The Operation of Pneumatic Impact Wrench Analysis by Fuzzy Logic System
Keywords:
Pneumatic impact wrench, Pressure sensor, Fuzzy logic system, Tightening, Untightening, Poka Yoke systemAbstract
This research utilized computational analysis to examine how a pneumatic impact wrench, a common instrument for tightening bolt assemblies in the workplace, operates. The aim of this research is to discover a new substitute that does not necessitate a thorough analysis of the tightening torque of the bolt to confirm the accuracy of bolt tightening using the pneumatic impact wrench in the production process. The findings of this study may be used to improve the error prevention system (Poka Yoke), which is in charge of ensuring the precision of the number of attempts made during bolt assembly using a pneumatic impact wrench in industrial production. The operation of the pneumatic impact wrench is examined using a fuzzy logic system to assess the instantaneous change in air pressure during the pneumatic impact wrench operation from the pressure sensor signal that detects the air pressure in the system during the pneumatic impact wrench operation. The results of this analysis are acceptable and can be used to assess the tightening or loosening cases of a pneumatic impact wrench. The results showed that it was possible to apply the fuzzy logic system’s pneumatic impact wrench analysis to industrial manufacturing.
References
Anas, E. (2016). Edge detection techniques using fuzzy logic. 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 169-173). Noida, India: IEEE. https://doi.org/10.1109/SPIN.2016.7566682
Brkić, D. (2011). A gas distribution network hydraulic problem from practice. Petroleum Science and Technology, 29(4), 366-377. http://dx.doi.org/10.1080/10916460903394003
Buemi, A., Giacalone, D., Naccari, F., & Spampinato, G. (2016). Efficient fire detection using fuzzy logic. 2016 IEEE 6th International Conference on Consumer Electronics-Berlin (ICCE-Berlin) (pp. 237-240). Berlin, Germany: IEEE. https://doi.org/10.1109/ICCE-Berlin.2016.7684764
Gougam, F., Rahmoune, C., Benazzouz, D., & Merainani, B. (2019). Bearing fault diagnosis based on feature extraction of Empirical Wavelet Transform (EWT) and Fuzzy Logic System (FLS) under variable operating conditions. Journal of Vibroengineering, 21(6), 1636-1650. https://doi.org/10.21595/jve.2019.20092
Jumahat, S., Beng, G. K., Misran, N., Islam, M. T., & Mahri, N. (2019). Automatic QRS onset detection of ECG signal using secant line slope formula. 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 132-136). Penang, Malaysia: IEEE. https://doi.org/10.1109/CSPA.2019.86959823
Karuna, M., Ganesh, Ch., Das, R. P., & Kumar, M. V. (2017). Classification of wrist movements through EMG Signals with fuzzy logic algorithm. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 2258-2261). Chennai, India: IEEE. https://doi.org/10.1109/ICECDS.2017.8389854
Khalid, M., Yusof, R., & Mokayed, H. (2011). Fusion of multi-classifiers for online signature verification using fuzzy logic inference. International Journal of Innovative Computing, Information and Control, 7(5), 2709-2726. http://www.ijicic.org/isme09-si16.pdf
Kumar, R. S., Raj, L. G. C., & Abarna, J. (2018). Analysis of fuzzy logic based fault detection for three phase induction motor drive system. 2018 4th International Conference on Electrical Energy Systems (ICEES) (pp. 700-705). Chennai, India: IEEE. https://doi.org/10.1109/ICEES.2018.8443247
Lee, Y. G., Talluri, T., & Chung, H. T. (2019). Development of impact wrench torque measurement system. 2019 IEEE International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH) (pp. 33-36). Xiamen, China: IEEE. https://doi.org/10.1109/ICACEH484242019.9042104
Marselli, C., Daudet. D., Amann, H. P., & Pellandini, F. (1998). Application of Kalman filtering to noise reduction on microsensor signals. Proceedings of the Colloque interdisciplinaire en instrumentation: C2I’98 (pp. 443-450). France: The Open University and Jisc. https://core.ac.uk/download/pdf/20641186.pdf
Mendoza, C., Schweitzer, P., & Weber, S. (2019). Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network. International Journal of Electrical Power and Energy System, 107(3), 251-263. https://doi.org/10.1016/j.ijepes.2018.11.002
Pharne, S., & Patil, A. (2017). Fault diagnosis of motor using fuzzy logic technique. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3110-3115). Chennai, India: IEEE. https://doi.org/10.1109/ICECDS.2017.8390029
Pratumsuwan, P. (2018). Fuzzy logic control. Bangkok: Se-Education Public Company Limited. (in Thai)
Ratanaprasert, C., Ananchuen, N., Chumchob, N., Supaporn, W., & Howsud, T. (2016). Integrals. In N. Ananchuen, M. Chaiya, J. Rakbud & R. Srithus (Eds.). Calculus II. Silpakorn University. http://www.math.sc.su.ac.th/web3/textbooks/cal2/Calculus2-ch1-integrals.pdf. (in Thai)
Roveda, L., Riva, D., Bucca, G., & Piga, D. (2021). External joint torques estimation for a position-controlled manipulator employing an extended Kalman Filter. 2021 18th International Conference on Ubiquitous Robots (UR) (pp. 101-107). Gangneung, Korea (South): IEEE. https://doi.org/10.1109/UR52253.2021.9494674
Sahid, D., & Alaydrus, M. (2020). Multi sensor fire detection in low voltage electrical panel using modular fuzzy logic. 2020 2nd International Conference on Broadband Communications, Wireless Sensors and Powering (BCWSP) (pp. 31-35), Yogyakarta, Indonesia: IEEE. https://doi.org/10.1109/BCWSP50066.2020.9249400
Semsri, A., Suksukont, A., Sisamut, S., & Pudtipatkosit, T. (2021). Quality control and loss reduction in the front side fender model assembly process using Poka-Yoke Techniques. RMUTP Research Journal, 15(2), 103-118. https://doi.org/10.14456/jrmutp.2021.25. (in Thai)
Somsirikarnjanakoon, T., Prasopchaichana, K., Tongjiphitak, K., Suthachewa, K., & Srikosak, K. (2018). Defect reduction in assembly process: Case study of automotive manufacturer. IE Network Conference 2018 (July 23-26). Ubon Ratchathani: IE Network. (in Thai)
Ülkir, O., Gökmen, G., & Kaplanoğlu, E. (2017). Emg signal classification using fuzzy logic. Balkan Journal of Electrical and Computer Engineering, 5(2), 97-101. https://doi.org/10.17694/bajece. 337941
Xia, Z., Xie, F., & Wang. X. (2019). Design of electric fixed torque wrench based on speed difference detection. 2019 Chinese Control and Decision Conference (CCDC) (pp. 3724-3729). Nanchang, China: IEEE. https://doi.org/10.1109/CCDC.2019.8832745.
Xu, J., Zhoa, Y., & Chen, K. (2020). Development of a high-precision digital display torque wrench. Journal of Physics: Conference Series, Volume 1626, 2020 4th International Conference on Electrical, Automation and Mechanical Engineering 21-22 June 2020 (pp. 1-6). Beijing, China: IOP Publishing Ltd. http://dx.doi.org/10.1088/1742-6596/1626/1/012138
