Optimization of correction margin in a CAD/CAM system applying neuro-logic

Research Project

Project/Area Number	11671904
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Single-year Grants
Section	一般
Research Field	Conservative dentistry
Research Institution	TOKYO DENTAL COLLEGE
Principal Investigator	TAKASE Yasuaki TOKYO DENTAL COLLEGE, DEPARTMENT OF DENTISTRY, ASSISTANT PROFESSOR, 歯学部, 講師 (60171443)
Co-Investigator(Kenkyū-buntansha)	HIRAI Yoshito TOKYO DENTAL COLLEGE, DEPARTMENT OF DENTISTRY, PROFESSOR, 歯学部, 教授 (80119742) ISHIKAWA Tatsuya TOKYO DENTAL COLLEGE, DEPARTMENT OF DENTISTRY, PROFESSOR, 歯学部, 教授 (30085708) 牛木猛雄東京歯科大学, 歯学部, 講師 (40203509)
Project Period (FY)	1999 – 2001
Project Status	Completed (Fiscal Year 2001)
Budget Amount *help	¥3,500,000 (Direct Cost: ¥3,500,000) Fiscal Year 2001: ¥600,000 (Direct Cost: ¥600,000) Fiscal Year 2000: ¥500,000 (Direct Cost: ¥500,000) Fiscal Year 1999: ¥2,400,000 (Direct Cost: ¥2,400,000)
Keywords	CAD / CAM / H-Net Neuro / cavity margin / three dimensional measurement / H-Netニューロ / 歯頸部マージン
Research Abstract	Optimization of correction margin in a CDA/CAM system applying neuro-logic As a result of comparing the die and the three dimensional point group data obtained from the same sample model using an optical three dimensional coordinate entering equipment, we learned that the measurement accuracy at axial surfaces with a large inclination and at sharp corners is degraded and that the measurement are affected by the measuring steps of the shape measuring equlpment and arrangement of the optical system including illumination and optical receiver devices. While we earned that the application of neuro-logic resulted in higher ability of correcting errors over most of the areas of a smooth surface comparing to the case of moving average, not much difference was observed between the two methods at a section connecting the shoulder type margin and axial surface and at a transitional section from axial surface to the mating surface section, and there were many cases where correction by learning effect could not be recognized. On the other hand, while correction is made with rounded corner in every case under the moving average method, more sharp edges were observed under this method although some errors were included. In curve interpolation using H-NeT, too, there were cases where partial accuracy improvement could not be observed, which indicated a need for further analysis by changing parameters. Furthermore, it is considered that improvements are required in the optical reading device and the reading method.