Challenges In Additive Manufacturing Research Articles

Sustainable Transformation: A Case Study of an Engineering-to-Order Additive Manufacturing Firm

Purpose: Addressing sustainability challenges in additive manufacturing (AM) is critical due to high energy consumption and waste. Optimising AM operations is vital for sustainability. However, the existing literature lacks practical examples for AM companies on how to enhance sustainability. To address this gap, a case study within an AM firm was conducted. Design approach: Using the analytical hierarchy process (AHP), sustainability key performance indicators (KPIs) were identified, aligned with the firm’s strategic objectives. Sustainable value stream mapping (Sus-VSM) analysed the production process for sustainability performance. A sustainability strategy map (SSM) was created based on the five perspectives of the 4th generation balanced scorecard (BSC) approach and the results from the case study. Findings: The research identified optimisation areas in the multi-jet-fusion (MJF) process, crafted tailored sustainability KPIs, and developed a SSM to seamlessly integrate sustainability initiatives into the company’s vision. Value: This contribution provides a structured methodology for selecting KPIs and offers a holistic approach to implementing Sus-VSM within the AM industry. The manuscript emphasises the need to incorporate a fifth perspective into the BSC and proposes a framework for developing a SSM. This study aims to guide AM companies toward a more environmentally responsible and socially conscious approach to manufacturing.

Laser powder bed fusion in-situ alloying of W-Y alloy: Microstructure, mechanical properties and cracking suppression

The microcracks caused by impurity oxygen are recognized as one of the main challenges in additive manufacturing of tungsten. In this study, yttrium was incorporated into the tungsten matrix to suppress crack formation, and W-Y alloys with minimal cracks were successfully obtained by laser powder bed fusion (LPBF). It is found that during the LPBF process, yttrium combines with impurity oxygen to generate intergranular and intragranular precipitated particles in situ, which effectively suppress cracking behavior due to impurity oxygen. Furthermore, these intracrystalline nanoprecipitate are cubic Y2O3 particles that have formed a semi-coherent interface with the tungsten matrix. These dispersed particles significantly improved the mechanical properties of W-Y alloy. The results offer a corresponding strategy for crack suppression of tungsten alloys in laser additive manufacturing processes, and would provide a benificial additive manufacturing strategy to other refractory alloys.

Experimental Study and Random Forest Machine Learning of Surface Roughness for a Typical Laser Powder Bed Fusion Al Alloy

Surface quality represents a critical challenge in additive manufacturing (AM), with surface roughness serving as a key parameter that influences this aspect. In the aerospace industry, the surface roughness of the aviation components is a very important parameter. In this study, a typical Al alloy, AlSi10Mg, was selected to study its surface roughness when using Laser Powder Bed Fusion (LPBF). Two Random Forest (RF) models were established to predict the upper surface roughness of printed samples based on laser power, laser scanning speed, and hatch distance. Through the study, it is found that a two-dimensional (2D) RF model is successful in predicting surface roughness values based on experimental data. The best and minimum surface roughness is 2.98 μm, which is the minimum known without remelting. More than two-thirds of the samples had a surface roughness of less than 7.7 μm. The maximum surface roughness is 11.28 μm. And the coefficient of determination (R2) of the model was 0.9, also suggesting that the surface roughness of 3D-printed Al alloys can be predicted using ML approaches such as the RF model. This study helps to understand the relationship between printing parameters and surface roughness and helps print components with better surface quality.

Achieving uniform distribution of nanoparticles in oxide dispersion strengthened (ODS) SS316L through laser powder bed fusion (L-PBF)

Oxide dispersion strengthened (ODS) SS316L is a promising candidate for the nuclear industry for its enhanced irradiation resistance and high temperature strength. Additionally, additive manufacturing enables the design flexibility of components. Achieving a uniform distribution of oxides in ODS SS316L is one of the remaining challenges in additive manufacturing. In this paper, we investigated the effects of printing parameters on the microstructure and mechanical properties of ODS SS316L (SS316L + 0.5 wt% Y2O3) through laser powder bed fusion (L-PBF) additive manufacturing. The results showed that plate-like Y-Si-rich oxides (∼50 μm) were observed along the molten pool boundary in the ODS SS316L printed with nominal parameters (48.5 J/mm3) for pure SS 316L, resulting from inadequate heat input in molten pool due to the reduced laser absorption rate of powder feedstock. Through higher volumetric energy density (76.2 J/mm3) and remelting, a bimodal distribution of oxides, including nanoparticles and fine spherical oxide (∼2.5 μm), was achieved. Consequently, this contributed to increased ultimate tensile strength (UTS) and strain of ODS SS316L from 685.2.6 ± 31.4 MPa and 27.8 ± 6.2 % to 706.6 ± 36.2 MPa and 33.0 ± 6.1 %, respectively. The exploration of parameters optimization provides valuable insights into the additive manufacturing of ODS alloys with uniformly distributed nanoparticles.

