PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

An approach towards parametric optimization of construction frames for Cartesian industrial robots

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents an approach to parametric optimization with response surface methodology. This process was performed based on the design of a construction frame for a Cartesian industrial robot. The presented installation is dedicated to the real industrial pick‐and place application. Firstly, the case study was described with relevant information about the components invol‐ ved. Then, the finite element model with constraints and loads, as well as the settings of the response surface op‐ timization were discussed. The simulation was presented to the reader within all the stages with necessary details. Into consideration were taken six methods of creating response surfaces. Influence on the final optimization result and prediction accuracy of each one was presented. In the end, to validate the outcomes of the process, the static structural analysis of the setup was computed. The paper compares the impact of applying different methods of response surface generation on the results of parametric optimization. Moreover, it indicates the most vulnerable fragments of dynamically loaded elements made of construction profiles. Its results may be used to select appropriate settings in similar applications, mainly for frame structures.
Słowa kluczowe
Twórcy
  • Warsaw University of Technology, Plac Politechniki 1, Warsaw, 00‑661
  • ŁUKASIEWICZ Research Network – Industrial Research Institute for Automation and Measurements PIAP, Al. Jerozolimskie 202, Warsaw, Warsaw University of Technology, 02‑486, Plac Politechniki 1, Warsaw, 00‑661
Bibliografia
  • [1] T. H. G. Megson, Structural and stress analysis, Butterworth‑Heinemann, an imprint of Elsevier: Kidlington, Oxford Cambridge, MA, 2019.
  • [2] D. G. Pavlou. “Chapter 2 ‑ Mathematical Back‑ground”. In: D. G. Pavlou, ed., Essentials of the Finite Element Method, 19–40. Academic Press, January 2015. 10.1016/B978‑0‑12‑802386‑0.00002‑5.
  • [3] P. Falkowski, B. Wittels, Z. Pilat, and M. Smater, “Capabilities of the Additive Manufacturing n Rapid Prototyping of the Grippers’ Precision Jaws”. In: R. Szewczyk, C. Zieliń ski, and M. Kaliczyńska, eds., Automation 2019, Cham, 2020,379–387, 10.1007/978‑3‑030‑13273‑6_36.
  • [4] D. G. Pavlou. “Chapter 6 ‑ Beams”. In: D. G.Pavlou, ed., Essentials of the Finite Element Method, 135–212. Academic Press, January 2015.10.1016/B978‑0‑12‑802386‑0.00006‑2.
  • [5] D. G. Pavlou. “Chapter 7 ‑ Frames”. In: D. G.Pavlou, ed., Essentials of the Finite Element Metod, 213–278. Academic Press, January 2015.10.1016/B978‑0‑12‑802386‑0.00007‑4.
  • [6] L. M. S. Pereira, T. M. Milan, and D. R. Tapia Blácido, “Using Response Surface Methodology RSM) to optimize 2G bioethanol production: A review”, Biomass and Bioenergy, vol. 151, 2021, 106166, 10.1016/j.biombioe.2021.106166.
  • [7] J. R. Hanumanthu, G. Ravindiran, R. Subramanian, and P. Saravanan, “Optimization of process conditions using RSM and ANFIS for the removal of Remazol Brilliant Orange 3R in a packed bed column”, Journal of the Indian Chemical Society, vol. 98, no. 6, 2021, 100086, 10.1016/j.jics.2021.100086.
  • [8] C. V. Rekhate and J. K. Srivastava, “Effectiveness of O3/Fe2+/H2O2 process for detoxification of heavy metals in municipal wastewater bymusing RSM”, Chemical Engineering and Processing ‑ Process Intensification, vol. 165, 2021, 108442, 10.1016/j.cep.2021.108442.
  • [9] H. Masoumi, A. Ghaemi, and H. Gilani Ghanadzadeh, “Elimination of lead from multicomponent lead‑nickel‑cadmium solution using hyper‑cross‑linked polystyrene: Experimental and RSM modeling”, Journal of Environmental Chemical Engineering, vol. 9, no. 6, 2021, 106579,10.1016/j.jece.2021.106579.
  • [10] N. Gammoudi, M. Mabrouk, T. Bouhemda,K. Nagaz, and A. Ferchichi, “Modeling and optimization of capsaicin extraction from Capsicum annuum L. using response Surface methodology (RSM), artificial neural network (ANN), and Simulink simulation”, Industrial Crops and Products, vol. 171, 2021, 113869, 10.1016/j.indcrop.2021.113869.
