Virtual Quality control using bidirectional LSTM networks and gradient boosting 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 Shortterm 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.

Practical Framework for Advanced Qualitybased Process Control in Interlinked Manufacturing Processes As the economic manufacturing of highquality products becomes an increasingly crucial competitive factor, corresponding quality assurance measures are gaining a growing interest. Even though research interest and industrial demand are both high, there is a large gap between methological approaches and practical applicability that needs to be closed. In this paper we therefore present a practical framework for advanced qualitybased process control (AQPC) in interlinked manufacturing processes. Machine learning algorithms are used to predict the expected product quality based on recorded process parameters. That information then serves as an input for the derivation of optimal control decisions. Therefore, we formulate a mathematical optimization model including different options such as order reassignment and process parameter adaption to determine an optimal set of control decisions. We then break down the optimization into a gradual procedure that allows an applicationspecific integration into manufacturing.