Stephan Lausterer

A Digital Data Collector – Multitool for the Shop floor

“Collect”, “Explore” and “Improve” – the IoT building blocks, the IIoT Building Blocks, from iT Engineering Software Innovations (iTE SI) work according to this three-stage principle. In this podcast episode, they demonstrate the use of their Data Collector using Manz as a practical example. Step 1: Data collection – even with a heterogeneous plant landscape. Step 2: Visualization and first derivations. Step 3: Learn from the data and add value through optimizations. The users of iTE SI’s solution are spread across the mechanical engineering sector, regardless of industry. The software company has been at the interface between machines and IT for over 20 years and accompanies the digital transformation of mechanical engineering and the manufacturing industry on the way to Industry 4.0 with its solutions and products in the manufacturing environment. They also help the Swabian engineering company Manz with the commissioning of special-purpose machines. Manz is active in lithium-ion battery technology, solar and photovoltaic cells, wet chemistry, laser processing, inspection systems, and the manufacture of various electronic components. iTE SI was brought on board with the goal of shortening the duration of special-purpose machine development up to final use on the shop floor and, once there, generating added value from data. The digitization solution collects raw data in a minimally invasive way without affecting the controls themselves. The data collector works with various interfaces and can therefore be used on a wide range of devices and machine controls, such as PLC or NC controls. The data is first pre-processed on an edge device and then cleaned and “harmonized” before being sent to the cloud – creating an all-round clean data picture. As a result, the data can be viewed visualized on a dashboard of an app. The discussed use case is about high frequency data as well as image processing data from axis positions of laser robots – drive data in control real time or laser data like pulse duration. Among other things, they enable targeted fault analysis, predictive fault avoidance and cycle time optimization. What makes the data even more valuable is that it is enriched with existing process knowledge from the customer and manufacturer. “In the end, it’s about bringing everyone’s knowledge together,” is summarized in this podcast episode.