• Proceedings of the CALSEC Conference
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• 1999.07a
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• pp.277-299
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• 1999

Since Chrysler Motor Co. had experienced the digital development system in the beginning of 1990's, most of leading automobile companies are trying to apply a digital information system for their own business process reengineering based upon concurrent engineering system from product planning phase. This is called as virtual DMU(Digital Mock-Up) system instead of the traditional PMU(Physical Mock-Up) system. By using the virtual prototype, all of the design requirements and system specifications can be checked, changed and optimized more quickly and more efficiently. This paper consists of five chapters for the DMU information system. In the 1$^{st}$ chapter, the principle of digital design system is suggested by using four basic modules such as product design module, process design module, manufacturing system design module and central control module. The basic scheme of DMU is introduced with the benefits of application in the chapter 2. In the chapter 3, a digital design process of new car development is explained with the detailed DMU design and design review processes. In the chapter 4, the practical DMU manufacturing techniques and applications are introduced as CAD/CAM analyses, DPA(Digital Pre-Assembly)reviews for development, production, operation and maintenance phases, digital tolerance analyses and digital factory analyses for assembling line simulation, automated robot welding processes, production jig ＆ fixtures and painting process simulation. Finally, the activities of digital design support; CAS-styling, CAE-engineering and CAT-testing are summarized for design optimization in the chapter 5. As today's automobile manufactures and related business organizations are struggling to compete in the global marketplace, they are concentrating on efficient use of DMU information system to reduce the new car development cost, to have shorten the delivery schedule and to improve product design quality. To meet the demand of those automobile industries on digital information systems, the CALS(Computer aided Acquisition and Logistics Support) and EC(Electronic Commerce)initiative has been focused as a dominant philosophy in defense ＆ commercial industries, specially automobile industries.s.

• The Journal of Advanced Prosthodontics
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• v.13 no.5
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• pp.269-280
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• 2021

PURPOSE. The objective of this study was to evaluate the effect of thickness reduction and fatigue on the failure load of monolithic zirconia crowns. MATERIALS AND METHODS. 140 CAD-CAM fabricated crowns (3Y-TZP, inCorisTZI, Dentsply-Sirona) with different ceramic thicknesses (2.0, 1.5, 1.0, 0.8, 0.5 mm, respectively, named G2, G1.5, G1, G0.8, and G0.5) were investigated. Dies of a mandibular first molar were made of composite resin. The zirconia crowns were luted with a resin composite cement (RelyX Unicem 2 Automix, 3M ESPE). Half of the specimens (n = 14 per group) were mouth-motion-fatigued (1.2 million cycles, 1.6 Hz, 200 N/ 5 - 55℃, groups named G2-F, G1.5-F, G1-F, G0.8-F, and G0.5-F). Single-load to failure was performed using a universal testing-machine. Fracture modes were analyzed. Data were statistically analyzed using a Weibull 2-parameter distribution (90% CI) to determine the characteristic strength and Weibull modulus differences among the groups. RESULTS. Three crowns (21%) of G0.8 and five crowns (36%) of G0.5 showed cracks after fatigue. Characteristic strength was the highest for G2, followed by G1.5. Intermediate values were observed for G1 and G1-F, followed by significantly lower values for G0.8, G0.8-F, and G0.5, and the lowest for G0.5-F. Weibull modulus was the lowest for G0.8, intermediate for G0.8-F and G0.5, and significantly higher for the remaining groups. Fatigue only affected G0.5-F. CONCLUSION. Reduced crown thickness lead to reduced characteristic strength, even under failure loads that exceed physiological chewing forces. Fatigue significantly reduced the failure load of 0.5 mm monolithic 3Y-TZP crowns.

• Genomics & Informatics
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• v.20 no.3
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• pp.33.1-33.17
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• 2022

Nervous necrosis virus (NNV) is a deadly infectious disease that affects several fish species. It has been found that the NNV utilizes grouper heat shock cognate protein 70 (GHSC70) to enter the host cell. Thus, blocking the virus entry by targeting the responsible protein can protect the fishes from disease. The main objective of the study was to evaluate the inhibitory potentiality of 70 compounds of Azadirachta indica (Neem plant) which has been reported to show potential antiviral activity against various pathogens, but activity against the NNV has not yet been reported. The binding affinity of 70 compounds was calculated against the GHSC70 with the docking and molecular dynamics (MD) simulation approaches. Both the docking and MD methods predict 4 (PubChem CID: 14492795, 10134, 5280863, and 11119228) inhibitory compounds that bind strongly with the GHSC70 protein with a binding affinity of -9.7, -9.5, -9.1, and -9.0 kcal/mol, respectively. Also, the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the compounds confirmed the drug-likeness properties. As a result of the investigation, it may be inferred that Neem plant compounds may act as significant inhibitors of viral entry into the host cell. More in-vitro testing is needed to establish their effectiveness.

