NEW APPROACH TO EXPERIMENTAL DETERMINATION OF ROTATIONAL PIVOT BEHAVIOUR BY PHOTOGRAMMETRIC MEASUREMENT

Kristina Marković, Maja Dundović, Željko Vrcan

DOI Number
10.22190/FUME240208025M
First page
Last page

Abstract


This paper discusses an innovative approach to experimentally determine the behaviour of rotational pivot behaviour by photogrammetric measurements. The compliant rotational pivot was selected for research as it is a well understood compliant mechanism whose deformations can be calculated analytically or numerically, allowing easy verification of the results. The mechanism was redesigned for monolithic additive manufacturing with the selection of printing directions that have reduced the influence of material anisotropy. Rapid developments in image processing and computer vision have resulted in integration of photogrammetry and digital image correlation into a wide range of applications. The primary contribution of this article is in the custom designed experimental pure bending load testing setup with an additional optical displacement measurement system, which was used to study the behaviour of additively manufactured compliant mechanisms. A high quality, consumer-grade camera was used for image capture, while image processing was performed using ready-made and custom developed MATLAB tools. Also, a redesigned compliant mechanism with sufficient precision for applications in low-cost, single-use compliant precise positioning systems was developed, and it was determined whether the selected experimental method is applicable to the research of monolithic compliant rotational joints. The experimental results obtained using this method have been compared to results obtained using finite element analysis and analytical calculations, and it was shown that the results are in good concordance. Therefore, it was concluded that photogrammetric analysis aided by feature recognition is applicable to the measurement of parasitic shift of compliant mechanisms.

Keywords

Compliant mechanism, Parasitic shift, Photogrammetry, DIC, Feature detection

Full Text:

PDF

References


Jensen, B.D., Howell, L.L., 2002, The modeling of cross-axis flexural pivots, Mechanism and Machine Theory, 37(5), pp. 461–476.

Choi, Y.-j., Sreenivasan, S. V., Choi, B.J., 2008, Kinematic design of large displacement precision XY positioning stage by using cross strip flexure joints and over-constrained mechanism, Mechanism and Machine Theory, 43(6), pp. 724–737.

Liu, L., Bi, S., Yang, Q., Wang, Y., 2014, Design and experiment of generalized triple-cross-spring flexure pivots applied to the ultra-precision instruments, Review of Scientific Instruments, 85(10), 105102.

Dearden, J., Grames, C., Orr, J., Jensen, B.D., Magleby, S.P., Howell, L.L., 2018, Cylindrical cross-axis flexural pivots, Precision Engineering, 51, pp. 604–613.

Martin, J., Robert, M., 2011, Novel flexible pivot with large angular range and small center shift to be integrated into a bio-inspired robotic hand, Journal of Intelligent Material Systems and Structures, 22(13), pp. 1431–1437.

Hao, G., Yu, J., Li, H., 2016, A brief review on nonlinear modeling methods and applications of compliant mechanisms, Frontiers of Mechanical Engineering, 11(2), pp. 119–128.

Schitter, G., Thurner, P.J., Hansma, P.K., 2008, Design and input-shaping control of a novel scanner for high-speed atomic force microscopy, Mechatronics, 18(5–6), pp. 282–288.

Gonçalves Junior, L.A., Theska, R., Lepikson, H.A., Ribeiro Junior, A.S., Linß, S., Gräser, P., 2020, Theoretical and experimental investigation of performance characteristics and design aspects of cross-spring pivots, International Journal of Solids and Structures, 185–186, pp. 240–256.

Merriam, E.G., Lund, J.M., Howell, L.L., 2016, Compound joints: Behavior and benefits of flexure arrays, Precision Engineering, 45, pp. 79–89.

Merriam, E.G., Jones, J.E., Magleby, S.P., Howell, L.L., 2013, Monolithic 2 DOF fully compliant space pointing mechanism, Mechanical Sciences, 4(2), pp. 381–390.

Khurana, J., Hanks, B., Frecker, M., 2018, Design for additive manufacturing of cellular compliant mechanism using thermal history feedback, Proc. Volume 2A: 44th Design Automation Conference, American Society of Mechanical Engineers, IDETC-CIE2018, Quebec City, V02AT03A035.

Li, N., Huang, S., Zhang, G., Qin, R., Liu, W., Xiong, H., Shi, G., Blackburn, J., 2019, Progress in additive manufacturing on new materials: A review, Journal of Materials Science and Technology, 35(2), pp. 242–269.

Baumers, M., Tuck, C., Bourell, D.L., Sreenivasan, R., Hague, R., 2011, Sustainability of additive manufacturing: Measuring the energy consumption of the laser sintering process, Proc. Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(12), 2228-2239.

Banerjee, S., Sutradhar, G., Sahoo, P., 2021, Design of experiment analysis of elevated temperature wear of Mg-WC nano-composites, Reports in Mechanical Engineering, 2(1), pp. 202–211.

