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2 Journal of Machine Engineering

2 2022

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Cutting force prediction of Ti6Al4V using a machine learning model of SPH orthogonal cutting process simulations

Klippel Hagen, Sanchez Eduardo Gonzalez, Isabel Margolis, Röthlin Matthias, Afrasiabi Mohamadreza, Michal Kuffa, Wegener Konrad

Journal of Machine Engineering

2022

Vol. 22, No. 1

111--123

The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.

Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network

Klippel Hagen, Pflaum Samuel, Kuffa Michal, Wegener Konrad

Journal of Machine Engineering

2022

Vol. 22, No. 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. 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.