  • [11] V. Cipolla, K. Abu Salem, G. Palaia, V. Binante, and D. Zanetti, “A DoE‑based approach for the imlementation of structural surrogate models in the early stage design of box‑wing aircraft”, Aerospace Science and Technology, vol. 117, 2021, 106968, 10.1016/j.ast.2021.106968.
  • [12] D. Meng, Y. Li, C. He, J. Guo, Z. Lv, and P. Wu,“Multidisciplinary design for structural integrity using a collaborative optimization method based on adaptive surrogate modelling”, aterials & Design, vol. 206, 2021, 109789, 10.1016/j.matdes.2021.109789.
  • [13] H. Xu, L. Liu, and M. Zhang, “Adaptive surrogate model‑based optimization framework applied to battery pack design”,Materials & Design, vol. 195, 2020, 108938, 10.1016/j.matdes.2020.108938.
  • [14] S. K. Behera, H. Meena, S. Chakraborty, and B. C.Meikap, “Application of response surface methodology (RSM) for optimization of leaching parameters for ash reduction from low‑grade coal”, International Journal of Mining Science and Technology, vol. 28, no. 4, 2018, 621–629, 10.1016/j.ijmst.2018.04.014.
  • [15] A. Menon. “Structural Optimization Using ANSYS and Regulated Multiquadric Response Surface Model”, 2005. MS Thesis.
  • [16] C. Juarez‑Santini, M. Ornelas‑Rodriguez, J. A. Soria‑Alcaraz, A. Rojas‑Domı́nguez, H. J. Puga‑oberanes, A. Espinal, and H. Rostro‑Gonzalez, “Single Spiking Neuron Multi‑Objective Optimization for Pattern Classification”, Journal of Automation, Mobile Robotics and Intelligent Systems,vol. 14, no. 1, 2020, 73–80, 10.14313/JAMRIS/1‑2020/9.
  • [17] Y. Poma, P. Melin, C. I. González, and G. E. Martı́nez, “Optimization of Convolutional Neural Networks Using the Fuzzy Gravitational Search Algorithm”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, no. 1, 2020, 109–120, 10.14313/JAMRIS/1‑2020/12.
  • [18] L. Daniyan, E. Nwachukwu, I. Daniyan, and O. Bonaventure, “Development and Optimization of an Automated Irrigation System”, Journal of Autoation, Mobile Robotics and Intelligent Systems, vol. 13, no. 1, 2019, 37–45, 10.14313/JAMRIS_1‑2019/5.
  • [19] S. Patel, D. Israni, and P. Shah, “Path Planning Optimization and Object Placement Through Visual Servoing Technique for Robotics Application”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, no. 1, 2020, 39–47, 10.14313/JAMRIS/1‑2020/5.
  • [20] S.‑P. Zhu, B. Keshtegar, N.‑T. Trung, Z. M. Yaseen, and D. T. Bui, “Reliability‑based structural design optimization: hybridized conjugate mean value approach”, Engineering with Computers, vol. 37, no. 1, 2021, 381–394, 10.1007/s00366‑019‑00829‑7.
  • [21] J. Yan, O. A. Broesicke, X. Tong, D. Wang, D. Li, and J. C. Crittenden, “Multidisciplinary design optimization of distributed Energy generation systems: The trade‑offs between life cycle environmental and economic impacts”, Applied Energy, vol. 284, 2021, 116197,10.1016/j.apenergy.2020.116197.
  • [22] H. Shi, Y. Gao, and X. Wang, “Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method”, The International Journal of Advanced Manufacturing Technology, vol. 48, no. 9, 2010, 955–962, 10.1007/s00170‑009‑2346‑7.
  • [23] X. Liu, X. Liu, Z. Zhou, and L. Hu, “An efficient multi‑objective optimization method based on the adaptive approximation model of the radial basis function”, Structural and Multidisciplinary Optimization, vol. 63, no. 3, 2021, 1385–1403, 10.1007/s00158‑020‑02766‑2.
  • [24] N. A. Zolpakar, S. S. Lodhi, S. Pathak, and M. A. Sharma. “Application of Multi‑objective Genetic Algorithm (MOGA) Optimization in Machining Processes”. In: K. Gupta and M. K. Gupta, eds., Optimization of Manufacturing Processes, Springer Series in Advanced Manufacturing, 185–199. Springer International Publishing, Cham, 2020.
  • [25] C. Liu, W. Bu, and D. Xu, “Multi‑objective shape optimization of a plate‑in heat exchanger using CFD and multi‑objective genetic algorithm”, International Journal of Heat and Mass Transfer, vol. 111, 2017, 65–82, 10.1016/j.ijheatmasstransfer.2017.03.066.
