Additive Manufacturing Materials Research Articles

Characterization of selected additive manufacturing materials for synchrotron monochromatic imaging and broad-beam radiotherapy at the Australian synchrotron- imaging and medical beamline.

This study aims to characterize radiological properties of selected additive manufacturing (AM) materials utilizing both material extrusion and vat photopolymerization technologies. Monochromatic synchrotron X-ray images and synchrotron treatment beam dosimetry were acquired at the hutch 3B of the Australian synchrotron - imaging and medical beamline (AS-IMBL).
Approach: Eight energies from 30 keV up to 65 keV were used to acquire the attenuation coefficients of the AM materials. Comparison of theoretical, and experimental attenuation data of AM materials and standard solid water for MV linac was performed. Broad-beam dosimetry experiment through attenuated dose measurement and a Geant4 Monte Carlo simulation were done for the studied materials to investigate its attenuation properties specific for a 4 tesla wiggler field with varying synchrotron radiation beam qualities.
Main results: PLA plus matches attenuation coefficients of both soft tissue and brain tissue, while ABS, ASA, and Draft resin have close equivalence to adipose tissue. Lastly, PLA, CPE plus, TPU, and White resins are promising substitute materials for breast tissue. For broad-beam experiment and simulation, many of the studied materials were able to simulate RMI457 SolidWater and bolus within +/-10% for the three synchrotron beam qualities. These results are useful in fabricating phantoms for synchrotron and other related medical radiation applications such as orthovoltage treatments. 
Significance and conclusion: These 3D printing materials were studied as potential substitutes for selected tissues such as breast tissue, adipose tissue, soft-tissue, and brain tissue useful in fabricating 3D printed phantoms for synchrotron imaging, therapy, and orthovoltage applications. Fabricating customizable heterogeneous anthropomorphic phantoms (e.g. breast, head, thorax) and pre-clinical animal phantoms (e.g. rodents, canine) for synchrotron imaging and radiotherapy using AM can be done based on the results of this study.

An approach to estimate the low cycle fatigue probabilistic curves of PBF-LB/M 316L steel from small size datasets using the remora optimization algorithm

The significant dispersion in fatigue properties is a prevalent attribute observed in metals manufactured by the Additive Manufacturing (AM). To ascertain the safety of components subjected to low cycle fatigue (LCF) loadings at a temperature of 550 °C, it is necessary to carry out a fatigue reliability assessment of AM metals. Nevertheless, the number of LCF life tests is limited owing to time-consuming nature of test and high total production cost of AM materials. Such a small size dataset cannot satisfy the criteria of the traditional reliability assessment method. This work aims to develop a novel method to evaluate the LCF reliability of 316L stainless steel manufactured by laser-based powder bed fusion of metals (PBF-LB/M) technology. Firstly, the Weibull model is coupled with the Coffin-Manson-Basquin relationship in order to characterize the dispersion of life data points at various strain levels. Secondly, reliability results from a small size dataset show a maximum relative error in fatigue life of less than 25 %, which is similar to results from a large size dataset and the traditional method using fatigue data from public sources. Among this step, the remora optimization algorithm is firstly applied to calculate the characteristic parameters of newly-proposed method. Thirdly, the LCF reliability of PBF-LB/M 316L based on the small size dataset is evaluated. The N95% is equivalent to 44 % of N50%. Finally, the PBF-LB/M 316L is compared with traditional 316L considering the reliability. The LCF properties of PBF-LB/M 316L is similar to the traditional 316L at 50 % reliability, however it’s slightly worse at 95 % reliability.

Material Flow Analysis and Occupational Exposure Assessment in Additive Manufacturing End-of-Life Material Management.

Additive manufacturing (AM) offers a variety of material manufacturing techniques for a wide range of applications across many industries. Most efforts at process optimization and exposure assessment for AM are centered around the manufacturing process. However, identifying the material allocation and potentially harmful exposures in end-of-life (EoL) management is equally crucial to mitigating environmental releases and occupational health impacts within the AM supply chain. This research tracks the allocation and potential releases of AM EoL materials within the US through a material flow analysis. Of the generated AM EoL materials, 58% are incinerated, 33% are landfilled, and 9% are recycled by weight. The generated data set was then used to examine the theoretical occupational hazards during AM EoL material management practices through generic exposure scenario assessment, highlighting the importance of ventilation and personal protective equipment at all stages of AM material management. This research identifies pollution sources, offering policymakers and stakeholders insights to shape pollution prevention and worker safety strategies within the US AM EoL management pathways.