Use of Pulsed Potentials for Electropolishing TA6V Parts Elaborated By Additive Manufacturing in Deep Eutectic Solvent

The Ti-6Al-4V alloy's high mechanical properties, low density, excellent biocompatibility, and corrosion resistance make it an ideal material for sectors such as aeronautics, astronautics and medical technology. The demand for increasingly complex part designs is speeding up the development of Additive Manufacturing (AM) technology, enabling the creation of shapes unachievable with traditional machining. However, the parts manufactured present significant surface roughness, thus reducing resistance to corrosion and mechanical stress. To mitigate these risks, homogeneous polishing of the entire shape is required. Electrochemical polishing is an all-round part treatment technique ideal for addressing AM challenges. Deep Eutectic Solvent (DES) electrolytes are well-suited for this application. The aim of this study is to develop electropolishing of Ti-6Al-4V parts in a DES solvent of ethylene glycol and choline chloride. The challenges posed by viscous layer thickness problems, as well as two methods to overcome them, are described, including agitation control and pulsed potentials.

An Overview on Additive Manufacturing of Duplex Stainless Steels: Microstructure, Mechanical Properties, Corrosion Resistance, Postheat Treatment, and Future Perspectives

Additive manufacturing (AM) is a cutting‐edge technique for constructing intricate components with unique microstructural features and strength comparable to wrought alloys. Due to their exceptional corrosion resistance and mechanical properties, duplex stainless steels (DSS) are used in a wide range of critical applications. Over the past several years, a substantial body of research has been conducted on the AM of DSS. In‐depth knowledge is required to understand the complete benefits of the AM process. This review overviews the AM‐processed DSS parts based on process‐specific microstructural changes, mechanical behavior, electrochemical performance, and postheat treatment processes based on the classifications of directed energy deposition and powder bed fusion AM techniques along with future perspectives. Major challenges in AM of DSS are optimizing the austenite–ferrite fractions and controlling the formations of deleterious phases. This review will be extensively useful to researchers and industries working in the AM of DSS.

Novel additive lamination manufacturing system for rapid fabrication of large-scale reinforced structural members

PurposeAdditive lamination manufacturing (ALM), as a novel additive manufacturing technology, builds up the geometry via the lamination of fiber-reinforced polymer (FRP) fabric laterally, rendering it suitable for fabricating large-scale Stay-in-Place concrete formwork. This paper aims to investigate the control parameters and structure performance of ALM and assess its application for the fabrication of large-scale concrete formwork.Design/methodology/approachBased on previous feasibility studies, this research systematically investigates the control and material parameters that influence horizontal and vertical extrusion speeds, as well as the overall quality of ALM. Once the system parameters are established, a series of prototypes are fabricated and tested to validate the tensile strength of the formwork and its reinforcement capabilities. In addition, this study assesses the potential geometric freedom and implementation constraints of ALM.FindingsThis research identifies the essential control parameters for path planning in ALM and examines their impact on fabrication. In addition, this paper evaluates ALM’s strengths and limitations in producing concrete formwork for large-scale concrete structures, comparing these to industry benchmarks.Originality/valueA critical challenge in additive manufacturing lies in its scalability and compatibility with existing construction processes. In comparison to concrete, FRP offers advantages such as being lighter, easier to handle and providing surface protection and reinforcement. These qualities make FRP superior for formwork and compatible with existing building standards. Despite its advantages and potential, the current path planning and control model in 3D printing do not apply to ALM due to its novel build-up process. Also, the performance of fabricated parts as part of integrated large-scale structures is yet to be studied.

Computational challenges in additive manufacturing for metamaterials design.

Computational challenges in additive manufacturing for metamaterials design.

Computational design of mechanical metamaterials.

