PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Optimization synthesis of technological parameters during manufacturing of the parts

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Technological ensuring the reliability of machine parts is realized by failing to reach the limited state of the elements of the technological system: machine – clamping device – metal-cutting tool-part. A method of optimization synthesis of parameters of technological processes of manufacturing machine parts has been developed. Testing the developed methodology, it was found that the metal cutting tool is Meanwhile, research has shown that metal cutting machine has the least influence on the formation of detailed quality-adjustable parameters from all the the weakest element of the technological system in terms of reliability and has the greatest impact on the quality of machined parts. elements of the process media "machine – clamping device – cutting tool". Finally, a concrete example is provided to demonstrate the effectiveness of the proposed method. The proposed technique has been successfully tested for the manufacturing process of the reduction-gear housing.
Rocznik
Strony
655--667
Opis fizyczny
Bibliogr. 56 poz., rys., tab.
Twórcy
  • Lviv Polytechnic National University, Department of Robotics and Integrated Mechanical Engineering Technologies, Bandera st. 12, 79013 Lviv, Ukraine
  • Lviv Polytechnic National University, Department of Robotics and Integrated Mechanical Engineering Technologies, Bandera st. 12, 79013 Lviv, Ukraine
autor
  • Ivano-Frankivsk National Technical University of Oil and Gas, Department of Computerized Mechanical Engineering, Karpatska st. 15, 76000 Ivano-Frankivsk, Ukraine
  • Kaunas University of Technology, Faculty of Mechanical Engineering and Design, Department of Production Engineering, Studentu st. 56, 51424 Kaunas, Lithuania
  • Kaunas University of Technology, Faculty of Mechanical Engineering and Design, Department of Production Engineering, Studentu st. 56, 51424 Kaunas, Lithuania
autor
  • Kielce University of Technology, Faculty of Mechatronics and Mechanical Engineering, Department Mechatronics and Armament Engineering, al. Tysiąclecia Panstwa Polskiego 7, 25-314 Kielce, Poland
Bibliografia
  • 1. Abbas, M., ElMaraghy, H. 2018. Synthesis and optimization of manufacturing systems configuration using co-platforming, CIRP Journal of Manufacturing Science and Technology 20, 51-65. https://doi.org/10.1016/j.cirpj.2017.09.006
  • 2. Arévalo-Ruedas, J.H., Espinel-Blanco, E., Florez-Solano, Е. 2021. Statistical analysis of cutting tool wear in machining centers, Journal of Physics: Conference Series 2139 012019. https://doi.org/10.1088/1742-6596/2139/1/012019
  • 3. Bazaluk, O.; Slabyi, O.; Vekeryk, V.; Velychkovych, A.; Ropyak, L.; Lozynskyi, V. 2021. A technology of hydrocarbon fluid production intensification by productive stratum drainage zone reaming, Energies 14 (12): 3514. http://dx.doi.org/10.3390/en14123514
  • 4. Bertsche B. 2008. Reliability in Automotive and Mechanical Engineering. Berlin Heidelberg: Springer-Verlag, 492p. https://doi.org/10.1007/978-3-540-34282-3
  • 5. Birolini A. 2014. Reliability Engineering: Theory and Practice. Berlin Heidelberg: Springer-Verlag. 626p. https://doi.org/10.1007/978-3-662-05409-3
  • 6. Bobalo, Y.; Seniv, M.; Yakovyna, V.; Symets, I. 2018. Method of Reliability Block Diagram Visualization and Automated Construction of Technical System Operability Condition, Advances in Intelligent Systems and Computing III, 871: 599-610. http://dx.doi.org/10.1007/978-3-030-01069-0_43
