An Attempt for an Industry 4.0 Inspired Cloud-Supported Approach for Predictive Maintenance on the Example of Refill Friction Stir Spot Welding (RFSSW)

2016-01-2125

09/27/2016

Event
SAE 2016 Aerospace Manufacturing and Automated Fastening Conference & Exhibition
Authors Abstract
Content
This paper presents an approach to how existing production systems can benefit from Industry 4.0 driven concepts. This attempt is based on a communication gateway and a cloud-based system, that hosts all algorithms and models to calculate a prediction of the tool wear. As an example we will show the Refill Friction Stir Spot Welding (RFSSW), a solid state joining technique, which is examined at the Institute of Production Engineering (LaFT) of the Helmut-Schmidt-University, University of the Federal Armed Forces Hamburg, for years.
RFSSW is a sub-section of friction welding, where a rotating tool that consists out of three parts is used to heat up material to a dough-like state. Since Refill Friction Stir Spot Welding produces a selective dot-shaped connection of overlapping materials, the production requirements are similar to riveting or resistance spot welding. In contrast to other bonding techniques, Refill Friction Stir Spot Welding can be integrated within the production process without major interferences or changes. At the LaFT we build a prototype from which we collected a big amount of data which we are now trying to analyze with methods that are known from the Industrie 4.0.
For the Industry 4.0 idea, the production environment respectively the welding equipment acts like an Internet of things device, that publishes its data to the cloud and retrieves a calculated result.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-2125
Pages
10
Citation
Hameister, H., "An Attempt for an Industry 4.0 Inspired Cloud-Supported Approach for Predictive Maintenance on the Example of Refill Friction Stir Spot Welding (RFSSW)," SAE Technical Paper 2016-01-2125, 2016, https://doi.org/10.4271/2016-01-2125.
Additional Details
Publisher
Published
Sep 27, 2016
Product Code
2016-01-2125
Content Type
Technical Paper
Language
English