Additive Manufacturing Process Parameters Research Articles

Advancements and Perspectives in Additive Manufacturing of Tungsten Alloys and Composites: Challenges and Solutions

This review explores additive manufacturing (AM) for refractory tungsten (W) and its alloys, highlighting the primary challenges and determining factors in the AM of pure W, W alloys and composites. The challenges mainly arise from W’s high melting point, low laser absorptivity, high thermal conductivity, high melt viscosity, high oxygen affinity, high ductile-to-brittle transition temperature, and inherent embrittlement, which lead to defects and anomalies in AM-produced parts. This review focuses on both processes and alloying strategies to address the issues related to densification, micro-cracking, and the resultant properties in W-based components. Cracking in additively manufactured W remains a persistent issue due to thermal stress, embrittlement, and oxide formation. Powder characteristics, process parameters, and thermal management strategies are crucial for W densification. Throughout the review, existing knowledge and insights are organized into comprehensive tables, serving as valuable resources for researchers delving deeper into this topic. Future research in W-AM should focus on understanding the interaction between AM process parameters and microstructural and material design. Advances in atomic-level understanding, thermodynamic modeling, and data analytics have the potential to significantly enhance the precision, sustainability, and applicability of W-AM.

Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy

ABSTRACT The inherent ‘black-box’ characteristic of neural networks renders the physical interpretability of fatigue life prediction challenging, resulting in physically inconsistent prediction results. In response to this challenge, a multi-physics information-integrated neural network framework is proposed for fatigue life prediction. Rooted in the continuous damage mechanics with embedded additive manufacturing process parameters (AM-CDM), coupled with activation functions and loss functions that amalgamate physical information, a multi-tiered and multi-physical information source network framework emerges. The framework incorporates the outcomes of the AM-CDM model as input features for domain knowledge enhancement, embeds the fatigue limit within the activation function, and establishes a loss function integrating the physical model. Through the integration of multiple physical information, the model can be restricted by different physical constraints during training, thereby improving its prediction accuracy and physical consistency. Validation of the proposed framework is conducted using laser powder bed fusion (LPBF)-fabricated Hastelloy X subjected to varying orientations, post-treatments and test temperatures. The findings demonstrate that the prediction accuracy of an integrating-physics-informed neural network (IPINN) is improved by 27.51%, and its life prediction results are capped at the fatigue limit life, yielding a better physical consistency. The IPINN framework provides a novel perspective for fatigue life prediction.

Additively Manufactured Alnico Permanent Magnet Materials—A Review

Additive manufacturing offers manufacturing flexibility for intricate components and also allows for precise control over the microstructure. This review paper explores the current state of the art in additive manufacturing techniques for Alnico permanent magnets, emphasizing the notable advantages and challenges associated with this innovative approach. Both the LPBF and L-DED processes have demonstrated promising results in fabricating Alnico with magnetic properties comparable with conventionally processed samples. The optimization of process parameters successfully reduced porosity and cracking in the LPBF processing of Alnico. The review further explored the significance of additive manufacturing process parameter optimization in managing the temperature gradient and solidification rate for a desired microstructure and enhanced magnetic properties. Other potential additive manufacturing methods suitable for the fabrication of Alnico were discussed, along with the challenges associated with the process. The insights provided also highlight how additive manufacturing holds the potential to replace post-processing techniques like solutionization, magnetic annealing, and tempering often necessary in Alnico production.

Size effects of voids on the mechanical properties of 3D printed parts

Although the effects of additive manufacturing process parameters on the mechanical properties of 3D printed parts have been numerically investigated in the literature, less attention has been paid on the size effects of voids between deposited filaments. This study fills this gap by developing a methodology based on a combined finite element (FE) and design of experiment (DoE) technique. The development of FE methodology is based on micro-mechanical analysis of representative volume element (RVE) of 3D printed parts to predict the effective orthotropic properties. To account for the size effects of inter-bead voids, the RVE includes contribution of the multiple parameters of layer heights, layer widths and overlapping regions. To study the main and interaction effects of the above input parameters on the stiffness properties of 3D printed parts, a structured approach based on full factorial design is used. Although the size effects of voids on the constituents of elastic moduli of RVE were investigated, the main focus in the present work is to develop a regression model to predict the stiffness properties. The FE stress analysis of the RVE conducted in this study provides an insight about the potential failure modes such as delamination and filament debonding that may occur in load bearing 3D printed parts. For a case study, the results of FE-based homogenization technique in terms of stiffness properties are validated against the experimental data via three-point bending and Iosipescu shear tests which were conducted in conjunction with digital image correlation technique. The combined numerical and statistical approach proposed in this study provides a swift iterative design of 3D printed parts prior to the time-consuming computation modelling, contributing to reduce the number of tests and manufacturing costs.