  • 7. Bobalo, Yu.,Volochiy, B., Lozynsky, O., Mandzyy, B., Ozirkovskyy, L., Fedasyuk, D., Shcherbovskykh, S., Yakovyna, V. 2013. Mathematical Models and Methods of Reliability Analysis of Radioelectronic, Electrical and Software Systems. Lviv, Lviv Polytechnic National University. 300p. [in Ukrainian]
  • 8. Braband, J. 2003. Improving the risk priority number concept, Journal of System Safety 39 (2): 21-23
  • 9. Cai, K.-Y., Hu, D.-B., Bai, C.-G., Hu, H., Jing, T. 2008. Does software reliability growth behavior follow a non-homogeneous Poisson process, Information and Software Technology 50, 1232-1247. https://doi.org/10.1016/j.infsof.2007.12.001
  • 10. Chen, Z., Chen, Z., Zhou, D., Xia, T., Pan, E. 2021. Reliability evaluation for multi-state manufacturing systems with quality-reliability dependency, Computers & Industrial Engineering 154, 107166. https://doi.org/10.1016/j.cie.2021.107166
  • 11. Crawley, F. 2020. Failure modes and effects analysis (FMEA) and failure modes, effects and criticality analysis (FMECA). In: A guide to hazard identification methods. 2th.ed. 103-109. Elsevier Inc. https://doi.org/10.1016/B978-0-12-819543-7.00012-4
  • 12. Di Bona, G.; Silvestri, A.; Forcina, A.; Petrillo, A. 2018. Total efficient risk priority number (TERPN): a new method for risk assessment, Journal of Risk Research 21 (11), 1384-1408. http://dx.doi.org/10.1080/13669877.2017.1307260
  • 13. Haken H. 2006. Information and Self–Organization. A Macroscopic Approach to Complex Systems: Third Enlarged Edition. Berlin: Springer. 258p. http://dx.doi.org/10.1007/3-540-33023-2
  • 14. Hu, Z.; Du, X. 2017. System reliability prediction with shared load and unknown component design details, AI EDAM 31(3): 223-234. http://dx.doi.org/10.1017/S0890060417000130
  • 15. International standard IEC 60812. 2006. Analysis techniques for system reliability - Procedure for failure mode and effects analysis (FMEA). 93p.
  • 16. ISO 14224:2016, Third Edition (2016-09-15). 2016. Petroleum, petrochemical and natural gas industries - Collection and exchange of reliability and maintenance data for equipment. ISO copyright office, Vernier, Geneva, Switzerland, 280p. 10/06/2016 22:31:11 MDT
  • 17. Kim, S. I.; Lee, H. Y.; Song, J. S. 2018. A study on characteristics and internal exposure evaluation of radioactive aerosols during pipe cutting in decommissioning of nuclear power plant, Nucl. Eng. Technol. 50 (7): 1088-1098. http://dx.doi.org/10.1016/j.net.2018.06.010
  • 18. Klocke F. 2011. Manufacturing Processes 1: Cutting. Berlin: Springer-Verlag. 504p. https://doi.org/10.1007/978-3-642-11979-8
  • 19. Kopei, V. B.; Onysko, O. R.; Panchuk, V. G. 2019. Component-oriented acausal modeling of the dynamical systems in Python language on the example of the model of the sucker rod string, PeerJ Computer Science 10: 227. http://dx.doi.org/10.7717/peerj-cs.227
  • 20. Korba, P., Hunady, R., Hovanec, M., Racek, B., Pavelka, P. 2021, Fatigue life analysis of an aircraft brake component to prevent damage and ensure operational safety, Engineering Failure Analysis 129, 105653. https://doi.org/10.1016/j.engfailanal.2021.105653.
  • 21. Kusiy Ya. 2021. Scientific and applied bases of technological inheritability of quality parameters for providing of operational characteristics of products: Thesis of Doctor of technical sciences. Lviv: Lviv Politechnic National University. 432p https://lpnu.ua/sites/default/files/2021/dissertation/16474/dysertdsckusyiyaroslav.pdf [in Ukrainian].
  • 22. Kusyi, Y. M.; Kuk, A. M. 2020. Investigation of the technological damageability of castings at the stage of design and technological preparation of the machine Life Cycle, Journal of Physics: Conference Series 1426. http://dx.doi.org/10.1088/1742-6596/1426/1/012034.
