This study explores fatigue crack propagation in face-centered cubic metals using an adaptive concurrent multiscale framework that couples coarse-grained molecular dynamics with the finite element method. The approach dynamically refines the mesh and activates atomistic regions as the crack advances, enabling high-resolution modeling near the crack tip while preserving computational efficiency in less critical regions. A crack-free surface identification method is used to track the evolving crack tip. Coarse-graining of dislocation plasticity mitigates large deformations, ensuring stable crack growth during fatigue cycles. A refinement scheme activates virtual atoms when the crack tip approaches the FEM domain, with the continuum model imposing displacements. The framework is applied to simulate fatigue failure in single-crystal aluminum. The results demonstrate agreement with fully atomistic and non-adaptive multiscale models in terms of crack trajectory, stress intensity versus crack growth rate, and the Paris law exponent, while achieving up to a 46

Accurate tool-wear prediction is essential for improving machining efficiency, yet reliable estimation remains challenging due to the large volumes of high-frequency sensor data generated during machining. Such data require substantial storage and computational resources, and raw signals cannot be used directly. Instead, they must undergo preprocessing steps—such as denoising, segmentation, and filtering—to extract relevant features, which becomes increasingly time-consuming with multi-sensor streams. To address these challenges, this study integrates semantic-aware data compression with machine learning for efficient and robust tool-wear estimation. A Bayesian Regularized Artificial Neural Network (BRANN) is employed as the predictive model. The network is trained using open-access datasets and validated on an in-house dataset from milling experiments on Inconel 718, enabling assessment of cross-dataset generalization. For data reduction, the semantic-aware compression framework Shrink is applied to force signals using static (ϵb = 100,000) and dynamic (SNR = 25 dB) error bounds. Results show substantial reductions in data size; dynamic compression achieved an average compression ratio of 2.59×, reducing the dataset to 40.81% of its original size. Inference time decreased from 63.5 s to 29.5 s under dynamic settings. Importantly, compression did not degrade predictive performance. Instead, by mitigating noise and redundancy, it improved model accuracy, achieving a lowest average estimation error of 0.1240. These findings demonstrate that integrating semantic-aware compression into tool-condition monitoring systems enhances both computational efficiency and predictive reliability in machining applications.

Accurately predicting the Remaining Useful Life (RUL) of cutting tools is critical for enhancing productivity, reducing unexpected downtime, and minimizing manufacturing costs. As tool wear directly affects product quality, process stability and overall efficiency, reliable prediction methods are key to achieving smart and sustainable manufacturing. With recent advances in machine learning, data-driven approaches have proven highly effective in forecasting key machining responses such as tool wear, RUL and surface quality. This study leverages a Bayesian Neural Network (BNN) to predict the RUL of milling tools. Unlike conventional machine learning models, BNNs combine artificial neural networks (ANNs) with Bayesian regularization, enhancing model robustness and reliability. This is especially valuable in manufacturing environments where data can be limited, noisy or variable across operating conditions. The proposed model is trained on an open-access dataset and rigorously validated through in-house milling experiments conducted on Inconel 718 and SS 304 under varying machining parameters. The separation of training and testing data sources prevents overfitting to a specific dataset and enables the model to learn generalized patterns, ensuring its estimation, adaptability and effectiveness across diverse machines, tools and real-world manufacturing scenarios. The predictive model achieved flank wear prediction accuracies between 83% and 91% and RUL prediction accuracies between 80% and 93%, highlighting its reliability in estimating the progressive wear of the cutting tool. In addition to prediction accuracy, the Bayesian framework provides a natural quantification of model uncertainty, which is essential for decision-making in manufacturing environments, even though uncertainty metrics are not explicitly reported in this study. By forecasting the tool's remaining useful life through flank wear behavior, the model enables proactive tool replacement and maintenance scheduling, thereby avoiding failures and reducing idle time. These findings highlight the BNN model's effectiveness in both predictability and transferability, making it a promising solution for real-world tool life estimation in manufacturing applications.

