An Approach to an Intelligent Scanning of the Machine Tool Workspace
 
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Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology in Szczecin, Poland
CORRESPONDING AUTHOR
Łukasz Marchewka   

Faculty of Mechanical Engineering and Mechatronics, West Pomeranian University of Technology in Szczecin, al. Piastów 19, 70-310, Szczecin, Poland
Submission date: 2021-03-31
Final revision date: 2021-05-04
Acceptance date: 2021-05-05
Online publication date: 2021-06-10
Publication date: 2021-06-25
 
Journal of Machine Engineering 2021;21(2):60–74
 
KEYWORDS
TOPICS
ABSTRACT
Modern 3D scanners can measure the geometry with high accuracy and within a short time. In turn, currently produced CNC machine tools allow for very accurate manufacturing; however, processes beyond the machining cycle remain time-consuming. This paper presents the idea and experimental tests of the scanning system in the CNC machine, which allows to speed up on-machine measurements, align clouds of 3D data points with an accuracy close to that of the machine itself, and finally set the workpiece coordinate system for machining. This modern approach is in line with Industry 4.0, combining the terms of data processing, machine vision, manufacturing automation, and human-machine interfaces. The future implementation of the proposed system as an interchangeable tool will allow performing autonomous measurements, inspection, and supervision of the workspace, without engaging the machine operator. The system calibration and experimental results using the industrial 3D scanner and CNC machine are described.
 
REFERENCES (35)
1.
RIYADI T., YULIANTO Y.H., EFFENDY M., SARJITO ZHEN Z., PING T.L., 2018, Evaluation on a Digitized CAD Model of 3D Scanner Used in Reverse Engineering, Conference: 12th South East Asian Technical University Consortium (SEATUC), doi: 10.1109/SEATUC.2018.8788885.
 
2.
XU J., XI N., ZHANG C., SHI Q., GREGORY J., 2011, Real-Time 3D Shape Inspection System of Automotive Parts Based on Structured Light Pattern, Opt. Laser Technol., 43/1, 1–8, doi: 10.1016/j.optlastec.2010.04.008.
 
3.
PATHAK V.K., SINGH A.K., SIVADASAN M., SINGH N.K., 2018, Framework for Automated GD&T Inspection Using 3D Scanner, J. Inst. Eng. Ser. C, 99/2, 197–205, doi: 10.1007/s40032-016-0337-7.
 
4.
HUANG W., KOVACEVIC R., 2011, A Laser-Based Vision System for Weld Quality Inspection, Sensors, 11/1, 506–521, doi: 10.3390/s110100506.
 
5.
PIEDRA-CASCÓN W., MEYER M.J., METHANI M.M., REVILLA-LEÓN M., 2020, Accuracy (Trueness and Precision) of a Dual-Structured Light Facial Scanner and Interexaminer Reliability, The Journal of Prosthetic Dentistry, 124/5, 567-574, doi: 10.1016/j.prosdent. 2019.10.010.
 
6.
LIN S.C.H., DOUGLASS M.J., HOLDAWAY S.J., FLOYD B., 2010, The Application of 3D Laser Scanning Technology to the Assessment of Ordinal and Mechanical Cortex Quantification in Lithic Analysis, J. Archaeol. Sci., 37/4, 694–702, doi: 10.1016/j.jas.2009.10.030.
 
7.
ABOUHASHEM Y., DAYAL M., SAVANAH S., ŠTRKALJ G., 2015, The Application of 3D Printing in Anatomy Education, Medical Education Online, 20/1, Co-Action Publishing, doi: 10.3402/meo.v20.29847.
 
8.
APEAGYEI P.R., 2010, Application of 3D Body Scanning Technology to Human Measurement for Clothing Fit, Int. J. Digit. Content Technol., 4/7, doi: 10.4156/jdcta.vol4.issue7.6.
 
9.
KRAWIEC P., DOMEK G., WARGUŁA L., WALUŚ K., ADAMIEC J., 2018, The Application of the Optical System ATOS II for Rapid Prototyping Methods of Non-Classical Models of Cogbelt Pulleys, MATEC Web of Conferences, 157, 01010, doi: 10.1051/matecconf/201815701010.
 
10.
WAGNER M., HESS P., REITELSHÖFER S., FRANKE J., 2016, 3D Scanning of Workpieces with Cooperative Industrial Robot Arms, 47th International Symposium on Robotics, ISR, 431–438.
 
11.
ZHANG X., LI W., LIOU F., 2018, Damage Detection and Reconstruction Algorithm in Repairing Compressor Blade by Direct Metal Deposition, Int. J. Adv. Manuf. Technol., 95/5–8, 2393–2404, doi: 10.1007/s00170-017-1413-8.
 
12.
MARTÍNEZ-PELLITERO S., CUESTA E., GIGANTO S., BARREIRO J., 2018, New Procedure for Qualification of Structured Light 3D Scanners Using an Optical Feature-Based Gauge, Opt. Lasers Eng., 110, 193–206, doi: 10.1016/j.optlaseng.2018.06.002.
 
13.
EIRÍKSSON E.R., WILM J., PEDERSEN D.B., AANAES H., 2016, Precision and Accuracy Parameters in Structured Light 3-D Scanning, ISPRS, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XL-5/W8/, 7–15, doi: 10.5194/isprs-archives-xl-5-w8-7-2016.
 
