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

Résumé

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 physicsinformed
machine learning models for the in-situ prediction of the surface roughness profile. A triaxial
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.