High speed machining generates severe thermomechanical loads that impact cutting tool performance and lifetime, yet systematic optimization of coated tool geometry considering coupled stress and temperature effects remains limited. To address this gap, this study develops a computational framework for multi-objective optimization of AlCrN coated carbide cutting tools by focusing on three key geometric parameters, namely coating thickness, edge radius, and rake angle, to simultaneously minimize maximum principal stress and temperature. The methodology combines Arbitrary Lagrangian Eulerian finite element simulations with Gaussian Process Regression, using distinct kernel combinations for each target, with both models attaining test R-2 > 0.90, while reducing computational cost through sampling. NSGA-II optimization identified Pareto optimal solutions spanning from 13.7 MPa at 566.0 K to 592.8 MPa at 475.6 K, with all optimal designs converging to 1 mu m coating thickness. Consequently, the trade-off between stress and temperature is controlled by rake angle and edge radius, with positive rake angles lowering stress and negative rake angles lowering temperature. The knee point solution balances competing objectives at 342.7 MPa and 494.3 K. While the framework is limited to computational analysis, the approach provides a quantitative basis for guiding tool geometry selection within the modeled machining conditions.

Grain boundaries critically influence the mechanical and tribological behavior of polycrystalline solids, especially in friction and wear applications. This study employs a mechanism-based gradient crystal plasticity model to examine how grain boundary morphology affects the scratch response of bi-crystalline copper. Simulations reveal that the trace angle—the in-plane grain boundary orientation—significantly impacts local scratch behavior, producing two distinct scratch depth variation patterns near the boundary. Meanwhile, the inclination angle—the grain boundary tilt relative to the surface normal—has minimal effect on overall scratch depth trends but alters the extent and location of these variations. These effects are explained through analyses of scratch force evolution, pile-up topography, and contact condition changes during scratching across the grain boundary.

Understanding the contact mechanics of thin elastic shells is essential for a wide range of applications, from structural systems to soft robotics. This study focuses on how interfacial adhesion and friction influence the mechanical response of spherical shells in contact with a rigid plane, with particular attention to the suppression of buckling instabilities (or more precisely, snap-back instabilities, or snap-buckling). A comprehensive mapping of the transition between buckling and non-buckling regimes is carried out using the Finite Element Method. Numerical results are further validated through experiments, which demonstrate that for thin shells in contact with smooth surfaces-where interfacial adhesion and friction are high-buckling can be entirely suppressed, resulting in a more stable contact configuration.

Fatigue is one of the most destructive processes leading to the failure of mechanical components under cyclic loading. Traditionally, continuum methods have been used to predict and simulate fatigue, but they struggle to simultaneously capture crack nucleation and propagation under large deformations, as well as the physics of crack closure under compression. Recently, Molecular Dynamics (MD) has shown promising results in fracture mechanics; however, its application is limited by the scale of the models, making it more suitable for studying crack nucleation rather than full crack propagation. In this work, we used a coarse-grained molecular dynamics approach to model crack nucleation, propagation, and closure under cyclic loading in FCC metals. By coarse-graining the dislocation plasticity, this approach enables the simulation of crack nucleation and propagation within an atomistic framework while significantly reducing computational costs. A crack-free surface identification method was also implemented to trace crack surfaces and prevent crack closure during unloading. The method is applied to simulate fatigue crack processes in single-crystal aluminum, as well as cases with pre-existing grain boundary and bi-crystal. It is also extended to large-scale polycrystalline aluminum samples. The crack trajectory and the Paris law exponent were examined, demonstrating good agreement with experimental data. Overall, the proposed method, combined with the crack-free surface identification technique, provides a robust numerical approach for simulating fatigue behavior in metallic materials with reasonable computational efficiency.