Multi-response optimisation of wire-arc additive manufacturing process parameters for AISI 4130 steel during remanufacturing process

Wire-arc additive manufacturing (WAAM) has emerged as a critical tool for remanufacturing industrial components. A limited understanding of this technique for quality product manufacturing has hindered its utilisation for industrial applications. This study reports on the optimisation of WAAM process parameters for AISI 4130 steel towards remanufacturing of high-quality products for industrial applications. AISI 4130 steel was selected for this study due to its high strength-to-weight ratio, excellent weldability, and suitability for the WAAM process. Taguchi’s Grey Relational Analysis (GRA) used four factors and three levels in the multiple response optimisation process. The study considered process parameters voltage, current, travel speed and gas flow in the gas metal arc welding (GMAW)-based WAAM technique. Analysis of Variance (ANOVA) results show that voltage, travel speed and gas flow significantly affect material deposition. Voltage had the highest significance (31.61%) compared to other parameters. The optimised process parameters were found to be: voltage –23 V, current –100 A, travel speed −350 mm min−1, and gas flow −10 L min−1. These parameters resulted in tensile residual stresses of 25 ± 74 MPa, microhardness of 171.4 ± 12.2 HV0.3, and a relative density of 98.21%. The microstructural analysis reveals the existence of predominant ferritic and pearlitic colonies. This is due to compounded thermal stresses during the deposition process and alloy composition resulting in tailored microstructure and mechanical properties. The study provides some insights into the WAAM remanufacturing process for producing highly quality industrial components.

Tailoring surface roughness through the temporal variation of additive manufacturing process parameters

The benefits that additive manufacturing (AM) offers to the industry are generally well understood and appreciated. However, the current design for additive manufacturing (DfAM) methodologies and computer-aided manufacturing (CAM) packages neglect to exploit the full potential that AM can offer through its unique ability to vary material characteristics whilst the final component geometry is being formed. The purpose of this research is to demonstrate that additional design control can be gained through temporal DfAM (TDfAM). In this study, the ability to tailor the surface roughness of fused deposition modelling (FDM) AM polylactic acid (PLA) parts through the variation of two process parameters, nozzle temperature and print speed, is explored. The underpinning hypothesis is that variation of temperature and printing speed, can provide a significant change of surface roughness within one homogeneous part. This research demonstrated that nozzle temperature and print speed have a statistically significant effect on the surface roughness of the top and side surfaces. By increasing temperature and speed, the roughness of the side surfaces decreased and the roughness of the top surface increased. Furthermore, the in-silico implementation of TDfAM is demonstrated. As such, the research supports the hypothesis that TDfAM can enable additional control over the surface characteristics of a homogeneous part.

Review on the Tensile Properties and Strengthening Mechanisms of Additive Manufactured CoCrFeNi-Based High-Entropy Alloys

The advent of high-entropy alloys (HEAs) provides new possibilities for the metallurgical community. CoCrFeNi-based alloys have been widely recognized to demonstrate superior mechanical properties, amongst the high-entropy alloy systems; in particular, they possess an outstanding tensile ductility and work-hardening capacity. Additive manufacturing (AM) uses a layer-by-layer material deposition approach to build parts directly from computer-aided design models, which are capable of producing near-net-shape HEAs with superior mechanical properties, surpassing traditional manufacturing methods that require a time-consuming post-treatment process, such as cutting, milling, and molding. Moreover, the rapid solidification inherent in AM processes induces the formation of high-density dislocations, which are capable of enhancing the mechanical properties of HEAs. This review comprehensively investigates and summarizes the diverse strengthening mechanisms within CoCrFeNi-based alloys produced using AM technologies, with a specific focus on their influence on tensile properties. A correlation is established between the AM processing parameters and the resultant phases and microstructures, as well as the mechanical properties of CoCrFeNi-based HEAs, which provide guidelines to achieve a superior strength–ductility synergy.

Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data

The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

Compressive and flexural mechanical responses of components obtained through mSLA vat photopolymerization technology

In recent years, Additive Manufacturing (AM) has become an increasingly popular method in industrial applications for fabricating components with complex geometries, offering several benefits over traditional (subtractive) manufacturing methods. Among all available AM technologies, “vat photopolymerization” is still a reliable approach for manufacturing high-resolution components at relatively small costs. This particular AM technology is based on the photopolymerization process where 3D objects are created by light-induced solidification and it has been broadly developed and used in the past decades. Depending on the employed light source there are three different subtypes of this technology, namely: SLA, DLP, and mSLA. Among all three listed, the Masked Stereolithography Apparatus (mSLA) technology has emerged as a promising approach due to the much simpler AM machine construction compared to the other two. However, the mechanical properties of mSLA components have not been studied extensively, hence there is a lack of knowledge of how AM process parameters and post-processing treatments affect the final mechanical properties of mSLA components. This work presents an experimental investigation of the compressive and flexural mechanical responses of components produced through this relatively new AM technology. A series of static and cyclic tests were conducted with varying layer thickness and post-curing times. It is demonstrated that a thorough optimization of the mentioned variables is required to obtain parts with the desired mechanical properties.

Development, Prospect and Challenge for Additive Manufacturing of Polymer Materials

As a manufacturing technology that fabricates three-dimensional objects by stacking materials layer by layer, additive manufacturing can be divided into laser fused deposition, electron beam fused deposition, inkjet printing and so on based on different additive manufacturing processes. In recent years, remarkable progress has been made in the additive manufacture of polymer materials. Firstly, the additive manufacturing equipment and process parameters suitable for polymer materials have been successfully developed, which realized the accurate manufacturing of complex structures. Secondly, by optimizing the formula and post-treatment of polymer materials, the performance and durability of additive products are improved. In addition, the researchers also explored the application potential of additive manufacturing of polymer materials in medical, aerospace and other fields. However, the additive manufacturing of polymer materials still faces some challenges. One of them is that the rheological properties of polymer materials have a great impact on the progress of additive manufacturing, which needs further research and improvement. Meanwhile, the heat and stress generated in additive manufacturing may lead to the deformation and degradation of polymer materials, which requires effective thermal management. Finally, compared with the traditional cutting process, the additive manufacturing cost of polymer materials is still high, so it is necessary to reduce the cost and promote production efficiency. In a word, additive manufacturing of polymer materials has broad prospects and application potential, but further research and improvement are still needed to overcome related challenges.

Ultrasonic characterization of material anisotropy in additively manufactured AlSi10Mg

Metal additive manufacturing (AM) based on laser powder bed fusion (LPBF) generates components by melting metal powder on a layer-by-layer basis. The melting and cooling process often generates samples with a preferred material symmetry aligned with the build direction. This anisotropy affects mechanical performance and can be challenging to characterize nondestructively. Here, LPBF was used to create samples of AlSi10Mg with specific geometries to affect the overall anisotropy. In addition, the hybrid AM process of interlayer milling was used to impact the microstructure and residual stress of some samples. Ultrasonic measurements were used to characterize the samples using both coherent wave and diffuse wave experiments to capture the anisotropic nature of the wave speed and scattering. The material symmetry and morphology of the grains affect the wave speed, attenuation, and backscatter with respect to direction. Furthermore, spatially resolved acoustic spectroscopy was used to provide insight regarding the localized wave speeds with respect to sample location and propagation direction. The experimental data were used collectively to quantify differences between the AM processes used to create the samples. Such information can be used to guide AM process parameters to optimize sample performance. Finally, prospects for characterization of residual stresses will be discussed.

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.