  • [26] K. Lenin, “Active Power Loss Reduction by Novel Feral Cat Swarm Optimization Algorithm”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, no. 2, 2020, 25–29, 10.14313/JAMRIS/2‑2020/16.
  • [27] K. Lenin, “A Novel Merchant Optimization Algorithm for Solving Optimal Reactive Power Problem”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 15, no. 1, 2021, 51–56,10.14313/JAMRIS/1‑2021/7.
  • [28] F. Valdez, Y. Kawano, and P. Melin, “Toward the Best Combination of Optimization with Fuzzy Systems to Obtain the Best Solution for the GA and PSO Algorithms Using Parallel Processing”, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 14, no. 1, 2020, 55–64, 10.14313/JAMRIS/1‑2020/7.
  • [29] A. Santiago, B. Dorronsoro, A. J. Nebro, J. J. Durillo, O. Castillo, and H. J. Fraire, “A novel multi‑objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME”, Information Sciences, vol. 471, 2019, 233–251, 10.1016/j.ins.2018.09.005.
  • [30] F. Olivas, F. Valdez, P. Melin, A. Sombra, and O. Castillo, “Interval type‑2 fuzzy logic for dynamic parameter adaptation in a modified gravitational search algorithm”, Information Sciences, vol. 476, 2019, 159–175, 10.1016/j.ins.2018.10.025.
  • [31] E. Bernal, M. L. Lagunes, O. Castillo, J. Soria, and F. Valdez, “Optimization of Type‑2 Fuzzy Logic Controller Design Using the GSO and FA Algorithms”, International Journal of Fuzzy Systems, vol. 23, no. 1, 2021, 42–57, 10.1007/s40815‑020‑00976‑w.
  • [32] G. Eichfelder, “Twenty years of continuous multiobjective optimization in the twenty‑first century”, EURO Journal on Computational Optimization, vol. 9, 2021, 100014, 10.1016/j.ejco.2021.100014.
  • [33] S. Wang, G. Jian, J. Xiao, J. Wen, and Z. Zhang, “Optimization investigation on configuration parameters of spiral‑wound heat exchanger using Genetic Aggregation response Surface and Multi‑Objective Genetic Algorithm”, Applied Thermal Engineering, vol. 119, 2017, 603–609, 10.1016/j.applthermaleng.2017.03.100.
  • [34] N. Hao, Y. Feng, and H. H. Zhang, “Model Selection for High‑Dimensional Quadratic Regression via Regularization”, Journal of the American Statistical Association, vol. 113, no. 522, 2018, 615–625, 10.1080/01621459.2016.1264956.
  • [35] C. F. J. Wu and M. Hamada. “Computer Experiments”. In: Experiments: Planning, Analysis, and Optimization,Wiley Series in Probability and Statistics. Wiley, 3rd edition, February 2021.
  • [36] J. Zeng, Z.‑H. Tan, T. Matsunaga, and T. Shirai, “Generalization of Parameter Selection of SVM and LS‑SVM for Regression”, Machine Learning and Knowledge Extraction, vol. 1, no. 2, 2019, 745–755, 10.3390/make1020043.
  • [37] M. Nouioua, M. A. Yallese, R. Khettabi, S. Belhadi, M. L. Bouhalais, and F. Girardin, “Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN)”, The International Journal of Advanced Manufacturing Technology, vol. 93, no. 5, 2017, 2485–2504, 10.1007/s00170‑017‑0589‑2.
  • [38] J. Garcke. “Sparse Grids in a Nutshell”. In: J. Garcke and M. Griebel, eds., Sparse Grids and Applications, volume 88, 57–80. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.
  • [39] G. Zhang, C. Webster, M. Gunzburger, and J. Burkardt, “A Hyperspherical Adaptive Sparse‑Grid Method for High‑Dimensional Discontinuity Detection”, SIAM Journal on Numerical Analysis, vol. 53, no. 3, 2015, 1508–1536.
  • [40] T. S. Ramu. “6.19.1 Correlation Coefficient”. In:Diagnostic Testing and Life Estimation of Power Equipment. New Academic Science, Kent, 2009.
  • [41] P. D. Harvey. “4.17.3 Physical Properties”. In: P. D. Harvey, ed., Engineering properties of steel. American Society for Metals, Metals Park, Ohio, 1982.
  • [42] T. N. Nguyen, Materials and Processing Technologies, Trans Tech Publications, Limited: Zurich, 2020.
  • [43] C. F. J. Wu and M. Hamada. “Response Surface Methodology”. In: Experiments: Planning, Analysis, and Optimization, Wiley Series in Probability and Statistics. Wiley, 3 edition, February 2021.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0114d750-ed66-462c-9654-c4ee529b7b39
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.