Quality improvement of a gear transmission by means of genetic algorithm

• Autorzy: Lempa Paweł , Lisowski Edward , Masui Fumito , Filo Grzegorz , Ptaszynski Michal , Domagała Mariusz , Fabiś-Domagała Joanna

Identyfikatory

DOI

10.2478/cqpi-2019-0052

• Języki publikacji: EN

Abstrakty

EN

The article deals with the issue of quality improvement of a gear transmission by optimizing its geometry with the use of genetic algorithms. The optimization method is focused on increasing productivity and efficiency of the pump and reducing its pulsation. The best results are tested on mathematical model and automatically modelled in 3D be means of PTC Creo Software. The developed solution proved to be an effective tool in the search for better results, which greatly improved parameters of pump especially reduced flow pulsation.

Słowa kluczowe

EN

optimization; genetic algorithm; gear transmission

PL

optymalizacja; algorytm genetyczny; przekładnia zębata

• Wydawca: De Gruyter
• Czasopismo: Quality Production Improvement - QPI
• Rocznik: 2019
• Tom: Vol. 1, Iss. 1
• Strony: 386--393
• Opis fizyczny: Bibliogr. 36 poz., rys.

Twórcy

autor

Lempa Paweł

• Cracow University of Technology, Faculty of Mechanical Engineering, Jana Pawla II 37, Cracow, Poland

autor

Lisowski Edward

• Cracow University of Technology, Faculty of Mechanical Engineering, Jana Pawla II 37, Cracow, Poland

autor

Masui Fumito

• Kitami Institute of Technology, Department of Computer Science, 165 Koen-cho Kitami, Hokkaido 090-8507, Japan

autor

Filo Grzegorz

• Cracow University of Technology, Faculty of Mechanical Engineering, Jana Pawla II 37, Cracow, Poland

autor

Ptaszynski Michal

• Kitami Institute of Technology, Department of Computer Science, 165 Koen-cho Kitami, Hokkaido 090-8507, Japan

autor

Domagała Mariusz

• Cracow University of Technology, Faculty of Mechanical Engineering, Jana Pawla II 37, Cracow, Poland

autor

Fabiś-Domagała Joanna

• Cracow University of Technology, Faculty of Mechanical Engineering, Jana Pawla II 37, Cracow, Poland