In recent years, hard coatings have gained various industrial applications due to their excellent properties. This research describes the effects of adding B, C and both elements on the microstructure, mechanical and tribological properties of TiN film synthesized by PACVD. The results indicate that incorporating boron and carbon into TiN results in the creation of a nanocomposite microstructure, where amorphous phases surround nano-crystals of TiN, h-BN, and TiB2. The existence of carbon in the TiCN and TiBCN coatings manifests as DLC and amorphous carbon. The use of BBr3 and CH4 for depositing TiBCN coatings reduces the intensity of plasma formed surrounding the samples and increases the etching rate, resulting in a thickness reduction from 2.16 to 0.28 mu m. The introduction of boron and carbon into the TiN coating enhances its hardness from 16.6 to 30 GPa, attributed to the formation of nanocomposite microstructures. TiBCN coating exhibits an indentation toughness value of 1.52 +/- 0.2 MPa.m0.5, the highest among the deposited coatings. The addition of carbon to both TiN and TiBN coatings reduces their coefficients of friction, decreasing from approximately 0.55 and 0.4, respectively, to about 0.3. This reduction is attributed to the creation of lubricating compounds like amorphous carbon and DLC.

Improving the material removal rate (MRR) can significantly enhance the efficiency of the milling operations during machining. However, increasing MRR develops a larger degree of stress and eventual wear at the cutting edge, reducing the tool’s lifetime, in particular for hard metals like stainless steel. Therefore, it is important to optimize the tool geometry to enhance the stress-carrying capacity under extreme cutting conditions. Considering a four-fluted tungsten carbide milling tool for cutting stainless steel, we propose in this study a procedure for reducing tool stresses by modifying the tool geometry. Using a systematic set of finite element simulations, we showed that the degree of stresses on the cutting edge can be reduced by optimizing three geometrical parameters, i.e., helix angle, rake angle, and cutting edge radius. To validate the simulation results, we manufactured 18 four-fluted milling tools with varying geometries and tested them by milling stainless steel 316 L under identical cutting conditions. The performance of each tool was ranked based on microscopic inspections of their cutting edges, showing a close agreement with the numerical simulation predictions. This study presents a procedure for modifying milling tool geometry to enhance performance under extreme machining conditions.

In tribological systems, hard particles trapped within the contact zone can induce substantial wear, adversely affecting system performance and durability. This type of wear, known as three-body wear, arises when these particles abrade the sliding surfaces, with their size and shape playing a pivotal role in the wear mechanisms. Although round particles are frequently employed in theoretical and numerical analyses due to their computational simplicity, this assumption may not accurately capture the full complexity of three-body wear dynamics. In this study, we critically examine the effect of particle roundness on three body wear between metals and polymers by systematically varying particle size, load, and sliding speed. Our findings show that particle roundness dominants rolling over sliding, irrespective of boundary conditions or particle dimensions, making round particle an unrealistic representation of real-world wear scenarios. This is explained as the roundness prevents particles from embedding deeply into the polymer, instead causing surface deformation. Numerical simulations confirm that sliding behavior occurs only under extremely low-friction conditions, which are not typical in practical applications. These results highlight the necessity of incorporating angular and irregularly shaped particles to accurately model the mechanics of three-body wear and its impact on tribological systems.

Tool wear is a critical factor in machining processes, particularly when dealing with difficult-to-machine materials like Inconel 718, which are susceptible to rapid tool degradation due to their inherent toughness and low thermal conductivity. Traditional methods of predicting tool wear, which rely on empirical models or extensive sensor data, often suffer from limitations inaccuracy and adaptability. To overcome these challenges, we propose a novel approach utilizing a ResNet50-based model that leverages high-resolution images of worn tools to directly predict wear levels. The study involved experiments using 35 different cutting tools, each coated with various materials, and subjected to varying combinations of cutting speeds and feeds. A comprehensive dataset of images from all the tools was collected and used to train the model. The ResNet50 algorithm, renowned for its deep residual learning capabilities, was employed to automatically extract relevant features from the images and establish a predictive relationship with actual tool wear measurements. The performance of the ResNet50 model was validated against other state-of-the-art deep learning approaches, demonstrating superior accuracy in predicting tool wear under different cutting conditions. The proposed model consistently provided tool wear predictions that closely matched experimental measurements and generalized effectively across different cutting tools and edges, showcasing both accuracy and robustness. This approach not only enhances predictive accuracy but also offers a scalable solution for real-time tool condition monitoring in industrial settings.

