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Smart Honeycomb Sandwich Panels With Damage Detection and Shape Recovery Functions
Okabe, Yoji,Minakuchi, Shu,Shiraishi, Nobuo,Murakami, Ken,Takeda, Nobuo The Korean Society for Composite Materials 2008 Advanced composite materials Vol.17 No.1
In this research, optical fiber sensors and shape memory alloys (SMA) were incorporated into sandwich panels for development of a smart honeycomb sandwich structure with damage detection and shape recovery functions. First, small-diameter fiber Bragg grating (FBG) sensors were embedded in the adhesive layer between a CFRP face-sheet and an aluminum honeycomb core. From the change in the reflection spectrum of the FBG sensors, the debonding between the face-sheet and the core and the deformation of the face-sheet due to impact loading could be well detected. Then, the authors developed the SMA honeycomb core and bonded CFRP face-sheets to the core. When an impact load was applied to the panel, the cell walls of the core were buckled and the face-sheet was bent. However, after the panel was heated over the reverse transformation finish temperature of the SMA, the core buckling disappeared and the deflection of the face-sheet was relieved. Hence the bending stiffness of the panel could be recovered.
Awutsadaporn Katheng,Manabu Kanazawa,Yuriko Komagamine,Maiko Iwaki,Sahaprom Namano,Shunsuke Minakuchi 대한치과보철학회 2022 The Journal of Advanced Prosthodontics Vol.14 No.1
PURPOSE. This in vitro study investigates the effect of different post-rinsing times and methods on the trueness and precision of denture base resin manufactured through stereolithography. MATERIALS AND METHODS. Ninety clear photopolymer resin specimens were fabricated and divided into nine groups (n = 10) based on rinsing times and methods. All specimens were rinsed with 99% isopropanol alcohol for 5, 10, and 15 min using three methods-automated, ultrasonic cleaning, and hand washing. The specimens were polymerized for 30 min at 40°C. For trueness, the scanned intaglio surface of each SLA denture base was superimposed on the original standard tessellation language (STL) file using best-fit alignment (n = 10). For precision, the scanned intaglio surface of the STL file in each specimen group was superimposed across each specimen (n = 45). The root mean square error (RMSE) was measured, and the data were analyzed statistically through one-way ANOVA and Tukey test (α < .05). RESULTS. The 10-min automated group exhibited the lowest RMSE. For trueness, this was significantly different from specimens in the 5-min hand-washed group (P < .05). For precision, this was significantly different from those of other groups (P < .05), except for the 15-min automated and 15-min ultrasonic groups. The color map results indicated that the 10-min automated method exhibited the most uniform distribution of the intaglio surface adaptation. CONCLUSION. The optimal postprocessing rinsing times and methods for achieving clear photopolymer resin were found to be the automated method with rinsing times of 10 and 15 min, and the ultrasonic method with a rinsing time of 15 min.
Prediction accuracy of incisal points in determining occlusal plane of digital complete dentures
Kenta Kashiwazaki,Yuriko Komagamine,Sahaprom Namano,Ji-Man Park,Maiko Iwaki,Shunsuke Minakuchi,Manabu Kanazawa 대한치과보철학회 2023 The Journal of Advanced Prosthodontics Vol.15 No.6
Purpose. This study aimed to predict the positional coordinates of incisor points from the scan data of conventional complete dentures and verify their accuracy. Materials and methods. The standard triangulated language (STL) data of the scanned 100 pairs of complete upper and lower dentures were imported into the computer-aided design software from which the position coordinates of the points corresponding to each landmark of the jaw were obtained. The x, y, and z coordinates of the incisor point (XP, YP, and ZP) were obtained from the maxillary and mandibular landmark coordinates using regression or calculation formulas, and the accuracy was verified to determine the deviation between the measured and predicted coordinate values. YP was obtained in two ways using the hamular-incisive-papilla plane (HIP) and facial measurements. Multiple regression analysis was used to predict ZP. The root mean squared error (RMSE) values were used to verify the accuracy of the XP and YP. The RMSE value was obtained after cross-validation using the remaining 30 cases of denture STL data to verify the accuracy of ZP. Results. The RMSE was 2.22 for predicting XP. When predicting YP, the RMSE of the method using the HIP plane and facial measurements was 3.18 and 0.73, respectively. Cross-validation revealed the RMSE to be 1.53. Conclusion. YP and ZP could be predicted from anatomical landmarks of the maxillary and mandibular edentulous jaw, suggesting that YP could be predicted with better accuracy with the addition of the position of the lower border of the upper lip.