Amirreza Baghbanpourasl, Edwin Lughofer, Pauline Meyer-Heye, Helmut Zörrer, Christian Eitzinger,
"Virtual Quality Control using bidirectional LSTM networks and gradient boosting"
: Proceedings of the IEEE international conference on industrial informatics (INDIN2019 Industrial Applications of Artificial Intelligence), Serie Proceedings of the IEEE international conference on industrial informatics (INDIN2019 Industrial App, IEEE Press, 2019
Original Titel:
Virtual Quality Control using bidirectional LSTM networks and gradient boosting
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the IEEE international conference on industrial informatics (INDIN2019 Industrial Applications of Artificial Intelligence)
Original Kurzfassung:
Quality control is usually performed at the end
of production lines. This postponed quality control, in case of
deviation of the process values from desired quantities can lead
to late detection of batches of defective products. A prediction
model allows a virtual online quality control. Observation of
any unfavourable trend or degradation in the predicted quality
can enable preventive measures. There are many cases where
quality control is performed manually and less often. This leads
to limited data which is challenging for training any machine
learning system used for purpose of prediction.
In this paper the goal is prediction of quality measure with
respect to the past history of all relevant process values such
as sensor readings. We present a machine learning model based
on bidirectional Long Short-term Memory (LSTM) recurrent
neural networks and gradient boosting. This model is applied
to a set of real world data from microfluidic chip production
plant and we show that despite limited amount of training data
compared to the large input space, it is capable of predicting
the relevant quality values, and especially their basic trends over
time including drifting phases and changing behaviors.
Sprache der Kurzfassung:
Englisch
Veröffentlicher:
IEEE Press
Serie:
Proceedings of the IEEE international conference on industrial informatics (INDIN2019 Industrial App
Forschungs-