Kenta Aoyagi

Bio: Kenta Aoyagi is an academic researcher from Tohoku University. The author has contributed to research in topics: Materials science & Alloy. The author has an hindex of 12, co-authored 57 publications receiving 440 citations. Previous affiliations of Kenta Aoyagi include Nagoya University.

Simple method to construct process maps for additive manufacturing using a support vector machine

Abstract: We propose a simple method to construct a process map for additive manufacturing using a support vector machine. By observing the surface of the built parts and classifying them into two classes (good or bad), this method enables a process map to be constructed in order to predict a process condition that is effective at fabricating a part with low pore density. This proposed method is demonstrated in a biomedical CoCr alloy system. We show that the proposed method is effective at reducing the number of experiments necessary to tailor an optimized process condition. This study also shows that the value of a decision function in a support vector machine has a physical meaning (at least in the proposed method) and is a semi-quantitative guideline for porosity density of parts fabricated by additive manufacturing.

Molten pool behavior and effect of fluid flow on solidification conditions in selective electron beam melting (SEBM) of a biomedical Co-Cr-Mo alloy

Abstract: Selective electron beam melting (SEBM) is a type of additive manufacturing (AM) that involves multiple physical processes. Because of its unique process conditions compared to other AM processes, a detailed investigation into the molten pool behavior and dominant physics of SEBM is required. Fluid convection involves mass and heat transfer; therefore, fluid flow can have a profound effect on solidification conditions. In this study, computational thermal-fluid dynamics simulations with multi-physical modeling and proof-of-concept experiments were used to analyze the molten pool behavior and resultant thermal conditions related to solidification. The Marangoni effect of molten metal primarily determines fluid behavior and is a critical factor affecting the molten pool instability in SEBM of the Co–Cr–Mo alloy. The solidification parameters calculated from simulated data, especially the solidification rate, are sensitive to the local fluid flow at the solidification front. Combined with experimental analysis, the results presented herein indicate that active fluid convection at the solidification front increase the probability of new grain formation, which suppresses the epitaxial growth of columnar grains.

Hot deformation characteristics and dynamic recrystallization mechanisms of a Co–Ni-based superalloy

Abstract: The hot deformation behavior of a Co–Ni-based superalloy was systematically investigated using thermal compression tests. Stress–strain curves showed a typical dynamic softening after peak stress, especially at high temperatures and low strain rates. An Arrhenius-type constitutive equation was developed to reveal the relationship between the flow stress, strain rate, and temperature, while a processing map was constructed based on the calculations from the stress-strain curves combined with microstructural observations to determine the optimum thermal deformation conditions. The extent of recrystallization was found to increase with increasing temperature, a decreasing strain rate, or an increasing strain. A complete dynamic recrystallization (DRX) condition was reached at 1050 °C/0.01 s−1/0.7. In addition, pre-existing annealing twins were replaced by discontinuous dynamic recrystallization (DDRX) grains along the twin boundaries and the twin-DRX (TDRX) grains in the twin interior. In the case of an un-twinned matrix, a combined DDRX and continuous DRX (CDRX) process occurred at high strain rates, in contrasted with a single DDRX process taking place at low strain rates.

Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment

Abstract: In laser powder bed fusion ( L -PBF) additive manufacturing, the mechanical performance, microstructure and defects of fabricated parts are closely associated with the melt pool morphology, e.g., its dimension and shape through the building process. Past studies have largely focused on how the process parameters such as laser power and scan speed affect melt pool characteristics. In this study, the melt pool morphology variation as a function of preheating temperature in the conduction, transition, and keyhole regimes and the underlying mechanisms in each regime are investigated through ex-situ sample characterization and computation thermal fluid dynamics (CtFD) simulation. Single tracks with different combinations of laser power and scan speed are deposited on an Inconel 718 bare plate preheated to a temperature range of 100–500 °C in the experiment. Significant changes are observed in melt pool morphology as a function of preheating temperature from optical measurements of melt track cross sections. The depth of melt pool in the three regimes increases monotonically with preheating temperature, e.g., at 500 °C, the experimental melt pool depth is increased by 49% in conduction regime, 34% in transition regime and 33% in keyhole regime, respectively, while the variation of melt pool width in each regime does not all follow an increasing trend but depends on the melt pool regimes. Melt pool width variation in the conduction and transition regimes is found to depend on the enhanced heat conduction directly related to temperature dependent thermal properties. Through validated CtFD simulations, it is found that in the keyhole regime the evaporation mass, recoil pressure, and laser drilling effect is enhanced with higher preheating temperature, which gives rise to a deeper melt pool. The simulations also reveal that preheating temperature significantly elongates the melt track length due to the increased flow rate and strong recoil pressure that accelerates the backward flow.

