Deformation Of Materials Research Articles

Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning

In the continuously advancing field of mechanical engineering, digitalization is bringing a major transformation, specifically with the concept of digital twins. Digital twins are dynamic digital models of real-world systems and processes, crucial for Industry 4.0 and the emerging Industry 5.0, which are changing how humans and machines work together in manufacturing. This paper explores the combination of physics-based and data-driven modeling using advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This approach provides a comprehensive understanding of mechanical systems, improving materials design and manufacturing processes. The focus is on the advanced 42SiCr alloy, where AI-driven digital twinning is used to optimize cooling rates during Quenching and Partitioning (Q-P) treatments. This leads to significant improvements in the mechanical properties of 42SiCr steel. Given its complex properties influenced by various factors, this alloy is perfect for digital twinning. The Q-P heat treatment process not only restores the material's deformability but also gives it advanced high-strength steel (AHSS) properties. The findings show how AI and ML can effectively guide the development of high-strength steels and enhance their treatment processes. Additionally, integrating digital twins with new technologies like the Metaverse offers exciting possibilities for simulated production, remote monitoring, and collaborative design. By establishing a clear workflow from physical to digital twins and presenting empirical results, this paper connects theoretical modeling with practical applications, paving the way for smarter manufacturing solutions in mechanical engineering. Furthermore, this paper analyzes how digital twins can be integrated into advanced technologies like the Metaverse, opening up new possibilities for simulated production, remote monitoring, design collaboration, training simulations, analytics, and complete supply chain visibility. This integration is a crucial step toward realizing the full potential of digitalization in mechanical engineering.

Understanding mechanical stresses upon solid-state battery electrode cycling using discrete element method

Solid-state batteries (SSBs) often fall behind conventional lithium-ion batteries (LIBs) in performance. Electrochemical cycling protocols, in particular under isostatic compression, present ample opportunities for improvement to mitigate issues like contact loss and aging among numerous other challenges. This study introduces a novel Discrete Element Method (DEM) workflow to assess the effectiveness of uniaxial and isostatic compression as proceeding electrode preparation on the conductivity and structural integrity of SSBs. Isostatic compression achieves conductivity levels comparable to uniaxial compression with significantly reduced pressure requirements. In contrast, uniaxial compression at elevated loads, while resulting in higher conductivity, subjects the particles to significant stress, increasing the risk of cracking and deformation of the SSB materials. This study shows the impact of mechanical stress during different stages of electrochemical cycling on the microstructure's integrity to provide valuable insights into electrochemistry-mechanics couplings, which can be challenging to evaluate experimentally.

Finite Element Analysis of Plastic Deformation in Ultrasonic Vibration Superimposed Face Milling of Steel X46Cr13.

Ultrasonic vibration superimposed face milling enables the generation of predefined surface microstructures by an appropriate setting of the process parameters. The geometrical reproducibility of the surface characteristics depends strongly on the plastic material deformation. Thus, the precise prediction of the emerging surface microstructures using kinematic simulation models is limited, because they ignore the influence of material flow. Consequently, the effects of plastic as well as elastic deformation are investigated in depth by finite element analysis. Microstructured surfaces resulting from these numerical models are characterized quantitatively by areal surface parameters and compared to those from a kinematical simulation and a real machined surface. A high degree of conformity between the values of the simulated surfaces and the measured values is achieved, particularly with regard to material distribution. Deficits in predictability exist primarily due to deviations in plastic deformation. Future research can address this, either by implementing a temperature consideration or adapting specific modeling aspects like an adjusted depth of cut or experimental validated material parameters.