Anisotropic Mechanical and Microstructural Properties of a Ti-6Al-7Nb Alloy for Biomedical Applications Manufactured via Laser Powder Bed Fusion

Through tailoring the geometry and design of biomaterials, additive manufacturing is revolutionizing the production of metallic patient-specific implants, e.g., the Ti-6Al-7Nb alloy. Unfortunately, studies investigating this alloy showed that additively produced samples exhibit anisotropic microstructures. This anisotropy compromises the mechanical properties and complicates the loading state in the implant. Moreover, the minimum requirements as specified per designated standards such as ISO 5832-11 are not met. The remedy to this problem is performing a conventional heat treatment. As this route requires energy, infrastructure, labor, and expertise, which in turn mean time and money, many of the additive manufacturing benefits are negated. Thus, the goal of this work was to achieve better isotropy by applying only adapted additive manufacturing process parameters, specifically focusing on the build orientations. In this work, samples orientated in 90°, 45°, and 0° directions relative to the building platform were manufactured and tested. These tests included mechanical (tensile and fatigue tests) as well as microstructural analyses (SEM and EBSD). Subsequently, the results of these tests such as fractography were correlated with the acquired mechanical properties. These showed that 90°-aligned samples performed best under fatigue load and that all requirements specified by the standard regarding monotonic load were met.

Process parameters for three-dimensional specimens made of Ti6Al4V materials using additive manufacturing: A review

The Ti6Al4V alloy is highly mechanical, corrosion-resistant, low-specific weight, and biocompatible. Several high-performance applications are ideal for technical uses in the auto, aerospace, and medical sectors. The recent process parameters for Ti6Al4V, manufactured using additive manufacturing methods, are reviewed in this article for the past 20 years. It is possible to link processing parameters with fundamental information. This review finds that the process parameters in additive manufacturing are laser power, scanning speed, layer thickness, and hatch distance. In addition, this reviewed the presence of processes in the additive manufacturing of Ti6Al4V components and their impact on mechanical performance and microstructure analysis. In the additive manufacturing process, the reviewed parameter is the additive manufacturing component that has reduced the defects of microstructural evaluation, residual stress, porosity, compression, and corrosion behaviour of the study.

Complex multiphase predicting of additive manufactured high entropy alloys based on data augmentation deep learning

Additive Manufacturing (AM) can rapidly fabricate and produce customized, large-scale components, thereby fully exploiting High-Entropy Alloys (HEAs)' performance and industrial value—their broad potential for application in extreme service environments such as nuclear energy and aerospace. Because HEAs have a vast composition space, researchers can accurately map their mechanical properties by HEAs phases to reduce time-consuming and intensive experimental work. However, it is still a considerable challenge to predict HEAs phases accurately. And machine learning (ML) can predict the phases to accelerate the study of HEAs.To accurately predict the HEAs phases, the study propose deep learning (DL) algorithms that rely on AM process parameters and data augmentation and do the following: (i) This study developed a more detailed phase classification strategy for HEAs to enable the model to predict the complex multiphase of HEAs; (ii) The process parameters of AM affect the formation of the HEAs phase. In this study, the process parameters of AM are added to the model features to improve the model's accuracy in predicting the HEAs phase; (iii) To alleviate the data scarcity dilemma of HEAs, this study use data augmentation to improve DL's performance further. The results show that after adding AM process parameters to the model and performing data augmentation, the DL model's accuracy to 91.23%. In addition, this study manufactured four new HEAs through AM experiments to verify the robustness and practicality of their algorithms.

Microstructural feature-driven machine learning for predicting mechanical tensile strength of laser powder bed fusion (L-PBF) additively manufactured Ti6Al4V alloy

The rapid solidification inherent in laser powder bed fusion (L-PBF) additive manufacturing (AM) introduces segregation phenomena and formation of non-equilibrium phases in duplex titanium alloy components, thereby impeding their suitability for high-reliability engineering applications. Consequently, heat treatment becomes indispensable for optimizing both the microstructure and mechanical properties to meet application requirements. This study aims to investigate the influence of varied annealing temperatures on the evolution of L-PBF-built Ti6Al4V alloy microstructure, subsequently elucidating their impact on tensile properties by analyzing of L-PBF process parameters, building orientations, and annealing temperatures. The findings reveal that annealing at 850 °C for 2 h facilitates the transformation of brittle martensite into a ductile lamellar (α + β) microstructure, thereby conferring excellent tensile properties upon the L-PBF-built Ti6Al4V alloy. Furthermore, to accurately predict the tensile strengths of the Ti6Al4V, we take into account the L-PBF process parameters and the as-built microstructures in a comprehensive manner, extracting the pertinent microstructural features. A machine learning (ML)-based model is built to facilitate accurate predictions. Accurate and reliable predictions are demonstrated by this model when applied to Ti6Al4V. This data-driven approach establishes a novel avenue for AM material property prediction and process parameter optimization.