High-speed machining is a practical way to attain high productivity with lower costs. Under this condition, the tool geometry needs to be optimized to sustain high cutting forces and temperatures. The sharpness of the cutting edge and the coating thickness (CT) are two key parameters that affect the tool’s performance. While a sharp edge eases the cutting process, it causes a high stress concentration, which increases the wear rate and eventual edge fracture. In this study, we use a combination of finite element simulations and experimental testing to evaluate the effects of CT ( 1–3 μm), edge radius ( r_β , 6–15 μm), and coefficient of friction ( = 0 - 0.2 ) on the stress distribution at the cutting edge. Our simulations showed that the larger the CT, the higher the stress magnitude inside the coating, but the lower the maximum stress depth percentile. Considering an industrial case of cutting steel workpieces using AlCrN-coated tungsten carbide tools under given cutting parameters, our simulations suggested an optimum CT of 3 μm. By manufacturing a series of milling tools with different CTs and edge radii, we validated the simulation results using a set of well-controlled milling experiments. Finally, the edge radius should be selected considering the size of rake/flank angle mainly to control stress distribution over the cutting edge.

The prediction of tool wear helps minimize costs and enhance product quality in manufacturing. While existing data-driven models using machine learning and deep learning have contributed to the accurate prediction of tool wear, they often lack generality and require substantial training data for high accuracy. In this paper, we propose a new data-driven model that uses Bayesian Regularized Artificial Neural Networks (BRANNs) to precisely predict milling tool wear. BRANNs combine the strengths and leverage the benefits of artificial neural networks (ANNs) and Bayesian regularization, whereby ANNs learn complex patterns and Bayesian regularization handles uncertainty and prevents overfitting, resulting in a more generalized model. We treat both process parameters and monitoring sensor signals as BRANN input parameters. We conducted an extensive experimental study featuring four different experimental data sets, including the NASA Ames milling dataset, the 2010 PHM Data Challenge dataset, the NUAA Ideahouse tool wear dataset, and an in-house performed end-milling of the Ti6Al4V dataset. We inspect the impact of input features, training data size, hidden units, training algorithms, and transfer functions on the performance of the proposed BRANN model and demonstrate that it outperforms existing state-of-the-art models in terms of accuracy and reliability.

This study investigates the impact of Ultrasonic Peening Treatment (UPT) at room temperature (T room ) and cryogenic temperature (T cryo ) on phase transformation, surface morphology, and fatigue life of stainless steel 304L samples. Finite element simulations were developed to analyze the effects of different material models and the pin's vertical velocity on residual stress, martensite volume fraction (xi), and surface deformation. The numerical results of residual stress and xi were consistent with experimental measurements obtained via X-ray diffraction. Significant compressive residual stress and martensite volume fraction were induced in the subsurface of the specimens following UPT at both temperatures. Various UPT process parameters, including static load, the pin's horizontal velocity, the number of treatments, and lubrication conditions, were experimentally explored for their effects on surface morphology and hardness. Indentation hardness revealed values of 621 HV for samples treated at T- cryo and 489 HV for those treated at T room , compared to 286 HV for untreated specimens. Furthermore, UPT reduced surface roughness by approximately 88 % for a mechanically polished surface and over 93 % for a rough surface. Investigating conditions leading to potential damage and defects during the process was also undertaken. Fractography of the fracture surfaces and fatigue analysis revealed that the fatigue life of UPT-treated samples at T-room and T-cryo increased by 63 % and 45 %, respectively, compared to untreated specimens.

