The project enhanced a data acquisition system to monitor changes in a machining system, record new components and processing parameters, and store sensor data and user input. It also aimed to enable the machine to learn from past data to improve its response to sensor input. An analysis tool for defect detection was developed to run alongside the machining process, and a strategy was implemented to evaluate and store the collected data based on its volume and relevance.
| Topic Fields | |
| Published | 2022 |
| Involved Institutes | |
| Project Type | ICNAP Research/Transfer Project |
| Responsibles |
The goal of this project was the identification of the requirements on the machine to track and store the changes inside the machining system, and automatically communicate the usage of new machine components, sensors, or processing parameters. The previously developed data acquisition system was therefore extended to detect and save the details about the connected sensors, as well as further information through the user input. In addition, the machine is to be made able to improve its reaction to sensor signals based on the knowledge gained offline. For the preparation, an extensible analysis for defect detection was developed and is executed parallelly to the machining. To support the future offline analyses, the entirety of the collected information was evaluated to decide about their mid and long term storing depending on the amount of data and their significance for this specific use case.
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