Identification of unnatural variation in manufacturing of hard disc drive component

Hard disc drive (HDD) is known as a main device in a computer. In order to produce a high quality HDD, the source of unnatural variation need to be identified and controlled during manufacturing operation. In this research, simulation and modeling approach was utilized for analyzing the statistical...

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Bibliographic Details
Main Authors: Masood, Ibrahim, Abdul Rahman, Norasulaini, Abdul Halim, Siti Nur Hasrat
Format: Article
Language:English
Published: ARPN Journal 2016
Subjects:
Online Access:http://eprints.uthm.edu.my/3827/1/AJ%202016%20%288%29.pdf
http://eprints.uthm.edu.my/3827/
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Institution: Universiti Tun Hussein Onn Malaysia
Language: English
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Summary:Hard disc drive (HDD) is known as a main device in a computer. In order to produce a high quality HDD, the source of unnatural variation need to be identified and controlled during manufacturing operation. In this research, simulation and modeling approach was utilized for analyzing the statistical process control (SPC) chart patterns of unnatural variation associated to its root cause error. Initially, the computer aided design (CAD) software was used to model a HDD component and to analyze the source of unnatural variation in manufacturing operation. Then, the artificial data streams for SPC were generated mathematically using MATLAB programming. The process started with normal (in-control) condition and can be followed by sudden shifts when there is a disruption of unnatural variation such as loading error, offsetting in cutting tool, and inconsistency in pneumatic pressure. The design parameters of artificial data streams can be manipulated in terms of window size (WS, length of data streams), magnitude of shifts (Sigma, size of unnatural variation), initial point of shifts (IS), and cross correlation (p) for bivariate data. The results indicated that the generation of artificial data streams can be adapted effectively to various condition of unnatural variation. Generally, this research has provided useful methodology for a quality practitioner in identifying the source of unnatural variation based on the SPC chart patterns.