  • 23. Kusyi, Y.; Stupnytskyy, V. 2020. Optimization of the Technological Process Based on Analysis of Technological Damageability of Casting. In: V. Ivanov, J. Trojanowska, I. Pavlenko, J. Zajac, D. Perakovič (eds). Advances in Design, simulation and manufacturing III, 276-284. Springer Nature Switzerland AG. http://dx.doi.org/10.1007/978-3-030-50794-7_27
  • 24. Lai, R., Garg, M. 2012. A detailed study of NHPP software reliability models, Journal of Software 7(6), 1296-1306. doi:10.4304/jsw.7.6.1296-1306
  • 25. Latinovic, T.; Preradović, D.; Barz, C. R.; Pop Vadean, A.; Todić, M. 2019. Big Data as the basis for the innovative development strategy of the Industry 4.0, IOP Conference Series: Materials Science and Engineering 477(1): 012045. https://iopscience.iop.org/article/10.1088/1757-899X/477/1/012045
  • 26. Lee, C., Park, J., Choi, J., Ha, J., Lee, S. 2021. Control logic synthesis for manufacturing systems using Markov decision processes, IFACPapers On Line 54 (20), 495-502. https://doi.org/10.1016/j.ifacol.2021.11.221
  • 27. Li, X., Fang, Z., Yin, C. 2020. A machine tool matching method in cloud manufacturing using Markov decision process and cross-entropy, Robotics and Computer Integrated Manufacturing 65, 101968. https://doi.org/10.1016/j.rcim.2020.101968
  • 28. Li, Y., Liu, Q., Tong, R., Ciu, X. 2015. Shared and service-oriented CNC machining system for intelligent manufacturing process, Chinese Journal of Mechanical Engineering, 28 (6), 1100-1108. DOI: 10.3901/CJME.2015.1010.119
  • 29. Nachlas J. A. 2017. Reliability Engineering: Probabilistic Models and Maintenance Methods, 2nd ed. CRC Press, Taylor & Francis Group. 378p. https://doi.org/10.1201/9781315307596
  • 30. Nakagawa T. 2005. Maintenance Theory of Reliability. London Limited: Springer-Verlag. 269p. https://doi.org/10.1007/1-84628-221-7.
  • 31. Ostasevicius, V.; Paulauskaite-Taraseviciene, A.; Paleviciute, I., Jurenas, V., Griskevicius, P., Eidukynas, D., Kizauskiene, L. 2022. Investigation of the Robotized Incremental Metal-Sheet Forming Process with Ultrasonic Excitation. Materials 15(3): 1024. http://dx.doi.org/10.3390/ma15031024.
  • 32. Paraschos, P.D., Xanthopoulos, A.S., Koulinas, G.K., Koulouriotis, D.E. 2022. Machine learning integrated design and operational management for resilient circular manufacturing systems, Computers & Industrial Engineering 167, 107971. https://doi.org/10.1016/j.cie.2022.107971.
  • 33. Pham, H. 2003. Handbook of Reliability Engineering. Springer-Verlag London Limited. 663p. https://doi.org/10.1007/b97414
  • 34. Pham, H. 2006. System software reliability. Springer-Verlag London Limited. 442p. https://doi.org/10.1007/1-84628-295-0
  • 35. Pham, H. 2003. Software reliability and cost models: Perspectives, comparison, and practice, European Journal of Operational Research, 149 (3), 475-489. https://doi.org/10.1016/S0377-2217(02)00498-8
  • 36. Roci, M., Salehi, N., Amir, S., Shoaib-ul-Hasan, S., Asif, F.M.A., Mihelič, A., Rashid, A. 2022. Towards circular manufacturing systems implementation: A complex adaptive systems perspective using modelling and simulation as a quantitative analysis tool, Sustainable Production and Consumption 31, 97-112 https://doi.org/10.1016/j.spc.2022.01.033
  • 37. Ropyak, L. Y.; Pryhorovska, T. O.; Levchuk, K. H. 2020. Analysis of materials and modern technologies for PDC drill bit manufacturing, Progress in Physics of Metals 21 (2): 274-301. http://dx.doi.org/10.15407/ufm.21.02.274.
  • 38. Sabri-Laghaie, K., Fathi, M., Zio, E., Mazhar, M. 2022. A novel reliability monitoring scheme based on the monitoring of manufacturing quality error rates, Reliability Engineering and System Safety 217, 108065. https://doi.org/10.1016/j.ress.2021.108065
  • 39. Saez, M., Barton, K., Maturana, F., Tilbury, D.M. 2022. Modelling framework to support decision making and control of manufacturing systems considering the relationship between productivity, reliability, quality, and energy consumption, Journal of Manufacturing Systems, 62, 925-938. https://doi.org/10.1016/j.jmsy.2021.03.011
  • 40. Salonitis, K., Kolios, A. 2013. Reliability assessment of cutting tools life based on advanced approximation methods, Procedia CIRP 8, 397-402. https://doi.org/10.1016/j.procir.2013.06.123
  • 41. Shakhovska, N., Yakovyna, V., Kryvinska, N. 2020. An Improved Software Defect Prediction Algorithm Using Self-organizing Maps Combined with Hierarchical Clustering and Data Preprocessing. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12391. Springer, Cham. https://doi.org/10.1007/978-3-030-59003-1_27.
