Earticle

Purpose: This study aimed to assess the accuracy of implant custom abutments according to the different radii of the curvature value. Methods: Two custom abutments with different radii of the curvature value were designed based on dental computer-aided design. Then, a total of 20 customized abutments, 10 of each design, were milled according to the designs. The fabricated custom abutments were scanned using a model scanner. Using Geomagic Control X (3D Systems) scanned files were superimposed on the designed files to evaluate trueness, and scanned files for each group were superimposed on each other to evaluate precision. Results: The trueness values of radius of curvature value (CV)-0.75 and CV-1.50 were 56.26±5.66 μm and 47.10±2.64 μm, respectively. A significant difference (p<0.001) was found between the groups. The precision values of CV-0.75 and CV-1.50 were 26.87±1.24 μm and 26.08±1.03 μm, respectively. No significant difference was found between the groups. Conclusion: The radius of the curvature value in the custom abutment design affected the machining accuracy.

Purpose: In the dental field, acrylic resin can be used to produce various orthodontic appliances. The manufacturing of orthodontic resin requires the insertion of dental wires. However, currently, no study has compared the mechanical properties when dental wires are inserted into the orthodontic resin. Methods: In this study, specimens measuring 3.3 mm×10 mm×65 mm were produced by inserting 0.7- and 0.9-mm wires. A dental wire was used to create a specimen with the following configurations: single wire, two wires bonded together, and two wires spaced 5 mm apart. The flexural strength, flexural modulus, proportional limit, and work of fracture of the fabricated specimens were measured using a universal testing machine. Results: The dental resin inserted with wires demonstrated increased mechanical properties including bending strength, elastic modulus, yield strength, and fracture energy. The bending strength of the fabricated orthodontic resin specimens increased as the diameter of the wire expanded. Conclusion: Thus, the insertion of dental wires into dental resin increased the strength of the manufactured orthodontic appliance.

Purpose: This study aimed to investigate whether the accuracy and internal porosity of removable partial denture frameworks differ depending on the build direction in the selective laser melting method. Methods: A partially edentulous maxillary study model was scanned, and the anterior–posterior palatal bar was then digitally designed. The angles formed between the z-axis and the path of the insertion and removal were divided into five groups: –60°, –30°, 0°, 30°, and 60°. For each group, three removable partial denture metal frameworks were fabricated and used as specimens. The inner surface of each sample was scanned and superimposed on the design file to obtain the root mean square (RMS) value, and the average RMS value of each group was measured. One sample was randomly selected from each group, and the equivalent diameter and sphericity of the pores were analyzed using industrial X-ray threedimensional computed tomography. To compare statistical differences between groups, the Kruskal–Wallis test of SPSS Statistics ver. 27.0 (IBM) was used (α=0.05). Results: The average RMS values of the whole inner surface accuracy of the specimens were in the order of –60°<0°<–30°<30°<60° (p<0.05). The equivalent diameter and sphericity of internal pores were significantly different among groups (p<0.001). Conclusion: The build orientation of the selective laser melting method influences the accuracy and internal porosity of removable partial denture frameworks.

Purpose: To evaluate the effect of post-curing on the three-dimensional (3D) accuracy of artificial teeth, denture bases, and denture base monoblock manufactured using digital light processing (DLP) technology. Methods: Using an edentulous model, a 3D design was made for complete dentures. Three groups were printed by DLP: artificial teeth, denture bases, and denture base monoblock. The models were scanned, subjected to post-curing, and scanned again. Three-dimensional analysis was performed based on the post-treatment differences among the three groups. Statistical analysis was performed using SPSS Statistics ver. 22.0 (IBM), and the Mann– Whitney U-test and Kruskal–Wallis test were employed as nonparametric tests. Results: The complete denture monoblock (CM) and complete denture artificial teeth (CA) groups showed the lowest and highest errors at 15.13 and 23.37 μm, respectively. The groups did not show significant differences (p>0.05). In the significance test among the three groups, no significance was found in the CA group; however, significant differences were found between the complete denture base (CB) and CM groups. In addition, the three groups showed significant differences (p<0.05). Conclusion: Although deformation may occur during the post-curing process, it is within the clinically acceptable range. Future comparative studies using different 3D printers and searching for ways to minimize errors through optimization of the post-curing process are warranted.

Purpose: This study aimed to generate virtual mandibular first molars using deep learning technology, specifically deep convolutional generative adversarial network (DCGAN), and evaluate the accuracy and reliability of these virtual teeth by analyzing their morphological characteristics. These morphological characteristics were classified based on various evaluation criteria, facilitating the assessment of deep learning-based dental prosthesis production’s practical applicability. Methods: Based on our previous research, 1,000 virtual mandibular first molars were generated, and based on morphological criteria, categorized as matching, non-matching, and partially matching. The generated first molars or the categorization of the generated molars were analyzed through the expert judgment of dental technicians. Results: Among the 1,000 generated virtual teeth, 143 (14.3%) met all five evaluation criteria, whereas 76 (7.6%) were judged as completely non-matching. The most frequent issue, with 781 (78.1%) instances, including some overlapping instances, was related to occlusal buccal cusp discrepancies. Conclusion: The study reveals the potential of DCGAN-generated virtual teeth as substitutes for real teeth; however, additional research and improvements in data quality are necessary to enhance accuracy. Continued data collection and refinement of generation methods can maximize the practicality and utility of deep learning-based dental prosthesis production.