• The Journal of Advanced Prosthodontics
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• v.14 no.4
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• pp.262-272
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• 2022

PURPOSE. This study aimed to analyze the shear bond strength between the 3D-printed denture base and the chairside relining material, according to the surface treatment. MATERIALS AND METHODS. Cylindrical specimens were prepared using DENTCA Denture Base II. The experimental groups were divided into 6 (n = 10): no surface treatment (C), Tokuyama Rebase II Normal adhesive (A), sandblasting (P), sandblasting and adhesive (PA), sandblasting and silane (PS), and the Rocatec system (PPS). After bonding the chairside relining material to the center of the specimens in a cylindrical shape, they were stored in distilled water for 24 hours. Shear bond strength was measured using a universal testing machine, and failure mode was analyzed with a scanning electron microscope. Shear bond strength values were analyzed using one-way analysis of variance, and Tukey's honest significant difference test was used for post-hoc analysis (P < .05). RESULTS. Group PPS exhibited significantly higher shear bond strength than all other groups. Groups P and PA displayed significantly higher bond strengths than the control group. There were no significant differences between groups PS and A compared to the control group. Regarding the failure mode, adhesive failure occurred primarily in groups C and A, and mixed failure mainly in groups P, PA, PS, and PPS. CONCLUSION. The shear bond strength between the 3D-printed denture base and the chairside relining material exhibited significant differences according to the surface treatment methods. It is believed that excellent adhesive strength will be obtained when the Rocatec system is applied to 3D-printed dentures in clinical practice.

• Korean Journal of Cognitive Science
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• v.9 no.1
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• pp.31-39
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• 1998

This paper describes an electronic performance support system called HELPS(Handler Electronic Learning Performence Support) for semiconductor testing e equipment. The purpose of this system is to improve productivity of operators by providing just-in-time, on-the-job, mutimedia-based system information for operational support, training, and knowledge-based trouble shooting and repair. HELPS is composed of a operation module and a trouble shooting module. The operation module uses multimedia and hypermedia to provide the detailed and easily accessible information about equipment to users. Multimedia incorporate multiple. media forms including still and video images. animations 'texts' graphics. and audio. Hypermedia a are provided through a hierarchical information structure which offers not only specific information which is needed to perform a task to experienced operators. but detailed system guidance and information to novice operators. The trouble shooting module is composed of an integrated mutimedia-supported expert system which assists operators in trouble shooting and equipment repair. After diagnosis through the use of the expert system. multimedia advice is presented to the user in either still images with text or motion sequences with sound HELPS is evaluated in term of training time and trouble shooting and repair time. It improved productivity by saving more than 30% of the total time used without the system. This s system has the potential to improve productivity when it is used with ICAIOntellignet Computer Aided Instruction) and virtual reality.

• The Journal of Korean Academy of Prosthodontics
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• v.58 no.3
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• pp.177-184
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• 2020

Purpose: The purpose of this study was to compare and evaluate the tensile bond strength of chairside reline resin to denture base resin fabricated by different methods (subtractive manufacturing, additive manufacturing, and conventional heat-curing). Materials and methods: Denture base specimens were fabricated as cuboid specimens with a width of 25 mm × length 25 mm × height 3 mm by subtractive manufacturing (VITA VIONIC BASE), additive manufacturing (NextDent Base) and conventional heat-curing (Lucitone 199). After storing the specimens in distilled water at 37℃ for 30 days and drying them, they were relined with polyethyl methacrylate (PEMA) chairside reline resin (REBASE II Normal). The subtractive and additive manufacturing groups were set as the experimental group, and the heat-curing group was set as the control group. Ten specimens were prepared for each group. After storing all bound specimens in distilled water at 37℃ for 24 hours, the tensile bond strength between denture bases and chairside reline resin was measured by a universal testing machine at a crosshead speed of 10 mm/min. The fracture pattern of each specimen was analyzed and classified into adhesive failure, cohesive failure, and mixed failure. Tensile bond strength, according to the fabrication method, was analyzed by 1-way ANOVA and Bonferroni's method (α=.05). Results: Mean tensile bond strength of the heat-curing group (2.45 ± 0.39 MPa) and subtractive manufacturing group (2.33 ± 0.39 MPa) had no significant difference (P>.999). The additive manufacturing group showed significantly lower tensile bond strength (1.23 ± 0.36 MPa) compared to the other groups (P<.001). Most specimens of heat-curing and subtractive manufacturing groups had mixed failure, but mixed failure and adhesive failure showed the same frequency in additive manufacturing group. Conclusion: The mean tensile bond strength of the subtractive manufacturing group was not significantly different from the heat-curing group. The additive manufacturing group showed significantly lower mean tensile bond strength than the other two groups.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.189-197
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• 2023