Finite element analysis of reconstruction plates in mandibular defects

Segmental defects in the mandibular bone can occur after local trauma, infection, or surgical resection. Several reconstructive possibilities are currently available, and temporary stabilization of bone segments with reconstruction plates is commonly used when immediate bone reconstruction is not possible. However, little attention has been paid to the biomechanical aspects involved in this approach, and studies conducted with biomodels face the difficulty of simulating the muscular forces involved. The use of Finite Element Analysis allows for the mathematical evaluation of structures in a controlled environment, applying forces at any point and/or direction, assessing material deformation, as well as the tension applied to them. Thus, the aim of this study was to evaluate reconstruction plates used on segmental defects through mathematical analysis using finite elements. After simulation, lower stress values were found on thicker plates, suggesting greater resistance to fracture. Conversely, significantly increased stress values on the screws were present in thicker plates.

In-situ synchrotron diffraction analysis of deformation mechanisms in an AA5083 sheet metal processed by modified equal-channel angular pressing

Severe plastic deformation (SPD) is a process to significantly improve the mechanical properties of materials by grain refinement. For forming at room temperature (RT), higher strengths can thus be achieved; for increased temperatures (HT), greater elongations can be achieved. This great potential has been chiefly applied only on laboratory scale due to a lack of industrial process design. Equal-channel angular pressing (ECAP) offers the possibility to integrate these advantages in industrial processes. This paper investigates a modified ECAP process for aluminum AA5083 sheet metal. Two passes of ECAP for sheet metal and an additional heat treatment were used. To study the material deformation on a micro-scale, in-situ synchrotron measurements at DESY (Hamburg) were performed to analyze the evolution of lattice strains and dislocation densities during tensile testing. The mechanical properties of the ECAP-processed sheet material reveal an increase in yield strength and a decrease in elongation at RT. Higher strains can be measured for the processed material at elevated test temperatures. In-situ diffraction results show a changed behavior of the ECAP sheet material compared to the reference material for both temperature regimes. These changes are based on the increased defect density in the material, which leads to increased strength at RT. At HT, dynamic recrystallization occurs during tensile testing, and the prevailing deformation mechanisms reconfigure. Electron backscatter diffraction (EBSD) and micrographs are used to interpret and supplement the observations. The findings provide the basis for the in-depth understanding of the deformation behavior of the material and help to apply the ECAP process on an industrial scale.

Application of mesh-free and finite element methods in modelling nano-scale material removal from copper substrates: A computational approach

This study explores the modelling methodology using mesh-free smoothed particle hydrodynamics (SPH) and finite element modelling (FE) techniques to simulate the AFM-based nano-scratching processes for advancing precision engineering in nanotechnology. Tip wear in nano machining substantially increases the tip radius, thereby influencing the material removal mechanism and subsequently affecting the quality of machined nanostructures. In this context, this study examines the effects of rake angle (the inclination of the main cutting edge to the plane perpendicular to the scratched surface), tip radius and scratching depth on cutting forces, groove dimensions, and deformed thickness. This was achieved by implementing an in-house SPH method based particle code employing a Lagrangian algorithm, and an FE model incorporating the dynamic explicit algorithm implemented (in ABAQUS) to carry out nano-scratching simulations. The investigation revealed that the cutting mechanism transitioned to ploughing when the scratching depth decreased to 30% of the tip radius for OFHC-Cu workpiece material machined with a diamond tip. The dominance of normal forces over cutting forces during scratching indicated the side flow of material in the vicinity of the tip radius under intense contact pressure. The ploughing mechanism exhibited more sensitivity at a higher negative rake angle of 60°. Increased scratching depth and tip radius led to more significant material deformation owing to the induction of higher cutting forces, with the maximum deformation thickness 3.6 times the tip radius. The simulated results demonstrated favourable concordance with the experimental data.

Amino acids inhibit the photodynamic inactivation effect by hindering cellular oxidative stress

Photodynamic inactivation (PDI) could utilize light to activate reactive oxygen species (ROS) produced by photosensitizers to kill bacteria for preservation. To delve into the complex effects arising during the post-harvest PDI processing, we conducted experiments using Pseudomonas reinekei, a food spoilage bacteria extracted from rotten Pakchoi. Through analyzing the metabolomics results, we discovered that methionine (Met) and glutamate (Glu) exhibited significant inhibitory effects during the PDI process. The oxidative stress generated by light treatment resulted in a reduction of 30.31% and 36.37% in the levels of Met and Glu, respectively. The data also showed that exogenous Met and Glu reduced intracellular oxidative stress levels, increased peroxidase activity, and prevented the damage of intracellular material and cell membrane deformation. That amino acids could inhibit the effect of PDI by hindering oxidative stress. Therefore, the amino acid content should be considered when applying PDI to treat Met- or Glu-rich foods.