Purpose: This study evaluated the potential of a vacuum-brazed dental diamond dresser as an alternative to commercially available electroplated diamond dressers and assess its suitability for clinical use. Methods: Commercially available electroplated diamond dressers were selected as the control group (n=5), whereas vacuum-brazed diamond dressers with 80 grit and 100 grit particles were fabricated as the experimental groups (n=5 each). Prepared specimens were evaluated for surface roughness, wear resistance, and abrasiveness against a wear-inducing element (ZrO2 ball). The results were reported as means and standard deviations. Statistical analyses were performed using one-way ANOVA followed by Tukey’s HSD test using SPSS Statistics ver. 23.0 (IBM), with the significance level set at 0.05. Results: Surface roughness was highest in the 80 grit group (23.99±3.62 μm) followed by the 100 grit group (20.38±1.79 μm) and the control group (17.59±0.56 μm). Wear resistance was greatest in the control group (0.0060±0.0009 g) followed by the 80 grit group (0.0018±0.0003 g) and the 100 grit group (0.0013±0.0001 g). Abrasiveness against the ZrO2 ball was highest in the 80 grit group (0.120±0.007 g) followed by the 100 grit group (0.101±0.007 g) and the control group (0.086±0.004 g). Conclusion: This study offers key insights for enhancing the performance of diamond dressers through vacuum-brazing and is expected to aid in the development of more efficient and durable diamond dressers for future applications.

Purpose: This study aimed to develop dental alumina ceramic dressers, evaluate their abrasive wear characteristics based on particle size, and identify dental dressers with optimal particle size and performance. Methods: Commercial ceramic dressers (n=5) were selected as the control group, whereas alumina ceramic dressers with 80-grit and 120-grit particles were fabricated as the experimental groups (n=5 each). Prepared specimens were assessed for surface roughness, wear resistance, and abrasiveness against a wear-inducing element (SUS304 stainless steel ball). Means and standard deviations were calculated for each test. Statistical analysis was performed using the Kruskal–Wallis H test with Bonferroni correction (α=0.05) via SPSS Statistics ver. 23.0 (IBM). Results: Surface roughness was highest in the 80-grit group (22.734±1.308 μm), followed by 120-grit group (21.804±0.785 μm), and lowest in control group (15.782±0.223 μm). Wear resistance was greatest in 120-grit group (0.007±0.002 g), followed by the 80-grit group (0.007±0.004 g), and lowest in the control group (0.370±0.010 g). Furthermore, abrasiveness against the wear-inducing element (SUS304 stainless steel ball) was highest in 120-grit group (0.017±0.003 g), followed by the 80-grit group (0.015±0.003 g), and lowest in control group (0.010±0.001 g). Conclusion: Based on the combined results of surface roughness, wear resistance, and abrasiveness against a wear-inducing element, the newly developed alumina ceramic dressers show promise for clinical application.

Purpose: This study aimed to identify the social responsibility, self-leadership, and careerpreparation behavior of fourth-year students graduating from the 4-year program of the department of dental technology and provide basic data for developing specific methods and related programs to help graduates adopt positive and active career preparation behavior. Methods: The final analysis was performed using 143 questionnaires from fourth-year students of the 4-year department of dental technology at Gyeonggi-do, Gangwon-do, and Busan Metropolitan City. T-tests, analysis of variance, correlation analysis, and linear regression analysis were performed in IBM SPSS Statistics ver. 21.0 (IBM). Results: A significant positive correlation was found (p<0.01) between social responsibility, self-leadership, and career preparation behavior factors, and the detailed factors of selfleadership that influence career preparation behavior, “attitude” (p<0.05), “natural reward strategies” (p<0.001), “behavior-focused strategies” (p<0.01), and “constructive thought pattern strategies” (p<0.01) demonstrated significant relationships. The explanatory power values of the models were 50.7% and 52.9%, respectively. Conclusion: This study concluded that to improve career preparation behaviour, social participation through various major volunteer activities is needed to ensure students feel professional responsibility and guide and encourage participation in various self-development programs during their time in university. In addition, universities and departments need to develop step-by-step and various career-preparation behavior programs for each year level.

Purpose: This study aims to examine the impact of instructor–learner interaction on online class satisfaction, perceived academic achievement, and online class preference. Methods: From December 20, 2023, to February 10, 2024, this study surveyed students in the medical and public health departments of K University and D University located in Gangwon-do and Daejeon, respectively. Results: In the online class environment, instructor–learner interaction showed a significant positive correlation with online class satisfaction, academic achievement, and online class preference. On re-examination using regression analysis, it was found that among the subfactors of instructor–learner interaction, instructional support and instructor presence had a significant impact. Conclusion: The findings suggest that in an online learning environment, instructors must make efforts to help learners identify what they need to learn by repeatedly asking whether they understand the learning content and providing appropriate feedback.