Additive Manufacturing of Sensors: A Comprehensive Review

AbstractThe  development and manufacturing of sensors are of great importance to modern engineering, as sensors are essential for detecting environmental changes and for the monitoring of various systems. While conventional manufacturing is the most common method for fabricating sensors, additive manufacturing (commonly known as 3D printing) has gained popularity as an efficient alternative. Although additively manufactured sensors are applicable in many engineering fields, building an entire sensor (including the housing structure and sensing components) by additive manufacturing remains challenging. This work presents a comprehensive analysis of the additive manufacturing processes, materials, and applications for sensors that are either fully or partially produced by additive manufacturing. Key issues in material development and processes that limit the development of fully 3D-printed sensors are highlighted. Additionally, the role of additively manufactured sensors plays in green technology has been explored. This review is expected to provide the researchers with a comprehensive understanding of the processes and materials used to produce sensors for various applications.

Assessment of consolidative multi-criteria decision making (C-MCDM) algorithms for optimal mapping of polymer materials in additive manufacturing: A case study of orthotic application

ObjectiveThe objectives of this research are twofold. The primary goal is to introduce, investigate, and contrast consolidative multi-criteria decision-making (C-MCDM) approaches. The second objective is the investigation of five alternative additive manufacturing materials. MethodsIt integrates the subjective and objective weights using the Bayes hypothesis in conjunction with a normal method. Chang's Extent Analysis Method under fuzzy logic is used to estimate subjective weights and the CRITIC approach is used for assessing objective weights. Ranking techniques, including the simple ranking process (SRP), multi-objective optimization based on ratio analysis (MOORA), measurement alternatives and ranking according to compromise solution (MARCOS), and technique for order preference by similarity to ideal solution (TOPSIS) are applied. It also encompasses sensitivity analysis based on Kendall's coefficient of concordance and rank reversal phenomenon analysis. Spearman's rank correlation coefficient, a weighted rank measure of correlation, and rank similarity coefficient are among the metrics used to evaluate agreement between different approaches. It entails gathering expert opinions regarding the importance of various criteria as well as conducting extensive experiments. ResultsThe findings of the study indicate that polylactic acid is the best material to use for orthoses. When compared to the other MCDM approaches being discussed, SRP is the most reliable approach. It is also demonstrated that the SRP, MARCOS, and TOPSIS methods are rank reversal-free. Furthermore, SRP exhibits a very poor association with the TOPSIS technique but a strong correlation with the MOORA and MARCOS approaches. ConclusionsTo ensure results reliability, it is necessary to consider both the subjectivity and objectivity of weights as well as apply multiple MCDM methodologies in addition to sensitivity analysis.

Itaconic acid-based 3D printed nanocomposites: an in-depth study on the effect of nanoinclusions on the physicochemical properties and the printability of formulations based on polyester itaconates

The demand for novel bio-based materials in UV-curing additive manufacturing has surged due to increasing environmental concerns and a growing emphasis on sustainable practices in the manufacturing industry. However, at the moment, their thermomechanical performance is not equal to that of their fossil-based counterparts and this impedes the acceptance of these materials within the industrial community. Therefore, in this study, a series of nanocomposite polyesters based on itaconic acid was synthesized for the first time with in-situ polymerization, in an attempt to leverage the unique properties of nanofillers and improve the overall performance of the material. A variety of reinforcing agents were utilized, namely cellulose nanocrystals (CNC), montmorillonite (MMT), graphene nanoplatelets (GNP) and titanium dioxide (TiO2), to understand the effect of each filler on the physicochemical properties of the polyester. Formulations of these polyesters were then prepared and processed on a digital light processing (DLP) 3D printer to prepare test specimens. Extensive thermomechanical characterization showed that the interference of the fillers with the UV curing process was the main parameter determining the mechanical performance of the 3D printed materials.

