The Use of the Six Sigma Approach to Minimize the Defective Rate from Bending Defects in Hard Disk Drive Media Disks

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The objective of this research is to reduce the defective rate from bending defects in media disks of hard disk drives by finding an optimal machine setting in the assembly process. The Six Sigma method was applied to find out the factors which statistically affected the bending value and to obtain the optimal setting of those factors. It was found that a minimal bending value was achieved with the setting of the clamp screw torque at 3.25 in-lb, the screw bit height at 3.00 mm., and the vertical force on the disk clamp and the motor at 2.50 lbs. With this optimal setting, the process capability index Cpk increased from 0.69 to 1.39, the mean bending value decreased from 5.12% to 3.43%, and the defective rate reduced from 32,219 ppm to 39 ppm.

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298-304

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June 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] S. Bianca and G. Garth, Disk failure in the real world: What does an MTTF of 1, 000, 000, 000 hours mean to you, ". In Proc of the FAST, 07 Conference on File and Storage Technology, (2007).

Google Scholar

[2] K.W. Ng, Z. Yuan and B. Liu, Spacing fluctuation induce by disk clamping Distortion, IEEE Transactions on Magnetics, vol. 39, pp.2465-2467, (2003).

DOI: 10.1109/tmag.2003.816439

Google Scholar

[3] D. Shu, F.F. Yap, B. Gu, B. Shi and D.W. Djmari, Effect of disk clamping conditions on operational shock response of hard disk drives, IEEE Transactions on Magnetics, vol. 47, pp.1874-1877, (2011).

DOI: 10.1109/tmag.2011.2147284

Google Scholar

[4] W.C. Kim, W.S. Kim and J. Chang, Optimal disk clamp design to minimize stress variation of disks in a hard drive, Journal of Mechanical Science and Technology, vol. 23, pp.2645-2651, (2009).

DOI: 10.1007/s12206-009-0717-5

Google Scholar

[5] M.S. Raisinghani, H. Ette and R. Pierce, Six Sigma: concepts, tools, and application, Industrial Management & Data System, vol. 105, pp.491-505, (2005).

DOI: 10.1108/02635570510592389

Google Scholar

[6] J. Antony, M. Kumar, and M.K. Tiwari, An application of Six Sigma methodology to reduce the engine overheating problem in an automotive company, Journal of Engineering Manufacture, vol. 209, pp.633-646, (2005).

DOI: 10.1243/095440505x32418

Google Scholar

[7] R.B. Anand, S.K. Shukla, A. Ghorpade, M.K. Tiwari and R. Shankare, Six Sigma-based approach to optimize deep drawing operation variables, International Journal of Production Research, vol. 45, pp.2365-2385, (2007).

DOI: 10.1080/00207540600702308

Google Scholar

[8] S. Coleman, Six Sigma: an opportunity for statistics and for statisticians, Significance, vol. 5, pp.94-96, (2008).

DOI: 10.1111/j.1740-9713.2008.00300.x

Google Scholar

[9] W. L. Xie, S. Y. Zhou, and Y. Hu, Parameters Optimization for Injection Molding Based on Digital Signal Processing, Applied Mechanics and Materials, vol. 433-435, pp.1890-1893, (2013).

DOI: 10.4028/www.scientific.net/amm.433-435.1890

Google Scholar

[10] Y. Wu, W. Wu, and J. Ruan, The optimization analysis of the conditions for optimal parameter combination of husker capacity by response surface method, Applied Mechanics and Materials, vol. 433-435, pp.2203-2207, (2013).

DOI: 10.4028/www.scientific.net/amm.433-435.2203

Google Scholar

[11] W. Kaewon and N. Rojanarowan, Seal strength improvement of valve-regulated lead-acid (VRLA) battery, IOSR Journal of Engineering, vol. 3, pp.39-43, (2013).

DOI: 10.9790/3021-03913943

Google Scholar

[12] P. Ruthaiputpong and N. Rojanarowan, Improvement of track zero to increase read/write area in hard disk drive assembly process, Uncertain Supply Chain Management, pp.165-176, (2013).

Google Scholar

[13] W. Sonphuak and N. Rojanarowan, Strength improvement of fibre cement product, International Journal of Industrial Engineering Computations, pp.505-516, (2013).

DOI: 10.5267/j.ijiec.2013.06.004

Google Scholar

[14] S. Kotz and C.R. Lovelace, Process Capability Indices in Theory and Practice. New York, Arnold, (1998).

Google Scholar

[15] Y. Fasser and D. Brettner, Process Improvement in the Electronic Industry. 1st ed., J. New York: Wiley & Sons, Inc, (1992).

Google Scholar

[16] A.I.A. Group, Measurement Systems Analysis, Reference Manual, Automotive Industry Action Group , 3 rd ed. Michigan, (2002).

Google Scholar

[17] A. Shahin, Integration of FMEA and the Kano model: An exploratory examination, International Journal of Quality& Reliability Management, vol. 21, pp.731-46, (2004).

DOI: 10.1108/02656710410549082

Google Scholar

[18] Q. Feng, X. Wu, J. Luo, and B. Qi, Research and Application of Special Vehicle Design Based on the FMEA, Applied Mechanics and Materials, vol. 404, pp.171-176, (2013).

DOI: 10.4028/www.scientific.net/amm.404.171

Google Scholar

[19] T.T. Allen, Statistical Quality Control and Design of Experiments and Systems. 2nd ed. New York: Springer London Dordrecht Heidelberg, (2010).

Google Scholar

[20] D.C. Montgomery, Applied Statistics and Probability for engineers. 5th ed. New York: Johm Wiley & Sons, Inc, (2011).

Google Scholar