Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network
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IWF, ETH Zürich, Switzerland
DTDS, Bühler AG, Switzerland
Submission date: 2022-09-09
Final revision date: 2022-10-10
Acceptance date: 2022-10-26
Online publication date: 2022-11-04
Publication date: 2022-12-22
Corresponding author
Hagen Klippel   

IWF, ETH Zürich, Switzerland
Journal of Machine Engineering 2022;22(4):115-132
The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes from orthogonal cutting experiments. It enables, based on image processing methods, the determination of average chip thicknesses, chip curling radii and for segmented chips the extraction of chip segmentation lengths, as well as minimum and maximum chip thicknesses. To automatically decide whether a chip at hand should be evaluated using the proposed methods for continuous or segmented chips, a convolutional neural network is proposed, which is trained using supervised learning with available images from embedded chip cross-sections. Data from manual measurements are used for comparison and validation purposes.
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