The goal of this Master’s thesis is to interlink CAD/CAM planning data with machine-generated high-frequency data. Develop a hybrid data-fusion framework that leverages both signal patterns and CAD/CAM planning data to predict machining phases and contextualize anomalies, e.g., by segmenting machining phase operations (e.g., tool engagement, cutting, air gaps) using a hybrid ML approach. An expected outcome is a framework that combines time-series machining signals (torque data) with planning data from CAD/CAM systems (feed rates, spindle speed, etc.).
This thesis will be carried out in an industry cooperation with MUST Visibility FlexCo, Graz.
Student Target Groups:
- Information and Computer Engineering (ICE)
- Electrical Engineering (EE) / Electrical Engineering Sound Engineering
- Computer Science (CS)
Thesis Type:
- Master Thesis / Master’s Project
Recommended Prior Knowledge:
- Basics of Machine Learning and AI
- Embedded Systems and Microcontrollers
- Signal Processing and Analysis
- Programming Skills (e.g., Python, C/C++)
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