Bibliografia

• 1.Augustyn, E.; Kozien, M. S. 2014. A Study on Possibility to Apply Piezoelectric Actuators for Active Reduction of Torsional Beams Vibrations. Acta Physica Polonica A, 125, A164-A168.
• 2.El-Mahdy, O., Ahmed, M., Metwalli, S., 2010. Computer aided optimization of natural gas pipe networks using genetic algorithm. Applied Soft Computing, 10, 1141–50. DOI: 10.1016/j.asoc.2010.05.010
• 3.Frith, R., Scott, W., 1996. Comparison of an external gear pump wear model with test data. Wear, 196, 64–71. DOI: 10.1016/0043-1648(95)06845-7
• 4.Fuh, J., Li, W., 2005. Advances in collaborative CAD: the-state-of the art. Computer Aided Design, 37, 571 –81. DOI: 10.1016/j.cad.2004.08.005.
• 5.Gadek-Moszczak, A., Pietraszek, J., Jasiewicz, B., Sikorska, S., Wojnar, L., 2015. The bootstrap approach to the comparison of two methods applied to the evaluation of the growth index in the analysis of the digital x-ray image of a bone regenerate. New Trends in Comp. Coll. Intell., 572, 127-136. DOI: 10.1007/978-3-319-10774-5_12
• 6.Gen, M., Cheng, R., 2000. Genetic algorithms and engineering optimization Vol. 7. Wiley, Hoboken.
• 7.Goldberg, D.E., Holland, J.H., 1988. Genetic algorithms and machine learning. Machine Learning, 3, 95–9.
• 8.Grefenstette, J.J., 1986. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 16, 122–8.
• 9.Hu, Z.H., Ding, Y.S., Zhang, W.B., Yan, Q., 2008. An interactive co-evolutionary CAD system for garment pattern design. Computer Aided Design, 40, 1094–104. DOI: 10.1016/j.cad.2008.10.010
• 10.Ionel, I.I., 1986. Pumps and pumping. Elsevier, New York.
• 11.Ivantysyn, J., Ivantysynova, M., 2003. Hydrostatic pumps and motors: principles, design, performance, modelling, analysis, control and testing. Tech Books International.
• 12.Karpisz, D., Kielbus, A., 2018. Selected problems of designing modern industrial databases. MATEC Web Conf., 183, art. 01017. DOI: 10.1051/matecconf/201818301017
• 13.Kielbus, A., Karpisz, D., 2019. Risk management as a process security tool. System Safety: Human-Technical Facility-Environment, 1, 234-239. DOI: 10.2478/czoto2019-0030
• 14.Kita, E., Tanie, H., 1997. Shape optimization of continuum structures by genetic algorithm and boundary element method. Engineering Analysis with Boundary Elements, 19, 129–36. DOI: 10.1016/S0955-7997(97)00014-3
• 15.Kollek W. Pompy zebate – konstrukcja i eksploatacja. Zakład Narodowy im. Ossolinskich; 1996.
• 16.Kozien, E., Kozien, M.S., 2017. Academic governance as a determinant of efficient management of a university in Poland - legal and comparative perspective. ESD 2017: Economic and Social Development Conf., Madrid, Varazdin, 38-47.
• 17.Krenich, S., 2017. Multi-thread evolutionary computation for design optimization. Technical Transactions, 9, 197-206
• 18.Lampinen J., 2003. CAM shape optimisation by genetic algorithm. Computer Aided Design, 35, 727–37. DOI: 10.1016/S0010-4485(03)00004-6
• 19.Ladd, S.R., 1995. Genetic algorithms in C++. Hungry Minds, New York.
• 20.Langdon, W.B., Poli, R., 2002. Foundations of genetic programming. Springer.
• 21.Melanie, M., 1999. An introduction to genetic algorithms. Cambridge, Massachusetts.
• 22.Opydo, M., Kobylecki, R., Dudek, A., Bis, Z. 2016. The effect of biomass co-combustion in a CFB boiler on solids accumulation on surfaces of P91 steel tube samples. Biomass & Bioenergy, 85, 61-68. DOI: 10.1016/j.biombioe.2015.12.011
• 23.Osmera, P., Lacko, B., Peter M., 2003. Parallel Evolutionary Algorithms, 2003 IEEE Int. Symposium Computational Intelligence in Robotics and Automation, Kobe, IEEE, 1348-1353.
• 24.Pacana, J., Pacana, A., 2018. Analysis of Possibilities of Using Polymeric Materials for Testing Prototypes of Harmonic Drive. Materials Research Proceedings, 5, 61-66. DOI: 10.21741/9781945291814-11
• 25.Pal, P., Tigga, A., Kumar, A., 2005. Feature extraction from large cad databases using genetic algorithm. Computer Aided Design, 37, 545–58. DOI: 10.1016/j.cad.2004.08.002
• 26.Park, H.S., Dang, X.P., 2010. Structural optimization based on CAD–CAE integration and metamodeling techniques. Computer-Aided Design, 42, 889-902. DOI: 10.1016/j.cad.2010.06.003.
• 27.Pietraszek, J., Dwornicka, R., Krawczyk, M., Kołomycki, M., 2017. The non-parametric approach to the quantification of the uncertainty in the design of experiments modelling. UNCECOMP 2017: 2nd Int. Conf. Uncertainty Quantification in Computational Sciences and Engineering, Rhodes, NTU of Athens, 598-604. DOI: 10.7712/120217.5395.17225
• 28.Pietraszek, J., Goroshko, A., 2014. The heuristic approach to the selection of experimental design, model and valid pre-processing transformation of DoE outcome. Advanced Materials Research-Switzerland, 874, 145-149. DOI: 10.4028/www.scientific.net/AMR.874.145
• 29.Radek, N., Pasieczynski, L., Makrenek, M., Dudek, A., 2018. Mechanical Properties of Anti-Graffiti Coating Systems used in the Railway Industry. Materials Research Proceedings, 5, 243-247. DOI: 10.21741/9781945291814-43
• 30.Radek, N., Pietraszek, J., Antoszewski, B., 2014. The Average Friction Coefficient of Laser Textured Surfaces of Silicon Carbide Identified by RSM Methodology. Adv. Mat. Res.-Switz., 874, 29-34. DOI: 10.4028/www.scientific.net/AMR.874.29
• 31.Shi, X., 2011. Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy, 36, 1659–67. DOI: 10.1016/j.energy.2010.12.064
• 32.Stroustrup, B., 2000. The C++ Programming Language. The C++ Programming Language (Special Edition). Addison-Wesley, Reading.
• 33.Szczotok, A., Radek, N., Dwornicka, R., 2018. Effect of the induction hardening on microstructures of the selected steels. METAL 2018: 27th Int. Conf. Metallurgy and Materials. Ostrava, Tanger, 1264-1269.
• 34.Wang, N., Tai, K., 2010. Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms. Computers and Structures, 88, 1064–73. DOI: 10.1016/j.compstruc.2010.06.002
• 35.Wang, D., Zhang, W., Yang, J., Wang, Z., 2012. A virtual punching method for shape optimization of openings on curved panels using CAD-based boolean operations. Computer Aided Design, 44, 388–99. DOI: 10.1016/j.cad.2012.01.003
• 36.Wang, N.F., Tai, K., 2010. Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms. Computers and Structures, 88, 1064-1073. DOI: 10.1016/j.compstruc.2010.06.002

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).