Physics based models for characterization of machining performance – A critical review

This paper presents a comprehensive review of the concept of machinability by considering the dynamic, tribological, and thermo-mechanical interactions encountered at the tool-chip-machined surface interfaces. The paper provides a demonstration of the capabilities and gaps of the physics-based models for the characterization of the machining performance and the prediction of machinability of difficult-to-cut materials, including additively manufactured (AM) materials, nanocrystalline (NC) materials, fibre reinforced polymers (FRP), metal matrix composites reinforced with ceramic hard particles (MMC), and ceramic matrix composites (CMC). The utilization of efficient computation methods for accurate prediction of force, torque, power consumption, cutting temperature, deflection errors, vibration amplitudes, chatter stability, and thermomechanical interactions in the tool-workpiece system is discussed. The development of thermally-activated dissolution-diffusion wear models to describe the chemical reactions at the tool-chip-workpiece contact interfaces is also presented. These predictions are critical for identifying multi-objectives optimal machining conditions. The integration of predictive machining models within the framework of digital twins in cyber-physical spaces, for in-process monitoring and adaptive control, is demonstrated. Future research for developing new models that can characterize the machinability of AM and NC materials, by considering the effects of varying material microstructure and anisotropy, is presented for conventional and micro-machining operations.

Microstructural investigation of hybrid CAD/CAM restorative dental materials by micro-CT and SEM

ObjectivesAn increasing number of CAD/CAM (computer-aided design/computer-aided manufacturing) hybrid materials have been introduced to the dental market in recent years. In addition, CAD/CAM hybrid materials for additive manufacturing (AM) are becoming more attractive in digital dentistry. Studies on material microstructures using micro-computed tomography (µ-CT) combined with scanning electron microscopy (SEM) have only been available to a limited extent so far. MethodsOne CAD/CAM three-dimensional- (3D-) printable hybrid material (VarseoSmile Crown plus) and two CAD/CAM millable hybrid materials (Vita Enamic; Voco Grandio), as well as one direct composite material (Ceram.x duo), were included in the present study. Cylindrical samples with a diameter of 2mm were produced from each material and investigated by means of synchrotron radiation µ-CT at a voxel size of 0.65µm. Different samples from the same materials, obtained by cutting and polishing, were investigated by SEM. ResultsThe 3D-printed hybrid material showed some agglomerations and a more irregular distribution of fillers, as well as a visible layered macrostructure and a few spherical pores due to the printing process. The CAD/CAM millable hybrid materials revealed a more homogenous distribution of ceramic particles. The direct composite material showed multiple air bubbles and microstructural irregularities based on manual processing. SignificanceThe µ-CT and SEM analysis of the materials revealed different microstructures even though they belong to the same class of materials. It could be shown that µ-CT and SEM imaging are valuable tools to understand microstructure and related mechanical properties of materials.

Circular economy innovation: A deep investigation on 3D printing of industrial waste polypropylene and carbon fibre composites

Transforming waste polypropylene (PP) and waste carbon fibre into upcycled composite materials for additive manufacturing represents an ideal circular economy challenge. An optimized method for material extrusion and 3D printing was developed to overcome adhesion and warpage challenges during printing. The study explored the impact of varying waste carbon fibre weight fractions (0 wt%, 2 wt%, 5 wt%, 8 wt%, 15 wt%, and 25 wt%), firstly on the properties of filament and then on printability and mechanical properties of printed specimens. Optimized extrusion parameters yielded the successful fabrication of fibre-reinforced filaments. Micro-level assessment of the polymer revealed a decrease in mechanical properties due to thermal processes during filament making and 3D printing. Microstructural analysis, along with optical and scanning electron microscopy, provided insights into inter-bead voids, debonding, fibre dispersion, fracture modes, and filament/sample quality. Differential Scanning Calorimetry indicated slight matrix degradation during filament extrusion, leading to slightly reduced melting and crystallization temperatures.