Development of a plugging material featuring external flexibility and internal rigidity

Recognizing the challenges of low strength, inadequate cementation, significant pore development, and excellent connectivity of reef limestone, as well as the persistent issues of ‘re-leakage’ and increasing leaks when using rigid bridge plugs, this paper posits the use of materials possessing both flexible and rigid properties for the prevention of leaks and plugging in reef limestone formations. Consequently, a plugging material exhibiting external flexibility and internal rigidity is developed (EFIR Material). This synergistic approach employs the deformability of flexible materials and the skeletal support of rigid materials to create a high-strength, dense plugging section. The new design enhances the formation’s pressure-bearing capacity while effectively addressing leakage issues. The material’s functional structure design, a core-shell structure of EFIR, is achieved by reverse-phase suspension polymerization of acrylamide (AM), 2-acrylamide-2-methylpropanesulfonic acid (AMPS), and alkyl ketone polymer (NVP), water-soluble monomers functioning as the shell and modified sand as the core. The results indicate that EFIR retains significant flexibility and strength upon water absorption and expansion. A sand bed made from 10 to 18 mesh calcareous sand exhibits excellent sealing properties. The optimal dosage of 3%-4% produces robust sealing effects, withstands pressures up to 7 MPa, and results in a tightly cemented, high-strength sand bed post-sealing with reef tuff, displaying high-quality sealing that is resistant to re-leakage.

Effect of Al Content on Microstructure and Properties of AlxCr0.2NbTiV Refractory High-Entropy Alloys.

High-temperature creep refers to the slow and continuous plastic deformation of materials under the effects of high temperatures and mechanical stress over extended periods, which can lead to the degradation or even failure of the components' functionality. AlxCr0.2NbTiV (x = 0.2, 0.5, or 0.8) refractory high-entropy alloys were fabricated by arc melting. The effects of Al content on the microstructure of AlxCr0.2NbTiV alloys were studied using X-ray diffraction, scanning electron microscopy, and electron backscatter diffraction. The microhardness, compression properties, and nanoindentation creep properties of AlxCr0.2NbTiV alloys were also tested. The results show that the AlxCr0.2NbTiV series exhibits a BCC single-phase structure. As the Al content increases, the lattice constant of the alloys gradually decreases, and the intensity of the (110) crystal plane diffraction peak increases. Adding aluminum enhances the effect of solution strengthening; however, due to grain coarsening, the microhardness and room temperature compressive strength of the alloy are only slightly improved. Additionally, because the effect of solution strengthening is diminished at high temperatures, the compressive strength of the alloy at 1000 °C is significantly reduced. The creep mechanism of the alloys is predominantly governed by dislocation creep. Moreover, increasing the Al content helps to reduce the sensitivity of the alloy to the loading rate during the creep process. At a loading rate of 2.5 mN/s, the Al0.8Cr0.2NbTiV alloy exhibits the lowest creep strain rate sensitivity index (m), which is 0.0758.

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

BackgroundThere are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce.ObjectiveAdditive manufacturing capabilities offer consistent patterns using programmable fabrication methods.MethodsIn this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature.ResultsThe results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges.ConclusionOur results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

A meshless computational framework for studying cold spray additive manufacturing including large numbers of powder particles with diverse characteristics

