Additive Manufacturing Materials Research Articles

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.

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.

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.

Finite element simulation analysis of flow heat transfer behavior and molten pool characteristics during 0Cr16Ni5Mo1 laser cladding

0Cr16Ni5Mo1 super martensitic stainless steel has excellent weldability and is an economical material with a high life cycle and low cost, but there are few researches and applications of this material in additive manufacturing. In order to realize the application of 0Cr16Ni5Mo1 large-area laser cladding technology, the multi-track lap cladding of the material was studied. In this work, a multi-physical finite element model of multi-layer multi-track laser cladding was established, taking into account thermodynamic processes such as phase transition, Marangoni convection, and buoyancy. The flow and heat transfer behavior of 0Cr16Ni5Mo1 in multi-layer multi-track laser cladding process and the characteristics of the molten pool are studied. The research focused on the flow heat transfer behavior and molten pool characteristics of 0Cr16Ni5Mo1 during multi-layer multi-track laser cladding. The discussed aspects include the evolution history of the temperature field within the molten pool, flow field characteristics, molten pool dimensions, and dilution ratio under various laser powers and overlap rates. The results show that with the increase of overlap rate, the morphology of molten pools in different clad tracks will also change, and the flow rate, dilution ratio, and length of molten pools in the second track are negatively correlated, while the temperature and height of molten pools are positively correlated. As the laser power increases, all the characteristic quantities are positively correlated with it.

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.

Effect of in situ thermal treatment on interlayer adhesion of 3D printed polyetherimide (PEI) parts produced by fused deposition modeling (FDM)

Polyetherimide (PEI) possesses remarkable mechanical and thermal properties that make it a promising high-performance material for a wide range of applications. In addition to its simple processability, adhesive properties, and minimal smoke emission when heated, PEI is a suitable material for additive manufacturing. Because of its low cost and straightforward method, fused deposition modeling, or FDM, is a popular additive manufacturing technique for extruding thermoplastic filaments. Parts printed with the FDM approach, on the other hand, have common drawbacks such as low mechanical strength, shape inaccuracy, high porosity, void formation, interlayer adhesion problems, and anisotropic properties. The purpose of this study was to evaluate the effect of the thermal process during printing on the interlayer adhesion strength and the mechanical performances of PEI. The thermal treatment was done on a radiant heating system at 390 °C with a printing speed of 35 mm/s. In this study, parts printed with and without radiant heating system were characterized and analyzed with scanning electron microscopy (SEM), optical microscopy (OM), X-ray microtomography (µ-CT), optical profilometry (OP), atomic force microscopy (AFM), dynamic mechanical analysis (DMA), and tensile tests. The results revealed that the treated specimen exhibited better interlayer adhesion between printed layers. The interfacial voids were less visible in treated specimens. Furthermore, the treated specimen had lower porosity (5%) than the untreated one (8%). Moreover, increases of 183% in tensile strength, 22% in elastic modulus, and 190% in elongation at break were observed for treated specimens when compared to untreated ones.

Catalytic Chain Transfer in Crosslinking Photopolymerizations

AbstractPresented here is a detailed account of the development and implementation of macrocyclic cobaloxime complexes as sulfur-free, catalytic chain transfer agents (CTAs) in crosslinking photopolymerizations. Although much of this review is dedicated to understanding the fundamentals of catalytic chain transfer (CCT) in photopolymerizations, its impact on network topology and resultant mechanical properties, future goals of applying this technology to multimaterial 3D printing are also discussed. It is our long-term ambition for catalytic, sulfur-free CTAs to supplant existing consumptive, sulfur-based agents to provide new, unexplored, and not currently possible to fabricate photopolymeric materials with a specific eye towards application in dentistry, additive manufacturing, and responsive materials.1 Introduction2 History of Catalytic Chain Transfer (CCT)3 Understanding Catalyst Purity and Chain Transfer Activity4 Evidencing CCT in a Crosslinking Photopolymerization5 Comparing Cobalt(II)-Catalysts to Other Relevant CTAs6 Performance of Cobalt(II)-Catalysts in Commercial Resins7 Limitations of Approach and Looking Forward

Ductile fracture prediction of additively manufactured Ti-6Al-4 V alloy based on void growth and coalescence of a unit-cell model

In this work, we have studied the fracture mechanism and proposed a micromechanical method with a unit cell model to predict the ductile fracture of additively manufactured (AMed) Ti-6Al-4 V alloy. The tensile experiments with smooth round bars and notched round bars were conducted to investigate the mechanical properties and ductile fracture behavior of materials. The proposed unit-cell model was validated by quasi-static tensile fracture tests to predict the ductile failure of titanium alloy manufactured by laser power bed fusion (L-PBF) under different stress states, which include a wide range of stress triaxialities. An equivalent initial void fraction was introduced to evaluate the ductile behavior of the titanium alloy. Our investigation shows that the stress states significantly influence the void coalescence behavior of AMed Ti-6Al-4 V alloy, and the fracture locus of L-PBF fabricated Ti-6Al-4 V alloy under different stress states was successfully predicted by the proposed unit-cell model under proportional loading. Our study provides useful guidance for the future design and application of metal materials for additive manufacturing.

