improving tool life and surface quality by focusing on optimizing the inclination angle. Full article

Virtual simulation of high-resolution multi-axis machining processes nowadays plays an important role in the production of complex parts in various industries. In order to improve the surface quality and productivity, process parameters, such as spindle speed, feedrate, and depth of cut, need to be optimized by using an accurate process model of milling, which requires both the fast virtual prototyping of machined part geometry for tool path verification and accurate determination of cutter–workpiece engagement for cutting force predictions. Under these circumstances, this paper presents an effective volumetric method that can accurately provide the required geometric information with high and stable computational efficiency under the condition of high grid resolution. The proposed method is built on a tri-level grid, which applies two levels of adaptive refinement in space decomposition to abolish the adverse effect of a large fine-level branching factor on its efficiency. Since hierarchical space decomposition is used, this multi-level representation enables the batch processing of affected voxels and minimal intersection calculations, achieving fast and accurate modeling results. To calculate the instantaneous engagement region, the immersion angles are obtained by fusing the intersection points between the bottom-level voxel edges and the cutter surface, which are then trimmed by feasible contact arcs determined using envelope theory. In a series of test cases, the proposed method shows higher efficiency than the tri-dexel model and stronger applicability in high-precision machining than the two-level grid. Full article

It is recommended to use high-speed milling to maintain an effective material removal rate and the required cutting-edge geometry. However, on the other hand, high speed increases wear, so the surface of the cutters is modified by deposition functional coatings. The wear of end mills made of CTS12D and H10F tungsten carbides during the high-speed processing of aluminum A97075 (B95T1) was compared. To increase the durability of the tools, well-proven technologies for deposition diamond-like and polycrystalline diamond coatings in microwave plasma with different film structures, which were determined by the coating growth conditions, were used. The milling cutter corner was mostly worn out, but the nature of the wear had its characteristics. It was revealed that at a forced cutting mode of about 1000 m/min, cutters made of CTS12D alloy with a nanocrystalline diamond coating with a “cauliflower” structure and with a diamond-like film showed 10% higher resistance. The primary wear mechanism was adhesive. Images of worn cutting edges were obtained using a 3D optical digital image processing system. Full article

The implementation of Machine Vision (MV) systems for Tool Condition Monitoring (TCM) plays a critical role in reducing the total cost of operation in manufacturing while expediting tool wear testing in research settings. However, conventional MV-TCM edge detection strategies process each image independently to infer edge positions, rendering them susceptible to inaccuracies when tool edges are compromised by material adhesion or chipping, resulting in imprecise wear measurements. In this study, an MV system is developed alongside an automated, feature-based image registration strategy to spatially align tool wear images, enabling a more consistent and accurate detection of tool edge position. The MV system was shown to be robust to the machining environment, versatile across both turning and milling machining centers and capable of reducing tool wear image capturing time up to 85% in reference to standard approaches. A comparison of feature detector-descriptor algorithms found SIFT, KAZE, and ORB to be the most suitable for MV-TCM registration, with KAZE presenting the highest accuracy and ORB being the most computationally efficient. The automated registration algorithm was shown to be efficient, performing registrations in 1.3 s on average and effective across a wide range of tool geometries and coating variations. The proposed tool reference line detection strategy, based on spatially aligned tool wear images, outperformed standard methods, resulting in average tool wear measurement errors of 2.5% and 4.5% in the turning and milling tests, respectively. Such a system allows machine tool operators to more efficiently capture cutting tool images while ensuring more reliable tool wear measurements. Full article