Design Optimization of the Air Bearing Surface for the Optical Flying Bead

Optical Flying Head의 Air Bearing Surface 형상 최적 설계

  • 이종수 (연세대학교 기계공학부) ;
  • 김지원 (연세대학교 대학원 기계공학과)
  • Published : 2005.02.01


The systems with probe and SIL(Solid Immersion Lens) mechanisms have been researched as the technology to perform NFR(Near Field Recording). Most of them use the flying head mechanism to accomplish high recording density and fast data transfer rate. In this paper, ABS shape of flying head was optimized with the object of securing the maximum compliance ability of OFH. We suggest low different optimization processes to predict the static flying characteristics for the OFH. Two different approximation methods, regression analysis and back propagation neural network were used. And we compared the result of directly connected(between CAE and optimizer) method and two approximated optimization results. Design Optimization Tool(DOT) and ${\mu}GA$ were used as the optimizers.


Approximated Optimization;Back Propagation Neural Network;Air Bearing Surface;Regression Analysis;Design of Experiments;Global Optimization


  1. Taesun Song, Hyunck-Dong Kwon, No-Cheol Park, Yuong-Pil Park, 2002, 'Elliptic Solid Immersion Lens for NFR; Compensation for disk thickness variation and disk tilt,' Optical Memory and Optical Data Storage Topical Meeting, International Symposium on, pp.210-212
  2. Matthew A. O'Hara, Yong hu and David B. Bogy, 1996, 'Effects of Slider Sensitivity Optimization,' IEEE TRANSATIONS ON MAGNETICS, Vol. 32, No.5. pp. 3744-3746
  3. Cha, E. and Bogy, D. B., 1995, 'A Numerical Scheme for Static and Dynamic Simulation of Subambient Pressure Shaped Rail Sliders,'ASME Journal ofTribology, Vol. 117, pp. 36-46
  4. Jongsoo Lee, Seungjin Kim, 2001, 'Applications of Soft Computing Techniques in Response Surface Based Approximate Optimization,' KSME International Journal, Vol. 15 No.8, pp. 1132-1142
  5. Giunta, A. A., Balabanov, v., Haim. D., Grossman. B., Mason, W. H., Watson, L. T. and Haftka, R. T., 1997, 'Multidisciplinary Optimization of A Super-sonic Transport Using Design of Experiment Theory and Response Surface Modeling,' The Aeronautical Hournal, Vol. 101, No. 1008, pp. 347-365
  6. Krishnakumar, K., 1989, 'Micro-Genetic Algorithms for stationary and non-staionary function optimization, ' Intelligent Control and Adaptive Systems, Vol. 1196, pp. 289-296.
  7. Coello, C. A. and Pulido, G. T., 2001, 'Multiobjective Optimization Using a Micro-Genetic Algorithm,'GECCO 2001, pp. 274-296
  8. Fu, T. C. and Suzuki, S., 1999, 'Low Stiction/Low Glide Height Head-Disk Interface for High-Performance Disk Drives,' Journal of Applied Physics, Vol. 85, No.8, pp. 5600-5605
  9. Kim, W. S., Lee, J. S., 1999, 'System Decomposition Techniques in Multidisciplinary Design Optimization Problems Using Genetic Algorithms and Neural Networks,' COSEIK, Vol. 12, No.4, pp. 619-627
  10. Carpenter, W. C. and Barthelemy, J. -F. M., 1993, 'A Comparison of Polynomial Approximations and Artificial Neural Networks as Response Surface,' Structural Optimization, Vol. 5, pp. 166-174