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The hard disk drive is a reliable and relatively cheap mass storage device used in every computer nowadays. In this study, one major issue affecting the product quality of the fixture inside a hard disk drive is the surface contamination of the arm finger of actuator (AFA). For economical exploitation, a primary concern is to generate a model for optimizing the process parameter settings necessary to sustain the desired cleanliness level in an ultrasonic cleaning process. Two approaches were employed to identify critical process parameters, followed by the determination of the optimal parameter settings. The former approach was a statistical design of experiments (DOE) for developing regression equations for predicting the cleanliness level and finding out the dependence of each parameter and outcome. The latter approach was in using an artificial neural network (ANN) for building prediction models. A comparative study showed that both approaches have advantages over other methods. The results obtained show a reduction in contamination of the AFA; hence it provides an aid in the improvement of product quality. [ABSTRACT FROM AUTHOR]