Guo, N., Leu, M.C., 2013, Additive manufacturing: Technology, applications and research needs, Frontiers of Mechanical Engineering, 8(3), pp. 215–243.

Abaspur Kazerouni, I., Fitzgerald, L., Dooly, G., Toal, D., 2022, A survey of state-of-the-art on visual SLAM, Expert Systems with Applications, 205, 117734.

Elyan, E., Vuttipittayamongkol, P., Johnston, P., Martin, K., McPherson, K., Moreno-García, C.F., Jayne, C., Sarker, M.M.K., 2022, Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward, Artificial Intelligence Surgery, 2(1), pp. 24–45.

Kožar, I., Plovanić, M., Sulovsky,T., 2023, Derivation matrix in mechanics – data approach, Engineering Review, 43(1), pp. 1-8.

Cheng, J., Zhang, L., Chen, Q., Hu, X., Cai, J., 2022, A review of visual SLAM methods for autonomous driving vehicles, Engineering Applications of Artificial Intelligence, 114, 104992.

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., Socher, R., 2021, Deep learning-enabled medical computer vision, NPJ Digital Medicine, 4(1), 5.

Yang, B., Yang, S., Zhu, X., Qi, M., Li, H., Lv, Z., Cheng, X., Wang, F., 2023, Computer vision technology for monitoring of indoor and outdoor environments and HVAC equipment: A Review, Sensors, 23(13), 6186.

Hashimoto, K., 2003, A review on vision-based control of robot manipulators, Advanced Robotics, 17(10), pp. 969–991.

Shahria, M.T., Sunny, M.S.H., Zarif, M.I.I., Ghommam, J., Ahamed, S.I., Rahman, M.H., 2022, A comprehensive review of vision-based robotic applications: current state, components, approaches, barriers, and potential solutions, Robotics, 11(6), 139.

Spencer, B.F., Hoskere, V., Narazaki, Y., 2019, Advances in computer vision-based civil infrastructure inspection and monitoring, Engineering, 5(2), pp. 199–222.

Tian, H., Wang, T., Liu, Y., Qiao, X., Li, Y., 2020, Computer vision technology in agricultural automation —A review, Information Processing in Agriculture, 7(1), pp. 1–19.

Mohsin Abdulazeez, A., Faizi, S., 2021, Vision-based mobile robot controllers: A scientific review, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), pp. 1563–1580.

Ryad, A.K., Atallah, A.M., Zekry, A., 2022, An accurate partial shading detection and global maximum power point tracking technique based on image processing, Engineering Review, 42(1), pp. 46-55.

Liu, W., Ribeiro, E., 2011, A survey on image-based continuum-body motion estimation, Image and Vision Computing, 29(8), pp. 509–523.

Özyeşil, O., Voroninski, V., Basri, R., Singer, A., 2017, A survey of structure from motion, Acta Numerica, 26, pp. 305–364.

Sutton, M.A., 2013, Computer vision-based, noncontacting deformation measurements in mechanics: A generational transformation, Applied Mechanics Reviews, 65(5), 050802.

Heikkila, J., Silven, O., 1997, A four-step camera calibration procedure with implicit image correction, Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, CVPR-97, San Juan, PR, pp. 1106-1112.

Liu, Y., Lv, Z., Zhang, Q., Zhao, J., Fang, Z., Gao, Z., Su, Y., 2023, Comparison study of three camera calibration methods considering the calibration board quality and 3D measurement accuracy, Experimental Mechanics, 63(2), pp. 289–307.

Percoco, G., Guerra, M.G., Sanchez Salmeron, A.J., Galantucci, L.M., 2017, Experimental investigation on camera calibration for 3D photogrammetric scanning of micro-features for micrometric resolution, International Journal of Advanced Manufacturing Technology, 91(9–12), pp. 2935–2947.

Marković, K., 2015, Analysis of Influencing Parameters in the Design of Cross-Spring Pivots, PhD Thesis, University of Rijeka, Croatia, 183 p.

Zelenika, S., De Bona, F., 2002, Analytical and experimental characterisation of high-precision flexural pivots subjected to lateral loads, Precision Engineering, 26(4), pp. 381–388.

Haringx, J.A., 1949, The cross-spring pivot as a constructional element, Flow, Turbulence and Combustion, 1(1), pp. 313–332.

Kucak, R.A., Yakar, İ., Bilgi, S., Erol, S., Dervisoglu, A., 2020, A comparative analysis of speeded up robust features (SURF) and Harris algorithms in point cloud generation, Intercontinental Geoinformation Days, 1, pp. 5–8.

Zhu, S., Ma, W., Yao, J., 2022, Global and local geometric constrained feature matching for high resolution remote sensing images, Computers and Electrical Engineering, 103,108337.

Marković, K., Zelenika, S., 2017, Optimized cross-spring pivot configurations with minimized parasitic shifts and stiffness variations investigated via nonlinear FEA, Mechanics Based Design of Structures and Machines, 45(3), pp. 380–394.


Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4