Purpose. This study aimed to compare and evaluate the shear bond strength between various temporary prostheses resin blocks fabricated by subtractive and additive manufacturing methods bonded to self-curing reline resin. Materials and methods. The experimental groups were divided into 4 groups according to the manufacturing methods of the resin block specimens and each specimen was fabricated by subtractive manufacturing (SM), additive manufacturing stereolithography apparatus manufacturing (AMS), additive manufacturing digital light processing manufacturing (AMD) and conventional self-curing (CON). To bond the resin block specimens and self-curing resin, the reline resin was injected and polymerized into the same location of each resin block using a silicone mold. The shear bond strength was measured using a universal testing machine, and the surface of the adhesive interface was examined by scanning electron microscopy. To compare between groups, one-way ANOVA was done followed by Tukey post hoc test (α = 0.05). Results. The shear bond strength showed higher values in the order of CON, SM, AMS, and AMD group. There were significant differences between CON and AMS groups, as well as between CON and AMD groups. but there were no significant differences between CON and SM groups (P > .05). There were significant differences between SM and AMD groups, but there were no significant differences between SM and AMS groups. The AMS group was significantly different from the AMD group (P < .001). The most frequent failure mode was mixed failures in CON and AMS groups, and adhesive failures in SM and AMD groups. Conclusion. The shear bond strength of SM group showed lower but not significant bond strength compared to the CON group. The additive manufacturing method groups (AMS and AMD) showed significantly lower bond strength than the CON group, with the AMD group the lowest. There was also a significant difference between the AMD and SM group.

• Journal of Korean Dental Science
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• v.12 no.1
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• pp.13-19
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• 2019

Purpose: The aim of this study was to compare the flexural strength of provisional fixed dental prostheses which was three-dimensional (3D) printed by several build directions. Materials and Methods: A metal jig with two abutment teeth and pontic space in the middle was fabricated. This jig was scanned with a desktop scanner and provisional restoration was designed on dental computer-aided design program. On the preprocessing software, the build angles of the restorations were arranged at $0^{\circ}$, $30^{\circ}$, $45^{\circ}$, $60^{\circ}$, and $90^{\circ}$ and support was added and resultant structure was sliced to a thickness of $100{\mu}m$. Processed restorations were printed with digital light processing type 3D printer using poly methyl meta acrylate-based resin. After washing and post-curing, compressive loading was applied at a speed of 1 mm/min on a metal jig fixed to a universal testing machine. The maximum pressure at which fracture occurred was measured. For the statistical analysis, build direction was set as the independent variable and fracture strength as the dependent variable. One-way analysis of variance and Tukey's post hoc analysis was conducted to compare fracture strength among groups (${\alpha}=0.05$). Result: The mean flexural strength of provisional restoration 3D printed with the build direction of $0^{\circ}$ was $1,053{\pm}168N$; it was $1,183{\pm}188N$ at $30^{\circ}$, $1,178{\pm}81N$ at $45^{\circ}$, $1,166{\pm}133N$ at $60^{\circ}$, and $949{\pm}170N$ at $90^{\circ}$. The group with a build direction of $90^{\circ}$ showed significantly lower flexural strength than other groups (P<0.05). The flexural strength was significantly higher when the build direction was $30^{\circ}$ than when it was $90^{\circ}$ (P<0.01). Conclusion: Among the build directions $0^{\circ}$, $30^{\circ}$, $45^{\circ}$, $60^{\circ}$, and $90^{\circ}$ set for 3D printing of fixed dental prosthesis, an orientation of $30^{\circ}$ is recommended as an effective build direction for 3D printing.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.