  • 42. Signoret, J.-P., Leroy, A. 2021. Reliability Assessment of Safety and Production Systems. Analysis, Modelling, Calculations and Case Studies, Springer Nature Switzerland AG. 887p. https://doi.org/10.1007/978-3-030-64708-7
  • 43. Sonsino, C. M.; Heim, R.; Melz, T. 2016. Lightweight-structural durability design by consideration of variable ampli- tude loading, International Journal of Fatigue 91: 328-336. http://dx.doi.org/10.1016/j.ijfatigue.2015.07.030.
  • 44. Sosnovskiy L.;·Sherbakov S. 2016. Mechanothermodynamics. Springer: Cham, Switzerland, 155p. DOI 10.1007/978-3-319-24981-0
  • 45. Sun, H., Liu, Y., Pan, J., Zhang, J., Ji, W. 2020. Enhancing cutting tool sustainability based on remaining useful life prediction, Journal of Cleaner Production 244, 118794. https://doi.org/10.1016/j.jclepro.2019.118794
  • 46. Tönissen, S., Rey, J., Klocke, F. 2015. Economic efficiency of manufacturing technology integration, Journal of Manufacturing Systems 37, 173-181. https://doi.org/10.1016/j.jmsy.2015.07.003
  • 47. Volochiy, B., Yakovyna, V., Mulyak, O., Kharchenko, V. 2018. Availability model of critical nuclear power plant instrumentation and control system with non-exponential software update distribution. In:, et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2017. Communications in Computer and Information Science, vol 826. Springer, Cham. https://doi.org/10.1007/978-3-319-76168-8_1
  • 48. Xie, L., Habrekke, S., Liu, Y., Lundteigen, M.A. 2019. Operational data-driven prediction for failure rates of equipment in safety instrument systems: A case study from the oil and gas industry, Journal of Loss Prevention in the Process Industries 60, 96-105. https://doi.org/10.1016/j.jlp.2019.04.004.
  • 49. Yakovenko, I.; Permyakov, A.; Prihodko, O.; Basova, Y.; Ivanova, M. 2020. Structural Optimization of Technological Layout of Modular Machine Tools. In: , et al. Advanced Manufacturing Processes. InterPartner-2019, Lecture Notes in Mechanical Engineering. Springer, Cham.: 352-363. https://doi.org/10.1007/978-3-030-40724-7_36.
  • 50. Yakovyna, V., Seniv, M., Symets, I., Sambir, N. 2020. Algorithms and software suite for reliability assessment of complex technical systems, Radio Electronics, Computer Science, Control 4, 163-177. https://doi.org/10.15588/1607-3274-2020-4-16
  • 51. Yakovyna, V., Symets, I. 2021. Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains, Procedia Computer Science 192, 447-456. https://doi.org/10.1016/j.procs.2021.08.046
  • 52. Yang, X., He, Y., Liao, R., Cai, Y., Ai, J. 2022. Integrated mission reliability modelling based on extended quality state task network for intelligent multistate manufacturing systems, Reliability Engineering and System Safety 223, 108495. https://doi.org/10.1016/j.ress.2022.108495.
  • 53. Yeung, A. W. K. 2019. The “As Low As Reasonably Achievable” (ALARA) principle: a brief historical overview and a bibliometric analysis of the most cited publications, Radioprotection 54(2): 103-109. https://doi.org/10.1051/radiopro/2019016.
  • 54. Yoshimura, М. 2007. System Design Optimization for Product Manufacturing, Concurrent Engineering 15 (4): 329-343. http://dx.doi.org/10.1177/1063293x07083087.
  • 55. Zhang, X., Ming, X. 2021. An implementation for smart manufacturing information system (SMIS) from an industrial practice survey. Computers & Industrial Engineering 151, 106938. https://doi.org/10.1016/j.cie.2020.106938
  • 56. Zio, E. 2009. Reliability engineering: old problems and new challenges, Reliability Engineering and System Safety 94: 125-149.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-322eddcb-5593-4e0b-a897-7865d304b2bd
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.