Towards a Data Lake for High Pressure Die Casting

Abstract

The High Pressure Die Casting (HPDC) process is characterized by a high degree of automation and therefore represents a data rich production technology. From concepts such as Industry 4.0 and the Internet of Production (IoP), it is well known that the utilization of process data can facilitate improvements in product quality and productivity. In this work, we present a concept and its first steps of implementation to enable data management via a data lake for HPDC. Our goal was to design a system capable of acquiring, transmitting and storing static as well as dynamic process variables. The measurements originate from multiple data sources based on the Open Platform Communication Unified Architecture (OPC UA) within the HPDC cell and are transmitted via a streaming pipeline implemented in Node-Red and Apache Kafka. The data are consecutively stored in a data lake for HPDC that is based on a MinIO object store. In initial tests the implemented system proved it to be reliable, flexible and scalable. On standard consumer hardware, data handling of several thousand measurements per minute is possible. The use of the visual programming language Node-Red enables swift reconfiguration and deployment of the data processing pipeline.