14.
ADAMCZYK M., KAMIŃSKI M., SITNIK R., BOGDAN A., KARASZEWSKI M., 2014, Effect of Temperature on Calibration Quality of Structured-Light Three-Dimensional Scanners, Appl. Opt., 53/23, 51–54, doi: 10.1364/ ao.53.005154.
 
15.
ADAMCZYK M., LIBERADZKI P., SITNIK R., 2020, Temperature Compensation Method for Mechanical Base of 3D-Structured Light Scanners, Sensors (Switzerland), 20/2, 362, doi: 10.3390/s20020362.
 
16.
THE ASSOCIATION OF GERMAN ENGINEERS, 2012, VDI-Standard: VDI/VDE 2634 Optical 3-D Measuring Systems – Optical Systems Based on Area Scanning.
 
17.
JASIŃSKI D., GĘBARSKI K., 2013, Metrological Accuracy of the Non-Contact 3D Scanner According to the Standard VDI/VDE 2634 – Examples of Measurements with a Certified Polish 3D Scanner by SMARTTECH, XII Forum Inżynierskie ProCAx, 87, 8, (in Polish).
 
18.
JECIĆ S., DRVAR N., 2003, The Assessment of Structured Light and Laser Scanning Methods in 3D Shape Measurements, Proc. 4th Int. Congr. Croat. Soc. Mech., 237–244.
 
19.
GUERRA M.G., LAVECCHIA F., MAGGIPINTO G., GALANTUCCI L.M., LONGO G.A., 2019, Measuring Techniques Suitable for Verification and Repairing of Industrial Components: A Comparison Among Optical Systems, CIRP J. Manuf. Sci. Technol., 27, 114–123, doi: 10.1016/j.cirpj.2019.09.003.
 
20.
ISA M.A., LAZOGLU I., 2017, Design and Analysis of a 3D Laser Scanner, Meas. J. Int. Meas. Confed., 111, 122–133, doi: 10.1016/j.measurement.2017.07.028.
 
21.
CHROMY A., 2015, High-Accuracy Volumetric Measurements of Soft Tissues Using Robotic 3D Scanner, IFAC-PapersOnLine, 28/4, 318–323, doi: 10.1016/j.ifacol.2015.07.054.
 
22.
GESSNER A., STANIEK R., BARTKOWIAK T., 2015, Computer-Aided Alignment of Castings and Machining Optimization, Proc. Inst. Mech. Eng. Part C, J. Mech. Eng. Sci., 229/3, 485–492, doi: 10.1177/0954406214536380.
 
23.
PAJOR M., GRUDZIŃSKI M., 2015, Intelligent Machine Tool – Vision Based 3D Scanning System for Positioning of the Workpiece, Solid State Phenom., 220–221, 497–503, doi: 10.4028/www.scientific.net/SSP.220-221....
 
24.
OKARMA K., GRUDZIŃSKI M., 2012, The 3D Scanning System for the Machine Vision Based Positioning of Workpieces on the CNC Machine Tools, 17th International Conference on Methods and Models in Automation and Robotics, MMAR, 85–90, doi: 10.1109/MMAR.2012.6347906.
 
25.
YU Z., WANG T., WANG P., TIAN Y., LI H., 2019, Rapid and Precise Reverse Engineering of Complex Geometry through Multi-Sensor Data Fusion, IEEE Access, 7, 165793–165813, doi: 10.1109/ACCESS.2019.2948124.
 
26.
SONG L., SUN S., YANG Y., ZHU X., GUO Q., YANG H., 2019, A Multi-View Stereo Measurement System Based on a Laser Scanner for Fine Workpieces, Sensors, Switzerland, 19/2, 381, doi: 10.3390/s19020381.
 
28.
CHANG W.Y., HSU J.W., HSU B. Y., 2019, 3D Scanning System of Structured Light for Aiding Workpiece Position of CNC Machine Tool, Proceedings of the 2018 IEEE International Conference on Advanced Manufacturing, ICAM, 388–391, doi: 10.1109/AMCON.2018.8614757.
 
29.
SRINIVASAN H., HARRYSSON O.L.A., WYSK R.A., 2015, Automatic Part Localization in a CNC Machine Coordinate System by Means of 3D scans, Int. J. Adv. Manuf. Technol., 81/5–8, 1127–1138, doi: 10.1007/s00170-015-7178-z.
 
30.
ZHAO H., et al., 2013, The in-Situ 3D Measurement System Combined with CNC Machine Tools, International Conference on Optics in Precision Engineering and Nanotechnology icOPEN2013, 8769, 876912, doi: 10.1117/ 12.2021111.
 
31.
FRÖHLICH M., 2012, Method and Device for Determining an Effective Vent, EP 2759822 B1.
 
32.
JENS O., MANUEL K., 2015, Method, Machining Unit And Computer Program Product for the Image-Based Positioning of Workpiece Machining Operations, EP 3108311 B1.
 
33.
DOLD P., et al., 2014, Validation of an Optical System to Measure Acetabular Shell Deformation in Cadavers, Proc. Inst. Mech. Eng. Part H, J. Eng. Med., 228/8, 781–786, doi: 10.1177/0954411914546562.
 
34.
OOMORI S., NISHIDA T., KUROGI S., 2016, Point Cloud Matching Using Singular Value Decomposition, Artif. Life Robot., 21/2, 149–154, doi: 10.1007/s10015-016-0265-x.
 
35.
THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS, 2018, ASME-Standard: Y14.5 Dimensioning and Tolerancing.
 
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