In the past few years, design of mechanical metamaterials has been empowered by computational tools that have allowed the community to overcome limitations of human intuition. By leveraging efficient optimization algorithms and computational physics models, it is now possible to explore vast design spaces, achieving new material functionalities with unprecedented performance. Here, we present our viewpoint on the state of the art of computational metamaterials design, discussing recent advances in topology optimization and machine learning design with respect to challenges in additive manufacturing.

Solid State Additive Manufacturing of Thermoset Composites.

Softening and subsequent deformation are significant challenges in additive manufacturing of thermal-curable thermosets. This study proposes an approach to address these issues, involving the preparation of thermosetting composite powders with distinct curing temperatures, the utilization of cold spray additive manufacturing (CSAM) for sample fabrication, and the implementation of stepwise curing for each component. To validate the feasibility of this approach, two single-component thermosetting powders P1 and P2 and their composite powder C were subjected to CSAM and stepwise curing. From the sample morphology observation and deposition/curing mechanism investigation based on thermomechanical analysis and differential scanning calorimetry, it is found that severe plastic deformation occurs during the CSAM process, accompanied by heat generation, leading to local melting to promote a good bond at the contact surface of the particles and form small pores. During the progressive curing, the samples printed using C demonstrate superior deformation resistance compared with those using P1 and P2, and the curing time is reduced from 16.7 h to 1.5 h, due to the sequential curing reactions of P1 and P2 components in composite C, allowing the uncured P2 and cured P1 to alternately remain solid for providing structural support and minimizing deformation.

Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion

Bio-inks and biomaterial inks are crucial to the success of 3D bioprinting, as they form the foundation of almost every 3D bio-printed structure. Despite the use of various biomaterial inks with potential biomedical applications in 3D printing, developing printable biomaterial inks for extrusion-based 3D bioprinting remains a major challenge in additive manufacturing. To be effective, the inks must possess suitable mechanical properties, high biocompatibility, and the ability to print precisely. In this study, machine learning (ML) was employed to develop a chitosan-gelatin-agarose biomaterial ink. The ink's printability, rheological properties, hydrophilicity, degradability, and biological response were evaluated after an optimization process. The optimized ink exhibited adequate viscosity for reliable printing, and 3D structures were created to assess printability and shape integrity. Bone marrow mesenchymal stem/stromal cells (BMSCs) were cultured on the ink's surface, and cell adhesion, growth, and morphology were assessed. Results showed favorable cell morphology, and cell viability within the optimized ink. The ink consisting of 27 % agarose, 53 % chitosan, and 20 % gelatin (ACG), may be a suitable biomaterial for fabricating 3D complex tissue constructs.

A machine learning approach for predicting evaporation-induced composition variability in directed energy deposition in-situ alloying

This study addresses the challenge of compositional change due to evaporation in additive manufacturing, specifically focusing on directed energy deposition in-situ alloying. A machine learning model, capable of delivering high predictive accuracy and generalizability even with a limited dataset, was developed using data from 177 ferromanganese specimens fabricated in this study. To improve model interpretability, Surrogate SHAP, an innovative analysis technique, was introduced to clarify the relationships between process conditions and compositional changes. Furthermore, a machine learning-based process design method capable of predicting process conditions that meet specified compositional targets was developed. Experimental validation confirmed the model’s high predictive accuracy and its effectiveness in identifying optimal process conditions for achieving desired compositions. This research demonstrates the potential of machine learning to address traditional challenges in additive manufacturing and lays a foundation for further advancements in materials science and manufacturing process optimization.

Non-contact targeted removal of remnant powder particles from additive manufacturing builds with nano-second laser pulsing

