Big Data, IOTA and a great application in machine tool engineering
The full article was originally published by Markus on Medium. Read the full article here.
The Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University is currently researching the “Internet of Production” — the core of the Industrial Internet of Things, which paves the way for a future era of production. A few weeks ago, the chief engineer of WZL’s Chair for Manufacturing Technology, Dr.-Ing. Daniel Trauth, spoke with Markus Gebhardt from the „public IOTA Project“ on the processes and visions in the use of IOTA in fineblanking. It was also agreed that the „public IOTA Project“ would continue to flank this process of the real IOTA application in the future.
Big Data in general
Due to the progressive digitization of all areas of life — above all communication, science and the internet — countless data is generated every day, especially measurements in advanced industrial processes create a flood of data. Dealing with this Big Data is an important step towards Industry 4.0 and the Internet of Things.
Big data in fineblanking
Specifically, the WZL of RWTH Aachen University is currently working on the application of a fineblanking machine. In fineblanking, firstly the metal is decoiled from a sheet metal coil. Afterwards, the decoiled material is fed into a straightening system, where the sheet is flattened. A fineblanking press then processes the flattened material. Up to four parts a seconds are cut-out from the flattened material. Fineblanking is very suited for mass production of security-critical components, such as brake carriers. The goal is that all parts are identical. However, this is not the case, since uncertainties in the material, the process, or even the surrounding area result in different die-rolls and clean-cuts. However, since all parts have to be identical at the end, it requires expensive post-processing steps, which one ideally wants to avoid. Engineers have tried to describe these relationships in formulas of certain process parameters, but they contain constants that have to be determined experimentally. Therefore, the approach is to capture as much data as possible from the process and to combine physical-based and data-driven approaches for the first time in fineblanking.