Water Diffusion in Additively Manufactured Polymers: Analysis of the Capillary Effect

Additive manufacturing (AM) is an advanced manufacturing method that produces objects by sequential layering. Material extrusion AM (MEAM) with continuous-fibre reinforcement is becoming more widely used in naval structures, which are exposed to the marine environment. However, the water diffusion process and the effect of water ageing on the mechanical performance of AM materials are not yet well understood because of their complex internal structure, caused by defects generated during manufacturing. Current research on diffusion is mostly based on experimental methods for conventionally manufactured materials without considering AM-induced defects. The objective of this study is to explore how the defects inherent to MEAM affect water diffusion in a composite material by the capillary effect. Results from a numerical study of capillary flow in MEAM polymer are applied as a boundary condition in the subsequent finite-element analysis. The study illustrates that flow in the capillary reaches the steady state quicker compared to the saturation time in the matrix, predicted by Fick’s diffusion equation. It is demonstrated that the capillary effect can significantly affect the water diffusion in MEAM parts and reduce the saturation time to one-third compared to the case without accounting for this effect.

Recent development of photoresponsive materials toward 3D printing: From materials to application

AbstractThe rapid development of 3D printing has achieved considerable progress in architecture innovation, optical advancements, energy systems, dental care, pharmaceutical delivery, and personalized medical treatments. Nevertheless, the current technology of 3D printing is still limited to a single function. In recent years, the combination of photoresponsive materials and additive manufacturing has become a booming area for achieving intelligent photoresponsive 3D structures and stimulus‐responsive devices. By incorporating photoresponsive materials, 3D‐printed structures can change their shapes or colors under external stimuli. It is expected that the connection of photoresponsive materials and smart 3D printing will contribute to the development of adaptive camouflage, data retention, and molecular robotics. In this review, we summarize and discuss the current development of photoresponsive materials used in 3D printing, with an emphasis on photochromic materials, photodeformable materials, and photothermal materials. Additionally, the differences in the applications of these materials in 3D printing are compared. Finally, the current challenges and perspectives in the development of photoresponsive materials for additive manufacturing are also discussed.

Space Durable 3D Printed High‐Performance Polymers Based on Cyanate Ester/Extended‐Bismaleimide

AbstractTo accomplish the potential of the New‐Space emerging era and facilitate scientific and commercial space exploration, the development of versatile, customized, and affordable space technologies is essential. 3D printing is established as a disruptive technology, enabling the production of complex and lightweight structures with enhanced performance. However, the harsh conditions of the space environment, including atomic oxygen (AO), extreme temperatures, and ionizing radiation, pose significant challenges to the durability and longevity of additive manufacturing‐produced polymers. Until now, there are no additive‐manufacturing polymeric materials that are specifically developed and qualified to withstand space hazards. To address these challenges, novel materials for additive manufacturing, composed of cyanate ester and extended‐bismaleimide are engineered to withstand the extreme conditions in space. The developed materials demonstrate superior thermo‐mechanical properties (flexural stress of 72 MPa and Tg = 260 °C), enhanced durability to AO erosion, ionizing radiation (10 years in orbit), and thermal stability (Td5% = 360 °C). Moreover, it is found that printing orientation governs the AO erosion, thus guiding optimal printing designs for enhanced durability to AO. The materials show improved performance, endurance, and reliability, thus contributing to the development of space‐qualified components and enabling the advancement of additive manufacturing for future space missions.

Effect of Aging on Tensile and Chemical Properties of Polylactic Acid and Polylactic Acid-Like Polymer Materials for Additive Manufacturing.