Post-production precision cleaning is one critical challenge in Additive Manufacturing (AM) production of high-value, critical parts/builds. A part shedding particle during its operational use could cause severe complications in critical applications such as medical devices and aerospace components. This study presents a non-contact precision particle removal procedure based on a novel Dry Laser Cleaning (DLC) framework. DLC utilizes the thermo-elastic wave generation/propagation and high-frequency surface acceleration fields to dislodge and remove remnant particles due to inertial forces from a well-defined (user-specified) cleaning zone in a non-contact manner, as opposed to cleaning the entire build surface, which could lead to undesirable re-deposition and re-location of particles as well as high cost. The removal of spherical Stainless Steel 316 L (SS316L) micro-particles commonly used in AM from polished, atomically flat Silicon (Si) wafer substrates with uniform high free surface energy using a nano-second laser pulse is experimentally and computationally investigated. In general, a rough surface has a lower average free surface energy; thus, its cleaning, compared to Si wafer, is less challenging. The data with the SS316-Si material pair offers a consistent baseline. In parallel to experiments, a fully-coupled thermo-mechanical finite element study is also conducted for transient surface acceleration simulations to predict the effectiveness of SS316L micro-particle removal from a Si wafer substrate with laser pulsation. It is observed that SS316L micro-particles with a diameter larger than 5.61 μm, 8.43 μm, and 12.24 μm can be removed entirely at 3 mm, 6 mm, and 9 mm distances from its epicenter, respectively. Accordingly, a work-of-adhesion of 990.8 mJ/m2 is selected between the SS316-Si material pair, consistent with the literature. The reported experiments and simulations demonstrate the potential of the proposed rapid precision particle removal procedure for cleaning critical powder-based AM builds, where the targeted removal of particles is essential for the safe and reliable operation of high-value builds and parts.

Additive manufacturing in the medical sector: from an empirical investigation of challenges and opportunities toward the design of an ecosystem model

PurposeThis works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future.Design/methodology/approachThis research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector.FindingsAmong the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed.Originality/valueThis work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.

Defect analysis of 3D printed object using transfer learning approaches

Additive manufacturing (AM) is rapidly gaining traction across diverse industries, including healthcare, aerospace, and automotive, mainly due to its ability to enhance production efficiency. However, a critical challenge in AM is the early detection of defects, which can significantly reduce production costs and improve productivity. Addressing this need, our study explored the effectiveness of machine learning (ML) approaches, mainly focusing on transfer learning (TL) models, for defect detection in 3D-printed objects. We utilized a range of TL models such as visualgeometrygroup (VGG)-16, inception-residualnetwork (InceptionResNet)-V2, residual network (ResNet)-50, mobilenetwork (MobileNet)-V2, VGG19, ResNet101, extreme inception (Xception), efficientnetwork (EfficientNet)-B0, EfficientNetB7, efficientnetworkV2medium (EfficientNetV2M), and neural architecture search network large (NASNetLarge), analyzing images of 3D-printed objects across three datasets using various statistical measures. An ablation study was also conducted using adaptive moment estimation (Adam), stochastic gradient descent (Sgd), and root mean square propagation (Rmsprop) optimizers. We identified that TL models such as VGG16, MobileNetV2, InceptionResNetV2, and NASNetLarge significantly outperform others, especially when optimized with Adam and Rmsprop. Conversely, we found that models like EfficientNetB0, EfficientNetB7, and EfficientNetV2M exhibited lower performance when paired with the Sgd optimizer. Additionally, Local Interpretable Model-agnostic (LIME)-based approaches are used to provide explanations of the models’ predictions. These findings offer substantial insights into model optimization and integration, aiming to enhance AM product quality through advanced image-based monitoring and inspection.

Effects of high gravity on properties of parts fabricated using material extrusion system by additive manufacturing

Additive manufacturing (AM) has gained significant attention in recent years owing to its ability to fabricate intricate shapes and structures that are often challenging or unattainable using conventional manufacturing techniques. This high-quality development trend entails higher requirements for the structural design of 3D printers. In this study, polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) filaments were fed through a heated extrusion nozzle, which melted the material and deposited it onto a build platform. This study's objectives are high-gravitational material extrusion (HG-MEX) systems development, analyzing the high gravity influences on the flow behavior of materials during extrusion, and understanding the effects of gravitational on material flow and overall extrusion performance. HG-MEX systems have great potential for addressing various challenges in additive manufacturing, such as precise manufacturing. The highlight of the progress is that we developed an HG-MEX system and applied surface science to material extrusion in different gravity. We established a system and obtained results on different gravity, we analyzed the analogy between different gravity phenomena. We analyzed the interplay between the behavior of the fabricated parts and gravity. We analyzed high gravity effects on extrusion processes. The results confirmed the characteristics and feasibility of the developed system. The results suggest that a material extrusion line operating under 15 G conditions resulted in better printing quality compared to one operating under 1 G conditions. This observation implies that high gravity had a positive effect on the extrusion process, leading to improved material extrusion performance.

