In-situ prediction of the spatial surface roughness profile during slot milling

• Published in: MATEC web of conferences Vol. 368; p. 1019
• Main Authors: Peukert, Bernd, Rangaraju, Adithya, Archenti, Andreas
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2022
• Subjects: Accelerometers; data-driven modeling; Inspection; Machine learning; Machine tools; Neural networks; Numerical integration; physics-informed machine learning; Quality control; Slot milling; Surface roughness
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/202236801019

Quality inspection is traditionally considered non-productive. That is why the manufacturing industries aim to decrease inspection times to a bare minimum without sacrificing part quality. Alongside the implementation of the Industry 4.0 paradigm, data-driven in-situ quality control is a potential enabler for minimizing inspection times. In that, the surface roughness parameter prediction is the subject of a large body of research, but studies on the spatial surface roughness profile prediction are limited. This research contributes to this field by using vibration signals and physics-informed machine learning models for the in-situ prediction of the surface roughness profile. A tri-axial accelerometer mounted on the machine tool spindle is used to capture the vibrations during a slot milling process. For one tool revolution during a stable cut, the observed acceleration in the three axes and the surface roughness profile are periodic. A model is constructed to establish the correlation between the input signals and the spatial surface roughness profile by utilizing a physics-based model of the tool trajectory together with a two-layer feed-forward neural network. Furthermore, the feature engineering of denoised velocities and displacements derived by the numerical integration of the acceleration signals improves the prediction performance with overfitting. The results show a good correlation between the spatial surface roughness and the accelerometer signals.