A Nara Samattapapong creator Afzulpurkar, Nitin V. Examination Committee Huynh, Trung Luong Examination Committee Manukid Parnichkun Examination Committee Chittiporn Pupaichikul Examination committee Bohez, Erik L.J. Examination Committee Suranaree University of Technology Scholarship donor text Pathumthani Asian Institute of Technology 2016 monographic

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1 online resource The production throughput volume is a key performance index of hard disk drive manufacturing systems that need to be forecast. Because of the production throughput forecasting result is useful information for the management team to respond to any changes in production processes and resource allocation. However, a practically forecasting system for production throughput has not been described in detail yet and also a new forecasting system has not been proposed for a decade. This study describes a pragmatic procedure to develop forecasting system for production throughput in the automated test operation of the hard disc drives manufacturing factory. The procedures consist of three main stages. The first stage, selecting relevant inputs into the forecasting model was adopted mutual information method. In the second stage, generalized regression neural network was used in the forecasting model development phase. Finally, forecasting accuracy was improved by using the interval halving optimization method. The experiments were conducted on a real data set from the final testing operation of the hard disk drive manufacturing factory by using a Visual Basic Application on Microsoft Excel© to develop a preliminary forecasting system on testing and the verification process. The experimental result shows that the proposed model is superior to the performance of the current forecasting system. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechatronics, School of Engineering and Technology Data disk drives Testing Forecasting http://203.159.5.9/ait-thesis/detail.php?q=B04721 http://203.159.5.9/ait-thesis/detail.php?q=B04721 20260818170016.0