A Summary of Ti-2023: The World Conference on Titanium

Abstract The 15th World Conference on Titanium included papers on titanium alloy development, efforts to reduce Ti powder costs, additive manufacturing, and computational materials modeling tools. This article reviews some of the key advances reported at the conference.

Subtractive processing and surface integrity of additive manufacturing materials

PurposeAs an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.Design/methodology/approachFirstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.FindingsIn this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.Originality/valueWith the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Powder characterisation and the impact on part performance in electron beam melted Ti6Al4V

Metallic powder for Additive Manufacturing (AM) typically varies between different batches, while being supplied to achieve the same customer specification. In order to reduce this variation and minimise waste, batches are often mixed together in both their virgin and used form. However, the properties of bulk components must have limited variance in order to be reliably used in an industrial setting. This manuscript is focused on the analysis of Grade 5 Ti6Al4V powders (3 virgin, plus 2 blends) and the associated mechanical properties of Electron Beam Melted (EBM) AM specimens. Measurements were performed in accordance with the principles defined in the MMPDS Handbook, ESDU database, and ASTM F2924. The tensile, and fatigue properties of the EBM specimens were found to be superior when compared with Ti6Al4V properties within these standards. This demonstrated that these powders are suitable for a wide range of commercial applications, and that these blended powders (including used powders) are able to achieve these requirements. Additionally, the results provide a systematic route for powder verification and highlight the importance of adhering to standardised principles and specifications to ensure quality and reliability in metallic AM materials.

A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures

Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.

Microstructural characterization and mechanical behaviour of laser powder Bed Fusion stainless steel 316L

Laser Powder Bed Fusion (L-PBF) is a highly precise and customizable additive manufacturing (AM) technique that uses a high-energy laser to selectively melt and fuse powdered material into a three-dimensional object. However, depending on the process parameters, the final components may have potential flaws that can affect their quality and mechanical properties, due to porosity, melting and incomplete fusion of powder particles; and because the process involves local heating and sometimes uneven heat transfer, the processed components may warp or crack due to residual stresses or thermal gradients. The manufacturing process itself reflects in the final component structure having a detrimental effect on the strength, durability, fatigue resistance, and corrosion.In this work, static tensile and fatigue tests were performed on traditional and L-PBF manufactured AISI 316L stainless steel specimens. The energetic release has been evaluated with an infrared camera during the static and fatigue tests aiming to identify material thermal response to the loading and to predict the failure in rapid way adopting Thermographic Methods. Differences were observed comparing the fatigue data of the L-PBF processed specimens with the traditional material. However, analysis of internal structure, porosity, and surface characteristics of the AM material in combination with fractographic analysis helped to explain the differences in the fatigue life. The observed energy release, different for both material types, was discussed based on the structural characteristics. The results show that the crack originates from a defect on the surface or just below the surface, with a transgranular propagation.

Additively manufactured patient specific implants: A review

Medical applications of additive manufacturing have seen a significant growth in recent years due to availability of advanced medical imaging and design software and wide range of materials. The range of additively manufactured medical implants is growing rapidly and surgeons need to keep themselves updated with state-of-the-art of the technology. This article reviews several articles related to medical implants to help surgeons and researchers to stay up-to-date on recent developments in the domain. Additively manufactured medical implants are reviewed into five categories: orthopedic implants, dental implants, cranioplasty implants, scaffold implants for tissue engineering and other medical implants including chest wall reconstructive implants, anti-migration enhanced tracheal stents, and buccopharyngeal stents. Additive manufacturing process and material for fabrication of each type of implant are highlighted in the study. It has been observed that titanium alloy is a suitable material for cementless arthroplasty. Porosity in the implants supports bone ingrowth, which results in a significant reduction in stress shielding. Additive manufacturing has a very attractive future in medical implant fabrication due to its capability to produce complex and customized implants. The AM provides freedom to researcher to explore the complex design of medical implants for better bone regeneration and improved osseointegration.

A physics-informed Bayesian optimization method for rapid development of electrical machines

Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

Additive Manufacturing Technologies: Where Did We Start and Where Have We Landed? A Narrative Review.

Additive manufacturing (AM), also known as 3D printing, is gaining burgeoning interest among various dental disciplines. The import of this technology stems not only from its ability to fabricate different parts but from the solutions it provides for the customization and production of complex designs that other methods cannot offer-all to the end of enhancing clinical treatment alternatives. There is a wide range of AM machinery and materials available to choose from, and the goal of this review is to provide readers and clinicians with a decision tool for selecting the appropriate technology for a given application and to successfully integrate AM into the digital workflow.

Printability of elastomer as a 3D printing material for additive manufacturing

Printability of elastomer as a 3D printing material for additive manufacturing