Parallel Cross-section Recognition of Geometrical Features for Selected Machine Parts
More details
Hide details
Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Poland
Marcin Gołaszewski   

Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology, Poland
Submission date: 2021-07-06
Final revision date: 2021-08-20
Acceptance date: 2021-08-21
Online publication date: 2021-08-28
Increase of automation and autonomy of production is the latest trend incorporated into Industry 4.0 objectives. Production autonomy is very desirable in the field of damaged parts replacement. To fulfill this goal numerous reverse engineering systems have been developed that support geometry recognition from the 3D scan data. This study is focused on converting non-parametric geometry representation of shaft-type elements into a CAD model with a rebuilt feature tree. Algorithms are based on the analysis of parallel cross-sections. The proposed system is also capable of identification of additional geometric features typical for 2.5 axes milling such as pockets, islands and outer walls. The proposed algorithms are optimized to increase efficiency of the process. Initial identification parameters are selected with respect to defined criteria, e.g. identification accuracy, computing power and scanning accuracy. Described algorithms can be implemented in reverse engineering systems.
BATTY M., 2018, Digital Twins, Environment and Planning B: Urban Analytics and City Sci., 45, 817–820.
HAAG S., ANDERL R., 2018, Digital Twin – Proof of Concept, Manuf. Lett., 15, 64–66. 10.1016/j.mfglet.2018.02.006.
BABIC B., NESIC N., MILJKOVIC Z., 2008, A Review of Automated Feature Recognition with Rule-Based Pattern Recognition, Comput. Ind., 59, 321–337,
ABIODUN O.I., JANTAN A., OMOLARA A.E., DADA K.V., et al., 2019, Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition, IEEE Access, 7, 158820–158846, 1109/ACCESS.2019.2945545.
GHADAI S., BALU A., SARKAR S., KRISHNAMURTHY A., 2018, Learning Localized Features in 3D CAD Models for Manufacturability Analysis of Drilled Holes, Comput. Aided Geom. Des., 62, 263–275. https:// 2018.03.024.
Han J.H., PRATT M., REGLI WC., 2000, Manufacturing Feature Recognition from Solid Models: a Status Report. IEEE Trans. Robot. Autom., 16, 782–796,
SUNIL V.B., PANDE S.S., 2009, Automatic Recognition of Machining Features Using Artificial Neural Networks, Int. J. Adv. Manuf. Technol., 41, 932–947.
GONG J-H., ZHANG H., ZHANG G-F., SUN J-G., 2006, Solid Reconstruction Using Recognition of Quadric Surfaces from Orthographic Views, Comput-Aided Des., 38, 821–835,
YE X., LIU H., CHEN L., CHEN Z., PAN X., ZHANG S., 2008, Reverse Innovative Design — an Integrated Product Design Methodology, Comput-Aided Des., 40, 812–827,
WU Z., SONG S., KHOSLA A., FISHER Yu., LINGUANG Zhang., et al. 2015, 3D ShapeNets: A deep representation for volumetric shapes, IEEE Conf. Comput. Vis. Pattern Recognit. CVPR, Boston, MA, USA: IEEE, 1912–1920,
XIA Q., LI S., QIN H., HAO A., 2016, Automatic Extraction of Generic Focal Features on 3D Shapes via Random Forest Regression Analysis of Geodesics-in-Heat, Comput. Aided Geom. Des., 49, 31–43. 10.1016/j.cagd.2016.10.003.
ZHANG Z., JAISWAL P., RAI R., 2018, FeatureNet: Machining Feature Recognition Based on 3D Convolution Neural Network, CAD Comput. Aided Des., 101, 12–22.
SHI P., Qi Q., QIN Y., SCOTT PJ., JIANG X., 2020, A Novel Learning-Based Feature Recognition Method Using Multiple Sectional View Representation, J. Intell. Manuf., 31, 1291–1309.
BOLOTOV M.A., PECHENIN V.А., RUZANOV N.V., KOLCHINA E.J., 2019, Surface Recognition of Machine Parts Based on the Results of Optical Scanning, Inf. Technol. Nanotechnol, 342–349, 10.18287/1613-0073-2019-2391-342-349.
Qi C.R., SU H., MO K., GUIBAS L.J., 2017, PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Computer Vision Foundation, CVPR, 652–660.
MODABBER A., PETERS F., KNIHA K., GOLOBORODKO E., et al., 2016, Evaluation of the Accuracy of a Mobile and a Stationary System for Three-Dimensional Facial Scanning, J. Cranio-Maxillofac Surg., 44, 1719–1724,
CURTIS S.K., HARSTON S.P., MATTSON C.A., 2011, The Fundamentals of Barriers to Reverse Engineering and their Implementation into Mechanical Components, Res. Eng. Des., 22, 245–61, s00163-011-0109-6.
LEE I.D., SEO J.H., KIM Y.M., CHOI J., HAN S., YOO B., 2020, Automatic Pose Generation for Robotic 3-D Scanning of Mechanical Parts. IEEE Trans Robot, 36, 1219–1238,
BUONAMICI F., CARFAGNI M., FURFERI R., GOVERNI L., LAPINI A., VOLPE Y., 2018, Reverse Engineering of Mechanical Parts: A Template-Based Approach, J Comput. Des. Eng., 5, 145–159, 10.1016/j.jcde.2017.11.009.
LOWRY-DUDA D., 2017, On Some Variants of the Gauss Circle Problem, ArXiv170402376 Math 2017.
ZHANG X., WU F., LI Z., 2021, Application of Convolutional Neural Network to Traditional Data, Expert Syst. Appl., 168, 114185,
KIMURA A., TAKAHASHI I., TANAKA M., YASUDA N., UEDA N., YOSHIDA N., 2017, Single-Epoch Supernova Classification with Deep Convolutional Neural Networks, IEEE 37th Int. Conf. Distrib. Comput. Syst. Workshop ICDCSW, 354–359,
GANDER W., GOLUB G.H., STREBEL R., 1994, Least-Squares Fitting of Circles and Ellipses, BIT Numer. Math., 34, 558–578,
LI N., CHA J., LU Y., 2010, A Parallel Simulated Annealing Algorithm Based on Functional Feature Tree Modeling for 3D Engineering Layout Design, Appl. Soft. Comput., 10, 592–601. 2009.08.033.
TOLOUEI-RAD M., 2006, An Approach Towards Fully Integration, JAMME, 18, September-October.
MIĄDLICKI K., JASIEWICZ M., GOŁASZEWSKI M., KRÓLIKOWSKI M., POWAŁKA B. 2020, Remanufac-turing System with Chatter Suppression for CNC Turning, Sensors, 20, 5070,
JASIEWICZ M., MIĄDLICKI K., 2020, An integrated CNC system for chatter suppression in turning, Adv. Prod. Eng. Manag., 15, 318–330,