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In a hard disk media manufacturing, engineers rely on inspection machine to generate production yield temporal data that can be used for future analysis. To proactively perform process maintenance on the equipment in order to avoid unnecessary unplanned down time, they have to be able to predict the yield outcome before products arrive at the inspection machine. This paper proposes to predict the yield outcome by visualizing the historical data pattern generated from the inspection machine, transform the data pattern and trained it with machine learning algorithms. The trained visualized datasets can automatically generate a prediction model without the visual interpretation needs to be done by human.
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