Automated Evaluation of Continuous and Segmented Chip Geometries Based on Image Processing Methods and a Convolutional Neural Network
1
IWF, ETH Zürich, Switzerland
2
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
Journal of Machine Engineering 2022;22(4):115-132
KEYWORDS
TOPICS
ABSTRACT
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.
REFERENCES (28)
1.
JOHNSON G.R., COOK W.H., 1983, A Constitutive Model and Data for Materials Subjected to Large Strains, High Strain Rates, and High Temperatures, Proc. 7th Inf. Sympo. Ballist., 541–547.
2.
SHROT A. BÄKER M., 2010, Is it Possible to Identify Johnson-Cook Law Parameters from Machining Simulations?, Int. J. Mater. Form., 3, 443–446, doi: 10.1007/s12289-010-0802-4.
3.
SHROT A., BÄKER M., 2012, A Study of Non-uniqueness During the Inverse Identification of Material Parameters, Procedia CIRP, 1, 72–77, doi: 10.1016/j.procir.2012.04.011.
4.
HARDT M. BERGS T., 2021, Considering Multiple Process Observables to Determine Material Model Parameters for FE-Cutting Simulations, Int. J. Adv. Manuf. Technol., 113/11–12, 3419–3431, doi: 10.1007/s00170-021-06845-6.
5.
JEMIELNIAK K., 2021, Review of New Developments in Machining of Aerospace Materials, J. Mach. Eng., 21/1, 22–55, doi: 10.36897/jme/132905.
6.
TANABE I., YAMAGAMI Y., HOSHINO H., 2020, Development of a New High-Pressure Cooling System for Machining of Difficulut-to-Machine Materials, J. Mach. Eng., 20/1, 82–97, doi: 10.36897/jme/117776.
7.
GRZESIK W., 2017, Advanced Machining Processes of Metallic Materials: Theory, Modelling and Applications, Second edition, Amsterdam, Boston, Elsevier.
8.
KHARKEVICH A. VENUVINOD P.K., 1999, Basic Geometric Analysis of 3-D Chip Forms in Metal Cutting, Int. J. Mach. Tools Manuf., 39/5, 751–769, doi: 10.1016/S0890-6955(98)00065-0.
9.
KHARKEVICH A.G., VENUVINOD P.K., 2002, Extension of Basic Geometric Analysis of 3-D Chip Forms in Metal Cutting to Chips with Obstacle-Induced Deformation, Int. J. Mach. Tools Manuf., 42/2, 201–213, doi: 10.1016/S0890-6955(01)00115-8.
10.
KOUADRI S., NECIB K., ATLATI S., HADDAG B., NOUARI M., 2013, Quantification of the Chip Segmentation in Metal Machining: Application to Machining the Aeronautical Aluminium Alloy AA2024-T351 with Cemented Carbide Tools WC-Co, Int. J. Mach. Tools Manuf., 64, 102–113, doi: 10.1016/j.ijmachtools.2012.08.006.
11.
DEVOTTA A., BENO T., LÖF R., 2017, Finite Element Modelling and Characterisation of Chip Curl in Nose Turning Process, Int. J. Mach. Mach. Mater., 19/3, 277–295, doi: 10.1504/IJMMM.2017.084009.
12.
KLIPPEL H., SÜSSMAIER S., KUFFA M., WEGENER K., 2022, Dry Cutting Experiments Database Ti6Al4V and Ck45, arXiv:2209.04197, doi: 10.48550/ARXIV.2209.04197.
13.
KLIPPEL H., 2021, Constitutive Equations for Simulation of Metal Cutting with Meshless Methods on GPU, Doctoral Thesis, ETH Zurich, doi: 10.3929/ETHZ-B-000527668.
15.
SIEGWART R., NOURBAKHSH I.R., SCARAMUZZA D., 2011, Introduction to autonomous mobile robots. Cambridge, Mass, MIT Press.
16.
MARAGOS P., SCHAFER R., 1987, Morphological filters-Part I: Their Set-Theoretic Analysis and Relations to Linear Shift-Invariant Filters, IEEE Trans. Acoust. Speech Signal Process., 35/8, 1153–1169, doi: 10.1109/TASSP.1987.1165259.
17.
OTSU N., 1979, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on System Man Cybernetics, 9, 62–66, doi:10.1109/TSMC.1979.4310076.
18.
BUDZYN G. RZEPKA J., 2020, Review of Edge Detection Algorithms for Application in Miniature Dimension Measurement Modules, J. Mach. Eng., 20/4, 74–85, doi: 10.36897/jme/130876.
19.
SUZUKI S. ABE K., 1985, Topological Structural Analysis of Digitized Binary Images by Border Following, Comput. Vis. Graph. Image Process., 30/1, 32–46, doi: 10.1016/0734-189X(85)90016-7.
20.
Chetverikov D., 2003, A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves, Computer Analysis of Images and Patterns, 2756, N. Petkov and M.A. Westenberg, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 746–753, doi: 10.1007/978-3-540-45179-2_91.
21.
ZOU Z., SHI Z., GUO Y., YE J., 2019, Object Detection in 20 Years: A Survey, arXiv:1905.05055, doi: 10.48550/ARXIV.1905.05055.
22.
COSCUN M., YILDIRIM O., UCAR A., DEMIR Y., 2017, An Overview of Popular Deep Learning Methods, Eur. J. Tech., 7/2, 165–176, 2017, doi: 10.23884/ejt.2017.7.2.11.
23.
LIU W., ANGUELOV D., Dumitru ERHAN D., et al., 2016, SSD: Single Shot MultiBox Detector, Computer Vision – ECCV, 9905, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing, 21–37. doi: 10.1007/978-3-319-46448-0_2.
24.
SIMONYAN K. ZISSERMAN A., 2014, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv:1409.1556, doi: 10.48550/ARXIV.1409.1556.
25.
SMITH R., 2007, An Overview of the Tesseract OCR Engine’, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) 2, Curitiba, Parana, Brazil, 629–633. doi: 10.1109/ICDAR.2007.4376991.
26.
SAVITZKY A., GOLAY M.J.E., 1964, Smoothing and Differentiation of Data by Simplified Least Squares Procedures, Anal. Chem., 36/8, 1627–1639, doi: 10.1021/ac60214a047.
27.
BRONŠTEJN I.N., Ed., 2006, Taschenbuch der Mathematik, 6., Vollst. überarb. und erg. Aufl., Nachdr. Frankfurt am Main, Deutsch, ISBN 3817120168, 9783817120161.
28.
ABADI M., et al., 2016, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, arXiv:1603.04467, doi: 10.48550/ARXIV.1603.04467.
CITATIONS (2):
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An attempt for numerical optimisation of a micro-groove geometry at the rake face when turning Ti6Al4V alloy with indexable inserts
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| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
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