Determining the Advantages of a Linear Driven 3-Axis Industrial Robot by Structural and Force Analysis

Year 2023, , 174 - 183, 31.12.2023

Savaş Koç , İdris Şani

https://doi.org/10.36222/ejt.1230193

Abstract

The most important factors affecting the operation of industrial robots are the carrying capacity, the weight of the parts, the vibrations in the arms and the cost. The cost of the robot increases as the weight of the parts are increased. The forces falling on the motors and the accelerations at the end of the robot driven by the pushing force or rotational moment were measured and compared. In addition, modal analysis, forced vibration analysis and harmonic analysis of the robot were performed in two different drive systems, and the structural condition of the robot was examined. Thus, the robot designed in this study has advantages in terms of both structural and cost compared to other robot arms. In this design, the torques of the motors are very small and using smaller motors in each arm of the robot and to making the movement according to these weights reduce the cost very much. Likewise, it is seen that the robot can be operated using this design according to the accelerations measured at the end-point and the structural analysis. This robot can be applied to systems that have lower cost and need to carry more payloads.

Keywords

Linear actuator, Measuring force and torque, Acceleration, Modal analysis

References

• [1] Ertuğrul Ş, Kaya O, Eraslan H, et al. Humanoid robot arm design, simulation, kinesthetic learning, impedance control and suggestions. Journal of the Faculty of Engineering and Architecture of Gazi University. Vol. 37, No. 2, pp. 1139-1154, 2022.
• [2] Mehrpouya M, Dehghanghadikolaei A, Fotovvati B, et al. The potential of additive manufacturing in the smart factory industrial 4.0: A review. Applied Sciences. Vol. 9, No. 18, pp. 3865, 2019.
• [3] Lu Y, Xu X and Wang L. Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems. Vol. 56, pp. 312-325, 2020.
• [4] Almurib HA, Al-Qrimli HF and Kumar N. A review of application industrial robotic design. In: 2011 Ninth International Conference on ICT and Knowledge Engineering. Bangkok, Thailand, 12-13 January 2012; pp. 105-112. IEEE.
• [5] Evjemo LD, Gjerstad T, Grøtli EI, et al. Trends in smart manufacturing: Role of humans and industrial robots in smart factories. Current Robotics Reports. Vol. 1, No. 2, pp. 35-41, 2020.
• [6] Hedelind M. and Jackson M. "How to improve the use of industrial robots in lean manufacturing systems", Journal of Manufacturing Technology Management. Vol. 22, No. 7, pp. 891-905, 2011.
• [7] Acharya V, Sharma SK and Gupta SK. Analyzing the factors in industrial automation using analytic hierarchy process. Computers & Electrical Engineering. Vol. 71, pp. 877-886, 2018.
• [8] Pan Z, Polden J, Larkin N, et al. Recent progress on programming methods for industrial robots. Robotics and Computer-Integrated Manufacturing. Vol. 28, No. 2, pp. 87-94, 2012.
• [9] Bal HÇ and Erkan Ç. Industry 4.0 and competitiveness. Procedia computer science. Vol. 158, pp. 625-631, 2019.
• [10] Li Z, Li S and Luo X. An overview of calibration technology of industrial robots. IEEE/CAA Journal of Automatica Sinica. Vol. 8, No. 1, pp. 23-36, 2021.
• [11] Tezel C, Günay O and Kayisli K. Implementation of a Robot Hand Controlled with Android Software. International Journal of Engineering Science and Application. Vol. 1, No. 4, pp. 137-141, 2017.
• [12] Luo X, Liu S, Xu M, et al. On research progress and development trend for motion control problems of industrial robots. In: 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018). Shenzhen, China, 30-31 March 2018, pp. 241-246. Atlantis Press.
• [13] 13. Garcia E, Jimenez MA, De Santos PG, et al. The evolution of robotics research. IEEE Robotics & Automation Magazine. Vol. 14, No. 1, pp. 90-103, 2017.
• [14] Swevers J, Verdonck W and De Schutter J. Dynamic model identification for industrial robots. IEEE control systems magazine. Vol. 27, No. 5, pp. 58-71, 2007.