Designing a Novel Porous Fixation Plate with a Gyroid Lattice Structure for Humerus Fractures Using a Probabilistic Approach

Conventional fixation plates permanently attached to the body can lead to complications, such as stress shielding and aseptic loosening, due to their contact with the bone, resulting in bone loss. The contact between these solid fixation plates and the bone surface inhibits blood flow, potentially leading to necrosis at the interface. To overcome this challenge, a porous implant featuring a gyroid lattice structure for humerus bone fixation is proposed. However, designing such an implant typically involves deterministic approaches, which do not account for uncertainties in design parameters, loadings on the plate, and additive manufacturing process parameters. Consequently, the actual conditions experienced by the plate may not be accurately modeled. Herein, both deterministic and probabilistic analyses of a porous implant with a gyroid lattice structure positioned on the humeral bone is conducted. The findings are compared to the failure probabilities of both the conventional fixation plate and the optimal plate derived from a previous study. The study reveals that uncertainties in design parameters significantly influence the plate's failure probability compared to deterministic analysis, emphasizing the importance of probability‐based analyses for a reliable plate design.

Sustainable additive manufacturing of interference screws made from eco-friendly filament for anterior cruciate ligament reconstruction

Additive manufacturing (AM) is a sustainable manufacturing approach because it can lower emissions and have a lower negative impact on the environment. 3D-printed interference screws from poly-lactic acid, eco-friendly filament, by fused deposition modelling (FDM), were fabricated with different process parameters. The most popular technique for reconstructing the anterior cruciate ligament is interference screw fixation. This study considered the role of the AM sustainability paradigm in terms of material input for FDM and product manufacturing. This study considered the role of the AM sustainability paradigm in terms of material input for FDM and process efficiency by investigating the effect of the AM process parameters, such as printing temperature (PT), printing speed (PS), and infill percentage (IP) on the density and stripping torque (ST). Later, the degradation profile of the screw was analyzed using the weight loss of screws. The experimental design of the fully factorial design was with two levels for each process parameter. The significant process parameters and their interactions were through statistical analysis of the resulting design. Results have shown that PT, PS, and IP significantly influenced the density and ST of 3D-printed interference screws. In five weeks, 3D-printed interference screws started to lose weight.

Additive Manufacturing of Ti-6Al-4V with Carbon Nanotube Composite Material

Abstract This research is dedicated to investigating whether mechanical properties of Ti-6Al-4V (Titanium Grade 5) base material for additive manufacturing processes can be improved by addition of carbon nanotubes in the base material. Based on other research results with other materials, the assumption was made that reinforcing Ti-6Al-4V material with carbon nano-tubes should improve mechanical properties of the base material by 15–20 % in the additive manufacturing process. The research was divided in two steps, the first one was to investigate pure Ti-6Al-4V material and find optimal additive manufacturing processing parameters, which could maintain necessary quality standards and achieve material density levels higher than 98 % and tensile strength higher than 1000 MPa. The second part was to mix base powder with carbon nanotubes and using previously obtained processing parameters print test samples to investigate how a carbon nanotube concentration in a base powder would influence mechanical properties of the base material.

Process Parameter Prediction in Laser Powder Bed Fusion Using an Artificial Neural Network

Pores are the inevitable concomitant in the current state of laser powder bed fusion (PBF-LB/M) of AlSI10Mg components. Various pore characteristics, such as pore size and pore shape, influence the quality and affect the intended functionality of the component. Today, the experimental effort to find the appropriate process parameters for additive manufacturing (AM) results in high costs and long time-to-market. Pore formation is highly dependent on the applied process parameters. Consequently, pores can also be seen as an individual process fingerprint. Computed tomography is a commonly used measurement tool for AM components and can be used to comprehensively investigate process-induced defects. Furthermore, X-ray data allows an accurate categorisation of pores and provides a large amount of labelled data for supervised learning applications. The applied classification method classifies the pores into six classes (A-F) according to their shape and size. A total number of 3,066,249 pores detected in cylindrical samples were categorised and used for machine learning. The purpose of this work is to demonstrate an approach for predicting AM process parameters depending on the resulting pore distribution using supervised learning methods. The result is an expandable machine learning model based on an artificial neural network.