• Journal of dental hygiene science
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• v.15 no.1
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• pp.12-17
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• 2015

The purpose of this study was to evaluate the marginal and internal gap of metal coping fabricated using additive manufacturing (AM) group and subtractive manufacturing (SM) group by dental computer-aided design (CAD)/computer-aided manufacturing (CAM) systems. Twenty same cases of stone models of abutment teeth 16 by the universal numbering system were manufactured and scanned. Ten metal copings of control group were fabricated using SM and ten metal coping of experimental group were fabricated using AM. Marginal and internal gap of copings were measured using the silicone replica technique and digital microscope (${\times}140$). The data were analyzed using IBM SPSS 21.0 Statistical Software for independent samples t-test (${\alpha}=0.05$). Mean${\pm}$ standard deviation (SD) of marginal and internal gap total size of SM group was $101.00{\pm}40.33{\mu}m$ of AM group was $83.61{\pm}40.37{\mu}m$. Mean${\pm}$SD of marginal and internal gap total size of SM group was significantly greater than that of AM group (p<0.05). This study showed that AM metal copings had a better marginal and internal gap than SM metal copings.

• Safety and Health at Work
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• v.10 no.2
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• pp.229-236
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• 2019

Background: Emerging reports suggest the potential for adverse health effects from exposure to emissions from some additive manufacturing (AM) processes. There is a paucity of real-world data on emissions from AM machines in industrial workplaces and personal exposures among AM operators. Methods: Airborne particle and organic chemical emissions and personal exposures were characterized using real-time and time-integrated sampling techniques in four manufacturing facilities using industrial-scale material extrusion and material jetting AM processes. Results: Using a condensation nuclei counter, number-based particle emission rates (ERs) (number/min) from material extrusion AM machines ranged from $4.1{\times}10^{10}$ (Ultem filament) to $2.2{\times}10^{11}$ [acrylonitrile butadiene styrene and polycarbonate filaments). For these same machines, total volatile organic compound ERs (${\mu}g/min$) ranged from $1.9{\times}10^4$ (acrylonitrile butadiene styrene and polycarbonate) to $9.4{\times}10^4$ (Ultem). For the material jetting machines, the number-based particle ER was higher when the lid was open ($2.3{\times}10^{10}number/min$) than when the lid was closed ($1.5-5.5{\times}10^9number/min$); total volatile organic compound ERs were similar regardless of the lid position. Low levels of acetone, benzene, toluene, and m,p-xylene were common to both AM processes. Carbonyl compounds were detected; however, none were specifically attributed to the AM processes. Personal exposures to metals (aluminum and iron) and eight volatile organic compounds were all below National Institute for Occupational Safety and Health (NIOSH)-recommended exposure levels. Conclusion: Industrial-scale AM machines using thermoplastics and resins released particles and organic vapors into workplace air. More research is needed to understand factors influencing real-world industrial-scale AM process emissions and exposures.

• Journal of Dental Rehabilitation and Applied Science
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• v.37 no.1
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• pp.1-15
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• 2021

Additive manufacturing (AM) for dental materials can produce more complex forms than conventional manufacturing methods. Compared to milling processing, AM consumes less equipment and materials, making sustainability an advantage. AM can be categorized into 7 types. Polymers made by vat polymerization are the most suitable material for AM due to superior mechanical properties and internal fit compared to conventional self-polymerizing methods. However, polymers are mainly used as provisional restoration due to their relatively low mechanical strength. Metal AM uses powder bed fusion methods and has higher fracture toughness and density than castings, but has higher residual stress, which requires research on post-processing methods to remove them. AM for ceramic use vat polymerization of materials mixed with ceramic powder and resin polymer. The ceramic materials for AM needs complex post-processing such as debinding of polymer and sintering. The low mechanical strength and volumetric accuracy of the products made by AM must be improved to be commercialized. AM requires more research to find the most suitable fabrication process conditions, as the mechanical properties and surface of any material will vary depending on the processing condition.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.129-135
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• 2021

Recently, additive manufacturing (AM) technology such as powder bed fusion (PBF) and directed energy deposition (DED) are actively attempted as consumers' needs for parts with complex shapes and expensive materials. In the present work, the effect of processing parameters on the mechanical properties of 316L stainless steel coupons fabricated by PBF and DED AM technology was investigated. Three major mechanical tests, including tension, impact, and fatigue, were performed on coupons extracted from the standard components at angles of 0, 45, 90 degrees for the build layers, and compared with those of investment casting and commercial wrought products. Austenitic 316L stainless steel additively manufactured have been well known to be generally stronger but highly vulnerable to impact and lack in elongation compared to casting and wrought materials. The process-induced pore density has been proved the most critical factor in determining the mechanical properties of AM-built metal parts. Therefore, it was strongly recommended to reduce those lack of fusion defects as much as possible by carefully control the energy density of the laser. For example, under the high energy density conditions, PBF-built parts showed 46% higher tensile strength but more than 75% lower impact strength than the wrought products. However, by optimizing the energy density of the laser of the metal AM system, it has been confirmed that it is possible to manufacture metal parts that can satisfy both strength and ductility, and thus it is expected to be actively applied in the field of electric power section soon.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.7-15
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• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.28 no.5
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• pp.217-221
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• 2018