Additive manufacturing, with its fast development and application of polymeric materials, led to the wide utilization of polylactic acid (PLA) materials. As a biodegradable and biocompatible aliphatic polyester, produced from renewable sources, PLA is widely used in different sectors, from industry to medicine and science. The aim of this research is to determine the differences between two forms of the PLA material, i.e., fused deposition modeling (FDM) printed filament and digital light processing (DLP) printed resin, followed by aging due to environmental and hygiene maintenance conditions for a period of two months. Specimens underwent 3D scanning, tensile testing, and Fourier transform infrared (FTIR) spectrometry to obtain insights into the material changes that occurred. Two-way Analysis of Variance (ANOVA) statistical analysis was subsequently carried out to determine the statistical significance of the determined changes. Significant impairment can be observed in the dimensional accuracies between both materials, whether they are non-aged or aged. The mechanical properties fluctuated for aged FDM specimens: 15% for ultimate tensile stress, 15% for elongation at yield, and 12% for elastic modulus. Regarding the DLP aged specimens, the UTS decreased by 61%, elongation at yield by around 61%, and elastic modulus by 62%. According to the FTIR spectral analysis, the PLA materials degraded, especially in the case of resin specimens. Aging also showed a significant influence on the elastic modulus, ultimate tensile stress, elongation at yield, elongation at break, and toughness of both materials, which was statistically shown by means of a two-way ANOVA test. The data collected in this research give a better understanding of the underlying aging mechanism of PLA materials.

An investigation into the grindability of additively manufactured 42CrMo4 steel

This study investigates post-processing of additively manufactured (AM) low-alloy 42CrMo4 steel (AISI 4140) produced by powder bed fusion – laser beam (PBF-LB). While the PBF-LB process produces tempered martensite by in-situ heat-treatment, resulting in superior mechanical properties, finishing by grinding remains critical for use in precision components such as automotive gears. The grindability of the AM material is compared to conventionally produced steel and reveals comparable results to most of the grindability criteria tested. However, higher wheel wear is observed when grinding the AM material. This is likely due to the lack of machinability-enhancing inclusion engineering treatment common in conventional steelmaking.

Interpretable machine learning approach for exploring process-structure-property relationships in metal additive manufacturing

Process-structure-property (PSP) relationships are critical to the optimization of manufacturing processes, but establishing these relationships typically involves time- and cost- consuming experiments, especially for additive manufacturing (AM) due to the large number of process parameters involved. In this study, we develop a novel and interpretable machine learning approach for predicting, optimizing, and expanding the process window of laser powder bed fusion (LPBF) while simultaneously establishing PSP relationships, using AlSi10Mg as an example. Our iterative, error-targeted method substantially decreases the amount of experimentation required. Gaussian process regression (GPR) was employed as the predictive model, incorporating multiple input variables (e.g., process parameters, relative density, melt pool morphology, cellular structure, and grain structure), for predicting three mechanical properties (i.e., yield strength, ultimate tensile strength, and % elongation). A comparison of model predictions and experimental data outside the training scope reveals that the prediction accuracy can be improved with higher dimensional inputs and further enhanced by a multi-output model that accounts for correlations between the different mechanical properties. Additionally, the GPR kernel’s hyperparameter for each input enables feature selection and model interpretability. The proposed approach can assist with finding the most critical variables affecting mechanical performance, establishing the PSP relationships of AM fabricated alloys, and providing guidance for tailoring the final properties. The methodology presented in this study can be applied to various AM techniques and materials to broaden the process window to achieve previously unattainable mechanical properties, as well as to gain a deeper understanding of the PSP relationships.

Effect of surface modification on the high temperature low cycle fatigue performance of LPBF 316L austenitic steel

In this study, high temperature low cycle fatigue (LCF) tests at 550 °C were conducted on LPBF (Laser powder bed fusion) 316L with three different surface states, including the As-built surface (AB), fill contour (FC) manufacturing surface, and the surface combining FC and electropolishing (FC + EP). The effect of surface treatment on the high temperature LCF performance of LPBF 316L was investigated. Experimental results showed that the FC + EP surface treatment had the most significant effect on enhancing life. The difference in enhancing the fatigue life behavior was mainly attributed to the various crack initiation mechanisms with different surface states. The cracks on the surfaces of AB and FC mostly originate from the powder adhesion areas and the boundaries of the melt pool, while FC + EP surface treatment weaken the damage induced by powder adhesion. Furthermore, a parameter representing the fatigue crack propagation driving force that considers the surface condition has been defined, and a proposed life prediction model can provide guidance for surface modification and post-treatment of additive manufacturing materials as well as other metal materials.