Full-composition-gradient in-situ alloying of Cu–Ni through laser powder bed fusion

Multi-material laser powder bed fusion has long been a challenge in additive manufacturing, especially in terms of controlling spatial variations and joining multiple materials. The challenge is even greater when multi-material components have different physical properties (e.g., different thermal expansions, thermal conductivities and laser reflectivities). The aforementioned scenario is applicable to copper–nickel (Cu–Ni) alloy. The simultaneous printing and controllable mixing of pure Cu and pure Ni within a single print have not been accomplished due to the substantial disparity in the physical properties of pure Cu and pure Ni. In this work, a full composition with different mixing proportions (100 % Cu to 100 % Ni) was fabricated in a single printing process through in-situ alloying based on our process of micro laser powder bed fusion. A transition from a columnar grain to a quasi-equiaxed grain morphology with texture variation from <110> to <111> was found in the full–composition–gradient of the Cu–Ni alloy. Microstructural variations, such as fine grain strengthening and grain boundary strengthening, greatly affected the mechanical properties of the alloys: the ultimate tensile strength ranged from 303 to 488 MPa and the yield strength ranged from 231 to 445 MPa. Moreover, the electrical conductivity of the alloys with a compositional gradient ranged from 3.6 % IACS to 96 % IACS due to the electron scattering induced by lattice distortion. In summary, a full-composition Cu–Ni gradient alloy was fabricated through in-situ alloying adopting micro laser powder bed fusion for the first time, enabling the investigation of the evolution of the microstructure and properties of the alloy as the alloy composition varies.

MitM attacks on intellectual property and integrity of additive manufacturing systems: A security analysis

Additive Manufacturing (AM) was originally invented to reduce the cost of the prototyping process. Over time, the technology evolved to be faster, more accurate, and affordable. These factors, in addition to the potential use of AM in parts production, have helped rapidly drive the growth of AM in both industrial and personal uses. Thus, there is an accompanying demand to understand the cybersecurity implications of such systems. In our research, we present an in-depth security review of Stratasys Dimension Elite and show how manufacturers of such high-end 3D printers failed to protect the confidentiality and integrity of the printed 3D models. Revealing the intricate dimensions of cyber threats within the realm of AM and laying the foundation for understanding the multifaceted nature of attacks, offering insights into vulnerabilities and potential consequences. Moreover, we demonstrate the massive impact network attacks can have on 3D printers' communication channels. Our sniffing attack stole transmitted models with a minimal overhead of 0.015 seconds to evade detection. The developed replacement attack targeted and replaced specific models with offline-prepared models. Also, we automated a sabotaging attack to alter the interior model structure on the fly with minimal visual but significant strength differences. By revealing these attacks, this research not only improves the security posture of 3D printers but also enhances the understanding of security challenges in additive manufacturing as a whole.

Legal challenges of additive manufacturing on the Moon

Additive manufacturing (AM) is promising for supporting sustainable human presence on the Moon as it reduces costs and dependency on the Earth. It has been incorporated into several lunar programs. Nonetheless, AM on the Moon is legally challenging. First, the legal field most challenged by AM is intellectual property (IP) law, and the peculiar location of AM on the Moon complicates the issue. Deciding the State to exercise jurisdiction and extend its IP protection to the Moon encounters difficulties. Second, AM on the Moon is most valuable when it uses lunar regolith as a source of printing material, which may face similar legal problems to in situ resource utilization, including determining the legality of the activity, deciding the State to exercise the jurisdiction and control over it, the protection of the lunar environment, and so forth. Lunar in situ AM using lunar regolith (LISAMUR), meaning using lunar regolith as raw materials to manufacture objects on the Moon through AM technologies, renders these problems more imminent and substantial. The article delves into the two categories of legal issues ignited by AM on the Moon and reveals that three reasons lead to the legal dilemma: the ambiguous terminology of the space treaties, the abstract principles, and the lacunae in space law. It suggests that as international legislation is slow, temporary approaches of coordinating the understanding of critical notions and relevant principles in space law and updating the registration regime through international cooperation on multilateral platforms would be more viable for coping with the legal challenges of AM on the Moon.

A review on critical challenges in additive manufacturing via laser-induced forward transfer

A review on critical challenges in additive manufacturing via laser-induced forward transfer