Cold spray (CS) has emerged as an appealing additive manufacturing (AM) technique for producing or repairing individual components or entire structures. Compared to fusion-based AM technologies, cold spray additive manufacturing (CSAM) offers distinct advantages in the fabrication of components, while avoiding some melting/solidification-related issues such as phase transformation and oxidation. It involves intricate processes that pose significant challenges for numerical modeling, particularly when simulating the entire process at a large scale. The smoothed particle hydrodynamics (SPH) method is highly suitable for handling large material deformations due to its Lagrangian and meshless nature. In this work, we develop an enhanced SPH method to conduct large-scale simulations of CSAM with different powder sizes, morphologies, and distributions. A modified material model has been incorporated to accurately capture the strain-rate hardening effects during the plastic stage. The computational scale is greatly improved by using a Message Passing Interface (MPI) based framework, enabling the simulation of approximately ten million SPH particles. To the authors’ knowledge, this study marks the first attempt to numerically reproduce the entire process of CSAM with real powder sizes and distributions. Experimental data measured for a wide range of powder velocities are used to validate the simulation results and assess the prediction accuracy. Subsequently, we comparatively study the bonding mechanisms of powders with the same or different sizes, while also identifying a four-stage coating process. The effects of powder morphology on the bonding process are thoroughly investigated. A large-scale CSAM process is finally reproduced to demonstrate the capability of the present meshless scheme, and mechanisms of pore formation are analyzed, providing valuable insights for practical engineering applications.

Effect of crystal orientation on the scratching behavior of γ-TiAl alloy nanowires by molecular dynamics simulation

In micro-and-nanomachining, crystal orientation effects significantly influence the plastic deformation, mechanical response, and temperature profile of nanowire materials. This study utilizes molecular dynamics simulation to investigate the process of surface scratching on γ-TiAl alloy nanowires under six different crystal orientations. The results reveal that the evolution of internal microscopic defects during the surface scratching process is heavily affected by crystal orientation, resulting in distinct dislocation motion-induced shear deformation and extrusion removal by the tool. These factors determine variations in both the shape of the nanowire's surface pile-up and the overall degree of workpiece deformation. Among all cases examined, (001)[1‾1‾0] crystal orientation exhibits easier removal of atoms with minimal internal defects, while (110)[11‾0] crystal orientation shows maximum atom removal without significant overall deformation, both cases demonstrate superior surface morphology and subsurface quality. Furthermore, stress and temperature profiles within abraded atoms and inside the workpiece depend on crystal orientation. These findings provide valuable insights for characterizing surface damage in γ-TiAl alloy nanowires as well as facilitating the preparation of surface nano-channels.

Mechanical heterogeneity in a soft biomaterial niche controls BMP2 signaling

The extracellular matrix is known to impact cell function during regeneration by modulating growth factor signaling. However, how the mechanical properties and structure of biomaterials can be used to optimize the cellular response to growth factors is widely neglected. Here, we engineered a macroporous biomaterial to study cellular signaling in environments that mimic the mechanical stiffness but also the mechanical heterogeneity of native extracellular matrix. We found that the mechanical interaction of cells with the heterogeneous and non-linear deformation properties of soft matrices (E < 5 kPa) enhances BMP-2 growth factor signaling with high relevance for tissue regeneration. In contrast, this effect is absent in homogeneous hydrogels that are often used to study cell responses to mechanical cues. Live cell imaging and in silico finite element modeling further revealed that a subpopulation of highly active, fast migrating cells is responsible for most of the material deformation, while a second, less active population experiences this deformation as an extrinsic mechanical stimulation. At an overall low cell density, the active cell population dominates the process, suggesting that it plays a particularly important role in early tissue healing scenarios where cells invade tissue defects or implanted biomaterials. Taken together, our findings demonstrate that the mechanical heterogeneity of the natural extracellular matrix environment plays an important role in triggering regeneration by endogenously acting growth factors. This suggests the inclusion of such mechanical complexity as a design parameter in future biomaterials, in addition to established parameters such as mechanical stiffness and stress relaxation.