Polymers have attracted attention for their use in enabling biodegradable electronics. However, many polymers suitable as substrates either have no adhesion or suffer from weak and unstable adhesion. Addressing this challenge, we report a simple method to achieve tunable adhesion on various surfaces for wide applications. We achieve this by combining poly(vinyl alcohol) (P), dopamine (DA) and citric acid (CA) to produce modified poly(vinyl alcohol) adhesive films. These films are derived from bio-based constituents through an environmentally benign, easily reproducible and scalable fabrication process. They offer strong adhesion to various surfaces, such as stainless steel (138-191 kPa) and Polytetrafluoroethylene (PTFE) (67-93 kPa) and facilitate easy detachment with water. Notably, the modified films showed a better degradation compared to pristine P films under anaerobic conditions. The extent of degradation was characterized both quantitatively and qualitatively. The biokinetic parameters of anaerobic digestion process were estimated using three different kinetic models. It is anticipated that DA and CA molecules penetrate the interplanar distance of P chains as supported by powder X-ray diffraction (XRD) studies, thus, accelerating the degradation process. Additionally, the inclusion of CA enhanced the stability of DA molecules against oxidation, increased the extent of H-bonding and acted as a plasticizer. The addition of DA and CA bestowed the films with self-healing property due to the presence of multiple H-bonds. Tensile experiments revealed that the strength of self-healed samples approached that of pristine samples. The findings of this study hold promise for the development of innovative, biodegradable poly (vinyl alcohol)-based self-healing adhesive films with potential applications across various domains like smart packaging, soft robotics, on-skin electronic tattoos and self-healing electronics.

The anisotropy in the elastoplastic response of crystalline solids influences substantially their scratch resistance at the microscale. While grain boundaries typically act as obstacles to the motion of dislocations at the grain level, the scratch behavior in the vicinity of the grain boundary is influenced by other factors such as crystallographic orientation and pile-up topography. We performed a systematic set of nano-scratch simulations using the mechanism-based strain gradient crystal plasticity to study the evolution of scratch force and depth near grain boundaries. To validate our model, we conducted a nano-scratch test on a polycrystalline copper sample and compared the scratch depth profile for a specific grain boundary with the simulated result, where a good qualitative agreement was achieved. Our simulations showed that the scratch depth, force, and hardness vary between the corresponding values for the individual constitutive grains, and the variation decreases by reducing the grain size. Additionally, we analyzed the scratch response in terms of the pile-up topography and subsurface stresses and distinguished different regimes when the indenter approaches and scratches across the boundary. Our findings offer a new direction to optimize the scratch resistance of polycrystalline metals by tailoring the size and orientation of grains near the surface. Additionally, it introduces scratching as a robust material characterization method.

The classical Hertzian contact model establishes a monotonic correlation between contact force and area. Here, we showed that the interplay between local friction and structural instability can deliberately lead to unconventional contact behavior when a soft elastic shell comes into contact with a flat surface. The deviation from Hertzian solution first arises from bending within the contact area, followed by the second transition induced by buckling, resulting in a notable decrease in the contact area despite increased contact force. Additionally, our results invalidated a previous claim of a linear relation between friction and dissipated energy, demonstrating the suppression of both buckling and dissipation at high friction levels. Different contact regimes are discussed in terms of rolling and sliding mechanisms, providing insights for tailoring contact behaviors in soft shells.

The material removal process takes place due to phenomena such as plastic deformation and brittle fracture. A long continuous chip is formed when the plastic deformation dominates, whereas a fracture-induced discontinuous chip is formed when the brittle fracture dominates. The means of material removal changes at a certain cutting depth for a particular material, the so-called transition depth of cut (TDoC). This article aims to predict the TDoC while including the effect of friction between the tool and workpiece. We propose a modification to a recently developed model (Aghababaei et al., 2021, "Cutting Depth Dictates the Transition From Continuous to Segmented Chip Formation," Phy. Rev. Lett., 127(23), pp. 235502) to incorporate the effect of friction. The model predicts a transitional depth of cut as a function of tool geometry, material properties, and friction. The model is supported by performing orthogonal cutting experiments on different polymers such as polymethyl methacrylate (PMMA), polyoxymethylene (POM), and polycarbonate (PC). The model is also compared with existing models in the literature, where an improvement in the prediction of TDoC is shown. Moreover, the effect of the friction coefficient and rake angle on the TDoC is discussed. The results show that transitional cutting depth is reduced by increasing the friction coefficient. Alternatively, the TDoC reaches its maximum at an optimum rake angle, which is a function of the specific material being cut. The model aids in accurately predicting the TDoC, a crucial factor for optimizing various material removal processes.