Finite element modelling and characterisation of chip curl in nose turning process

Devotta, Ashwin Moris; Beňo, Tomáš; Löf, Ronnie

doi:10.1504/ijmmm.2017.084009

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2022

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“…In an attempt to get deeper process insights, the extraction of geometrical features of such chips is required. Basic geometrical definitions of the chip exist in literature [8][9][10][11], but their evaluation requires manual analyses, which is a tedious task since it is time-consuming and the reproducibility is not ensured. An approach for the automated chip thickness extraction is proposed in [4], with a limitation to numerical simulation results.…”

Section: Introductionmentioning

confidence: 99%

Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network

Klippel¹,

Pflaum²,

Kuffa³

et al. 2022

Journal of Machine Engineering

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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.

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