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High material waste in the order of more than 1000 millions of Euro/year in polymer film industry provides an economic as well as environmental incentive for manufacturing optimization in the polymer film industry. Advanced complex industry processes from microelectronics to pharmaceutical industries provide huge datasets (big data) from heterogeneous multi-sensory monitoring. Process optimization, energy efficiency, yield optimization by higher data analysis, e.g., as in microelectronics could be transfered to polymer fields.  Inspired by Industry 4.0, e.g., big data method approaches, condition monitoring, recommendation or human-machine interaction should provide options to be introduced in this way. Analytical tools are available to support manufacturers in quality and yield optimization, for real-time support. As a research vehicle for the development of methods for efficient process interfacing, a particular polymer film process was investigated with focus on novelty, and anomaly detection. A process line with 160 sensory channels has been monitored for several months. 21.900 process datasets of normal condition samples consist of about 160 dimensions were investigated. Accuracies of 99% were achieved, and a first prototype of a condition monitoring GUI for process recommendation was conceived. The results now allow process problem prediction in advance of occurrence.  In future work, a broadening of the approach to other production steps and lines as well as methodological improvements starting from the sensor level with a focus towards intelligent condition conitoring and self-x properties will be pursued.

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Advanced multi-sensory process data analysis and on-line evaluation by innovative human-machine-based process monitoring and control for yield optimization in polymer film industry