Role of operating and environmental conditions in determining molten pool dynamics during electron beam melting and selective laser melting

Abstract: Electron beam melting (EBM) and selective laser melting (SLM) are representative powder bed fusion additive manufacturing methods. Because EBM and SLM have different operating and environmental conditions, such as ambient pressure of the chamber, initial temperature, and heat source, they have different molten pool dynamics. In this study, single-bead melting experiments using EBM and SLM were performed in conjunction with computational thermal-fluid dynamics simulations in high-energy conditions to highlight the differences in the molten pool dynamics of EBM and SLM. The experimental results reveal that SLM is more likely to melt in the keyhole mode than EBM under nominally identical line energy. The simulations showed that the instantaneous maximum temperature of the SLM molten pool is much lower than that of the EBM molten pool. An increase in the preheating temperature is found to strengthen the vapor recoil pressure; however, the vapor recoil pressure under vacuum is maintained at a considerably low level in EBM. Compared to EBM, the high atmospheric pressure and multiple laser reflections during SLM significantly enhance the effect of the vapor recoil pressure on the melt surface. The findings of this study can be useful for the formulation of appropriate processing strategies for the two processes.

Machine learning in additive manufacturing: State-of-the-art and perspectives

Abstract: Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology. However, its broad adoption in industry is still hindered by high entry barriers of design for additive manufacturing (DfAM), limited materials library, various processing defects, and inconsistent product quality. In recent years, machine learning (ML) has gained increasing attention in AM due to its unprecedented performance in data tasks such as classification, regression and clustering. This article provides a comprehensive review on the state-of-the-art of ML applications in a variety of AM domains. In the DfAM, ML can be leveraged to output new high-performance metamaterials and optimized topological designs. In AM processing, contemporary ML algorithms can help to optimize process parameters, and conduct examination of powder spreading and in-process defect monitoring. On the production of AM, ML is able to assist practitioners in pre-manufacturing planning, and product quality assessment and control. Moreover, there has been an increasing concern about data security in AM as data breaches could occur with the aid of ML techniques. Lastly, it concludes with a section summarizing the main findings from the literature and providing perspectives on some selected interesting applications of ML in research and development of AM.

Device modeling of perovskite solar cells based on structural similarity with thin film inorganic semiconductor solar cells

Abstract: Device modeling of CH3NH3PbI3−xCl3 perovskite-based solar cells was performed. The perovskite solar cells employ a similar structure with inorganic semiconductor solar cells, such as Cu(In,Ga)Se2, and the exciton in the perovskite is Wannier-type. We, therefore, applied one-dimensional device simulator widely used in the Cu(In,Ga)Se2 solar cells. A high open-circuit voltage of 1.0 V reported experimentally was successfully reproduced in the simulation, and also other solar cell parameters well consistent with real devices were obtained. In addition, the effect of carrier diffusion length of the absorber and interface defect densities at front and back sides and the optimum thickness of the absorber were analyzed. The results revealed that the diffusion length experimentally reported is long enough for high efficiency, and the defect density at the front interface is critical for high efficiency. Also, the optimum absorber thickness well consistent with the thickness range of real devices was derived.

Metallurgy, mechanistic models and machine learning in metal printing

Abstract: Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes using the rich knowledge base of metallurgy remains a challenge because of the complexity of the printing process. Transforming 3D designs created in the virtual world into high-quality products in the physical world needs a new methodology not commonly used in traditional manufacturing. Rapidly developing powerful digital tools such as mechanistic models and machine learning, when combined with the knowledge base of metallurgy, have the potential to shape the future of metal printing. Starting from product design to process planning and process monitoring and control, these tools can help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification. Here, we examine advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals. Several key industries routinely use metal printing to make complex parts that are difficult to produce by conventional manufacturing. Here, we show that a synergistic combination of metallurgy, mechanistic models and machine learning is driving the continued growth of metal printing.

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review.

Abstract: Additive manufacturing (AM) is the process of joining materials layer by layer to fabricate products based on 3D models Due to the layer-by-layer nature of AM, parts with complex geometries, integrated assemblies, customized geometry or multifunctional designs can now be manufactured more easily than traditional subtractive manufacturing Path planning in AM is an important step in the process of manufacturing products The final fabricated qualities, properties, etc, will be different when using different path strategies, even using the same AM machine and process parameters Currently, increasing research studies have been published on path planning strategies with different aims Due to the rapid development of path planning in AM and various newly proposed strategies, there is a lack of comprehensive reviews on this topic Therefore, this paper gives a comprehensive understanding of the current status and challenges of AM path planning This paper reviews and discusses path planning strategies in three categories: improving printed qualities, saving materials/time and achieving objective printed properties The main findings of this review include: new path planning strategies can be developed by combining some of the strategies in literature with better performance; a path planning platform can be developed to help select the most suitable path planning strategy with required properties; research on path planning considering energy consumption can be carried out in the future; a benchmark model for testing the performance of path planning strategies can be designed; the trade-off among different fabricated properties can be considered as a factor in future path planning design processes; and lastly, machine learning can be a powerful tool to further improve path planning strategies in the future