Deformation of Cylindrical Shell Made of 9X2 Steel Under Complex Loading

The development of the construction industry in terms of the design and manufacture of shell structures of non-standard architectural forms made of materials with complex mechanical properties requires using modern integrated computer-aided design systems with step-by-step modeling of deformation of structural elements under operating conditions, as well as taking into account their subsequent behavior after accumulation of residual strains during plastic deformation. The purpose of the study is to simulate the process of plastic deformation of a thin-walled cylindrical shell made of 9X2 GOST 5950-2000 (Interstate Standard) steel under compression and torsion with theoretical calculations based on the general theory of elastoplastic processes by A.A. Ilyushin. The constitutive equations of the theory of elastoplastic processes by A.A. Ilyushin for complex loading path and deformation of materials in the deviatoric strain space are presented. Based on the presented solutions, according to the strain path of the 9X2 steel shell implemented in the model, the graphs showing the relation of the vector and scalar properties of the material to the arc length of the strain path are constructed. A conclusion is made about the degree of hardening of the material in question and its dependence on the magnitude of the angle of convergence at the kink point of the complex path. The graphs of changes in the constitutive plasticity functions with respect to the increments of the arc length of the strain path are presented.

Wide-range, durable, and adaptable miniature pressure sensor based on planar capacitance

Capacitive pressure sensor (CPS) is widely used in the field of industrial equipment, because of the merits of fast dynamic response and high resolution. However, the traditional laminated CPS makes it difficult to achieve a wide detection limit in a small size, and this structure is susceptible to electromagnetic interference. Here we developed a miniature planar capacitive pressure sensor (MPCPS) with high performance, which can realize the response to external touching stimuli through the deformation of the packaging material and the change of the equivalent resistance. A metal shielding layer was added under the insulating substrate to effectively isolate the external interference. The thickness of the sensor is about 200 μm, and the diameter of the core sensing area is less than 1 mm. Two types of electrodes with different shapes were designed, among which the spiral electrode MPCPS (S-MPCPS) has better performance than the linear electrode MPCPS. The S-MPCPS has a sensitivity of 99.2% MPa−1 in the low-pressure range (0–0.1 MPa), fast response (20 ms), wide detection limit (>1 MPa), and high durability (>2000 cycles). In addition, MPCPS is proven to have good resistance to high temperature and oil contamination. Finally, practical applications such as contact pressure measuring on the meshing surface of spur gears and mechanical gripper clamping force monitoring were successfully demonstrated. These results shed light on the potential application of the MPCPS in the pressure detection of industrial equipment.

Mechanical Behaviour of Photopolymer Cell-Size Graded Triply Periodic Minimal Surface Structures at Different Deformation Rates.

This study investigates how varying cell size affects the mechanical behaviour of photopolymer Triply Periodic Minimal Surfaces (TPMS) under different deformation rates. Diamond, Gyroid, and Primitive TPMS structures with spatially graded cell sizes were tested. Quasi-static experiments measured boundary forces, representing material behaviour, inertia, and deformation mechanisms. Separate studies explored the base material's behaviour and its response to strain rate, revealing a strength increase with rising strain rate. Ten compression tests identified a critical strain rate of 0.7 s-1 for "Grey Pro" material, indicating a shift in failure susceptibility. X-ray tomography, camera recording, and image correlation techniques observed cell connectivity and non-uniform deformation in TPMS structures. Regions exceeding the critical rate fractured earlier. In Primitive structures, stiffness differences caused collapse after densification of smaller cells at lower rates. The study found increasing collapse initiation stress, plateau stress, densification strain, and specific energy absorption with higher deformation rates below the critical rate for all TPMS structures. However, cell-size graded Primitive structures showed a significant reduction in plateau and specific energy absorption at a 500 mm/min rate.