• [15] Tsitsimpelis I, Taylor CJ, Lennox B, et al. A review of ground-based robotic systems for the characterization of nuclear environments. Progress in nuclear energy. Vol. 111, pp. 109-124, 2019.
• [16] Pogliani M, Quarta D, Polino M, et al. Security of controlled manufacturing systems in the connected factory: the case of industrial robots. J Comput Virol Hack Tech. Vol. 15, pp. 161–175, 2019.
• [17] Lu Z, Chauhan A, Silva F, et al. A brief survey of commercial robotic arms for research on manipulation. In: 2012 IEEE Symposium on Robotics and Applications (ISRA). Kuala Lumpur, Malaysia, 3-5 June 2012. pp. 986-991. IEEE.
• [18] Ayyıldız M and Aşkar Ayyıldız E. Accuracy and Repeatability Performance of Prototype Linear Delta Robot. Duzce University Journal of Science and Technology. Vol. 8, No. 1, pp. 869-879, 2020.
• [19] Almurib HA, Al-Qrimli HF and Kumar N. A review of application industrial robotic design. In: 2011 Ninth International Conference on ICT and Knowledge Engineering. Bangkok, Thailand,12-13 January 2012. pp. 105-112. IEEE.
• [20] Ehsani M, Gao Y and Gay S. Characterization of electric motor drives for traction applications. In: Proceedings of the IECON’03: 29th annual conference of the IEEE industrial electronics society (IEEE Cat. No. 03CH37468). Roanoke, VA, 2–6 November 2003, pp.891–896. New York: IEEE.
• [21] Madden JDW. Mobile robots: motor challenges and materials solutions. Science. Vol. 318, pp. 1094–1097, 2007.
• [22] Liang W, Liu H, Wang K, et al. Comparative study of robotic artificial actuators and biological muscle. Advances in Mechanical Engineering. Vol. 12, No. 6, pp. 1-25, 2020.
• [23] Kapsalas CN, Sakellariou JS, Koustoumpardis PN, et al. An ARX-based method for the vibration control of flexible beams manipulated by industrial robots. Robotics and Computer-Integrated Manufacturing. Vol. 52, pp. 76-91, 2018.
• [24] Thomsen DK, Søe-Knudsen R, Balling O, et al. Vibration control of industrial robot arms by multi-mode time-varying input shaping. Mechanism and Machine Theory. Vol. 155, pp. 1-32, 2021.
• [25] Chang PH and Park HS. Time-varying input shaping technique applied to vibration reduction of an industrial robot. Control Engineering Practice. Vol. 13, No. 1, pp. 121-130, 2005.
• [26] Mohamed Z, Chee AK, Hashim AM, et al. Techniques for vibration control of a flexible robot manipulator. Robotica. Vol. 24, No. 4, pp. 499-511, 2006.
• [27] Dwivedy SK and Eberhard P. Dynamic analysis of flexible manipulators, a literature review. Mechanism and machine theory. Vol. 41, No. 7, pp. 749-777, 2006.
• [28] Telli S and Kopmaz O. Two Different Methods to Investigate Dynamic Motion of Flexible Link That Rotates About Fixed Axes. Turkish Journal of Engineering and Environmental Sciences. Vol. 24, No. 3, pp. 127-134, 2000.
• [29] Tso SK, Yang TW, Xu WL, et al. Vibration control for a flexible-link robot arm with deflection feedback. International journal of non-linear mechanics. Vol. 38, No.1, pp. 51-62, 2003.
• [30] Garcia-Perez OA, Silva-Navarro G and Peza-Solis JF. Flexible-link robots with combined trajectory tracking and vibration control. Applied Mathematical Modelling. Vol. 70, pp. 285- 298, 2019.
• [31] Sahu S, Choudhury BB and Biswal BB. A vibration analysis of a 6 axis industrial robot using FEA. Materials Today: Proceedings. Vol. 4, No. 2, pp. 2403-2410, 2017.
• [32] Pham AD and Ahn HJ. High precision reducers for industrial robots driving 4th industrial revolution: state of arts, analysis, design, performance evaluation and perspective. International journal of precision engineering and manufacturing-green technology. Vol. 5, No. 4, pp. 519-533, 2018.
• [33] Koç S and Doğan C. Manufacturing and controlling 5-axis ball screw driven industrial robot moving through G codes, Gümüşhane University Journal of Science and Technology. Vol. 12, No. 2, pp. 454-465, 2022.
• [34] Raza K, Khan T and Abbas N. Kinematic analysis and geometrical improvement of an industrial robotic arm. Journal of King Saud University-Engineering Sciences. Vol. 30, No. 3, pp. 218-223, 2018.