Selective laser melting (SLM) technique is one of the additive manufacturing processes, in which functional, complex parts can be directly manufactured by selective melting layers of powder. SLM technique has received great attention due to offering a facile part-manufacturing route and utilizing a hard-to-manufacturing material (e.g. Ti6Al4V). The SLM process allows the accurate fabrication of near-net shaped parts and the significant reduction in the consumption of raw materials when compared to the traditional manufacturing processes such as casting and/or forging. In this study, we focus the high-speed additive manufacturing of Ti6Al4V parts in the aspect of manufacturing time, controlling various process parameters.

• The Journal of Korean Academy of Prosthodontics
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• v.57 no.4
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• pp.483-489
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• 2019

With development of digital dentistry, the 3-dimensional (3D) manufacturing industry using computer-aided design and computer-aided manufacturing (CAD/CAM) has grown dramatically in recent years. Denture fabrication using digital method is also increasing due to the recent development of digital technology in dentistry. The 3D manufacturing process can be categorized into 2 types: subtractive manufacturing (SM) and additive manufacturing (AM). SM, such as milling is based on cutting away from a solid block of materal. AM, such as 3D printing, is based on adding the material layer by layer. AM enables the fabrication of complex structures that are difficult to mill. In this case, additive manufacturing method was applied to the fabrication of the resin-based complete denture to a 80 year-old patient. During the follow-up periods, the denture using digital method has provided satisfactory results esthetically and functionally.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.337-344
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• 2018

Additive manufacturing (AM) is an alternative metal fabrication technology. The outstanding advantage of AM (3D-printing, direct manufacturing), is the ability to form shapes that cannot be formed with any other traditional technology. 3D-printing began as a new method of prototyping in plastics. Nowadays, AM in metals allows to realize not only net-shape geometry, but also high fatigue strength and corrosion resistant parts. This success of AM in metals enables new applications of the technology in important fields, such as production of medical implants. The 3D-printing of medical implants is an extremely rapidly developing application. The success of this development lies in the fact that patient-specific implants can promote patient recovery, as often it is the only alternative to amputation. The production of AM implants provides a relatively fast and effective solution for complex surgical cases. However, there are still numerous challenging open issues in medical 3D-printing. The goal of the current research review is to explain the whole technological and design chain of bio-medical bone implant production from the computed tomography that is performed by the surgeon, to conversion to a computer aided drawing file, to production of implants, including the necessary post-processing procedures and certification. The current work presents examples that were produced by joint work of Polygon Medical Engineering, Russia and by TechMed, the AM Center of Israel Institute of Metals. Polygon provided 3D-planning and 3D-modelling specifically for the implants production. TechMed were in charge of the optimization of models and they manufactured the implants by Electron-Beam Melting ($EBM^{(R)}$), using an Arcam $EBM^{(R)}$ A2X machine.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.119-124
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• 2022

Recently a method of constructing architectural products using additive manufacturing technology has been proposed. The additive manufacturing technology automates the construction process and it can secure the safety of workers. In addition, due to the high implementation efficiency of atypical shapes, the applicability to the manufacturing process of buildings and infrastructure is drawing attention. Additive manufacturing technology has the ability of satisfying computer-based construction automation, resource management and construction period prediction which is required in the modern construction industry. However, the industrial application is still limited by insufficient data, standards, regulations, and operating methods. In this study, in order to analyze the applicability of architectural additive manufacturing technology, we manufacture each architectural product with two additive manufacturing systems. In addition, we apply an application of each building product into an appropriate manufacturing system through the AM production decision model. And identify problems in the manufacturing process through empirical experiments. As a result, we propose an extended additive production decision model to improve the quality of building products.

• Corrosion Science and Technology
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• v.18 no.3
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• pp.110-116
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• 2019

Resistances to pitting corrosion of additive manufactured (AM) Ti-6Al-4V alloys in 0.6 M NaBr and 0.6 M NaCl aqueous solutions were compared using micro-droplet cell techniques. With respect to the pitting corrosion resistance, this study focused on two different types of halide anions in aqueous solutions, i.e. $Br^-$ and $Cl^-$. The differences between $Br^-$ and $Cl^-$ halide anions for breakdown on passive films of AM Ti-6Al-4V alloy were explained using Langmuir adsorption model with their equilibrium adsorption coefficients. The results of the analysis showed that the lower resistance to pitting potential of AM Ti-6Al-4V alloy in $Br^-$ aqueous solution was attributed to the higher equilibrium adsorption coefficient of Br-. In addition, micro-electrochemical test results showed that the pitting corrosion resistance of dark grains in additive manufactured Ti-6Al-4V alloy was lower as compared to that of bright grains due to the larger volume of ${\alpha}^{\prime}$ phase that caused the susceptibility to pit initiation.