Maximizing fiber content in 3D-printed earth materials: Printability, mechanical, thermal and environmental assessments

Earth building materials are experiencing a new renaissance with novel avenues in advanced manufacturing methods such as 3D printing. However, current mix designs are still limited to little to no fiber reinforcement, which can improve ductility and thermal resistivity of the fabricated building assemblies. This research develops 3D printable earth-fiber composites while maximizing natural fiber content for low-carbon and high-performance materials in additive manufacturing. A range of mix designs are formulated and characterized for their printability, mechanical, thermal, and environmental carbon storage performance. The results of this work present successfully developed novel earthen mixtures with natural fiber content ranging from commonplace “cob” (2%wt fiber) to newly developed “light fiber clay” (up to13%wt fiber content and 49% by total volume), incorporating a range of agrowastes products, including wheat straw, hemp, banana, and kenaf fibers. The performance assessment results showcase meaningful improvement of the fiber-reinforced mix designs as opposed to the earthen counterparts, with up to 125% higher compressive strength and up to 86% lower thermal conductivity. The significance of this work lies in the advancement of sustainable and circular materials and assemblies for future manufacturing of socially equitable built environments.

Machine-Guided Discovery of Acrylate Photopolymer Compositions.

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

Tuning the mechanical and thermomechanical properties through the combined effect of crosslinking and annealing in poly(lactic acid)/acrylonitrile‐EPDM‐styrene blends

AbstractAcrylonitrile‐EPDM‐styrene (AES) was applied as an impact modifier for poly(lactic acid) (PLA), and the combined effect of dicumyl peroxide (DCP) crosslinking and annealing heat treatment was investigated. Torque rheometry, melt flow index (MFI), mechanical properties (impact, tensile, and Shore D hardness), X‐ray diffraction (XRD), infrared spectroscopy (FTIR), heat deflection temperature (HDT), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) were investigated. There was a considerable torque increment and fluidity drop in the PLA/AES/DCP blends caused by the crosslinking process and the formation of PLA‐g‐AES. Consequently, the impact strength and elongation at break properties improved. As an engineering terpolymer, AES was decisive in maintaining high elastic modulus, Shore D hardness, and HDT values. The PLA/AES/DCP (0.8 phr) composition leaned towards a synergism of mechanical properties, gaining 505.6% and 264.8% in impact strength and elongation at break, respectively, compared to neat PLA. FTIR and XRD analysis revealed high crystallinity, with samples presenting large crystals. The higher crystallinity had a deleterious effect on the mechanical properties of the PLA/AES/DCP blends. However, there was a marked improvement in HDT and higher toughness compared to PLA. The results before and after annealing the PLA/AES/DCP (0.8 phr) blend are promising for constructing new semi‐biodegradable materials for additive manufacturing.

Experimental study and crystal plasticity modeling of additive manufacturing IN718 superalloy considering negative strain rate sensitivity behavior

Revealing underlying mechanisms of IN718 superalloy's negative strain rate sensitivity behavior at elevated temperatures is significantly important for its further applications in extreme conditions. This work investigates the microstructural mechanisms of the temperature-dependent strain rate sensitivity using experimental methods and crystal plasticity characterization model. Firstly, Electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM) techniques are utilized to observe microstructural characteristics of additively manufactured IN718 alloy at different temperatures. The influence of solute precipitation and dislocation slip interaction on the mechanical properties is discussed. Subsequently, the representative volume element (RVE) model is constructed by using acquired orientation, grain size, and dislocation density data. Finally, the crystal plasticity model incorporating dynamic strain aging and dislocation density evolution is developed to characterize the relationship between strain rate sensitivity and temperature. The model can effectively simulate the transition of strain rate sensitivity of additive manufacturing materials. The results indicate that the increase in dislocation pinning caused by intensified carbon element precipitation at high temperatures is the primary reason of the strain rate sensitivity change observed in IN718 metal materials. The carbon precipitated at the dislocation nucleus induces serration flow in IN718 superalloy under high strain. This study contributes to understanding and characterizing the microscopic mechanisms of deformation in metal materials subjected to thermal environments.