Predictive maintenance framework for hard disk media production

Manufacturing yield control for hard disk media is getting tougher.This is proportional to the tremendous increases of its bit/inch2 storage capacity.With the significantly difficult lithography process will be involved and drastically increase in number of product total output volume that will be required in near future, conventional maintenance type is no longer feasible. This paper proposes a novel framework for the implementation of predictive maintenance in hard disk media production.A novel technique to visualize the temporal data into pattern that can be trained with machine learning algorithm is introduced. Predictive models were produced after dealing with imbalance datasets issue, ensemble datasets and data cleaning.Experimental results have indicated that the proposed framework is successful

Handling imbalance visualized pattern dataset for yield prediction

The prediction of the yield outcome in a non close loop manufacturing process can be achieved by visualizing the historical data pattern generated from the inspection machine, transform the data pattern and map it into machine learning algorithm for training, in order to automatically generate a prediction model without the visual interpretation needs to be done by human. Anyhow, the nature of manufacturing process dataset for the bad yield outcome is highly skewed where the majority class of good yield extremely outnumbers the minority class of bad yield. Comparison between the undersampling, over- sampling and SMOTE + VDM sampling technique indicates that the combination of SMOTE + VDM and undersampled dataset produced a robust classifier performance capable of handling better with different batches of prediction test data sets. Furtherance, suitable distance function for SMOTE is needed to improve class recall and minimize overfitting whilst different approach on the majority class sampling is required to improve the class precision due to information loss by the undersampling