A novel method based on deep learning algorithms for material deformation rate detection

AbstractGiven the significant influence of microstructural characteristics on a material’s mechanical, physical, and chemical properties, this study posits that the deformation rate of structural steel S235-JR can be precisely determined by analyzing changes in its microstructure. Utilizing advanced artificial intelligence techniques, microstructure images of S235-JR were systematically analyzed to establish a correlation with the material’s lifespan. The steel was categorized into five classes and subjected to varying deformation rates through laboratory tensile tests. Post-deformation, the specimens underwent metallographic procedures to obtain microstructure images via an light optical microscope (LOM). A dataset comprising 10000 images was introduced and validated using K-Fold cross-validation. This research utilized deep learning (DL) architectures ResNet50, ResNet101, ResNet152, VGG16, and VGG19 through transfer learning to train and classify images containing deformation information. The effectiveness of these models was meticulously compared using a suite of metrics including Accuracy, F1-score, Recall, and Precision to determine their classification success. The classification accuracy was compared across the test data, with ResNet50 achieving the highest accuracy of 98.45%. This study contributes a five-class dataset of labeled images to the literature, offering a new resource for future research in material science and engineering.

Numerical modeling of RC column reinforced by new strategy using fiberglass tape and cloth

This paper presents the results of finite element analysis (FE) to study the axial compression behavior of reinforced concrete columns wrapped with tapes and fiberglass cloths according to different techniques using ABAQUS software. In the beginning, columns are modeled as 3D solid elements with a concrete damage plasticity model (CDPM), as the columns in this study are considered to be low compressive strength reinforced concrete and the rebar as 3D mesh elements. And once assembled, the specimen is wrapped with tape and cloth in the form of three-dimensional shell elements. Then, in order to analyze the behavior of the ultimate loads, the deformation of the confinement material, and the distribution of deformations in the concrete, the controlled displacement method used to apply the uniaxial compression loads to the specimen. Finally, a new mixed confinement model between partial confinement and complete has been proposed using a database of the results of a numerical simulation for confined concrete by fiberglass tapes and cloths. And it was found that the confinement strategy proposed in this study is very effective for the behavior of the reinforced concrete column, as the stress decreases significantly with the expansion and widening of the confinement stiffness of concrete in the plastic phase which improves the axial stress resistance.

Improved shape-from-template method with perspective space constraints for disappearing features

Inferring the 3D surface shape of a known template from 2D images captured by a monocular camera is a challenging problem. Due to the severely underconstrained nature of the problem, inferring shape accurately becomes particularly challenging when the template exhibits high curvature, resulting in the disappearance of feature points and significant differences between the inferred and actual deformations. To address this problem, this paper proposes a concise and innovative approach that utilizes a physical simulator incorporating the object’s material properties and deformation law. We utilize a view frustum space constructed from the contours of a monocular camera image to effectively restrict the physically-based free motion of the template. Additionally, we employ mesh denoising techniques to ensure the smoothness of the surface following deformation. To evaluate our shape inference results, we utilize a ground truth 3D point cloud generated from multiple viewpoint images. The results demonstrate the superior performance of our approach compared to other methods in accurately inferring deformations, particularly in scenarios where feature points are unobservable. This method carries significant practical implications across diverse domains, including virtual reality, digital modeling, and medical surgery training.

Use of metakaolin with a low surface area and rich in quartz and iron as a precursor in the production of structural alkali-activated concrete

Pumpable and machineable alkali-activated structural concretes are made using low-reactivity metakaolin with high quartz and iron contents. The effects and interaction of alkaline concentration (NaOH - 8, 10, 12, 14, and 16 M) and ratio of alkaline reagents (Na2SiO3/NaOH - 1.0, 1.5, 2.0, 2.5, and 3.0) on their properties were accessed using 2k complete factorial design. Higher concentrations of alkaline solutions resulted in faster hardening, decreased material deformation, and increased mechanical strength, with higher elastic modulus, lower damping, lower water absorption, and porosity. With time, the elastic modulus increased, and the dynamic damping decreased, suggesting the homogeneous formation of alkaline-activated products, confirming the proper configuration of the hardening process. The factorial design allowed for determining the ideal dosage for producing the material, indicating that the alkaline concentration (NaOH) should be 10 M and the Na2SiO3/NaOH ratio should be 2.5. They analyzed the possible combinations that allowed obtaining a material with adequate characteristics for the structural application.