Nauki Techniczne

Archive of Mechanical Engineering

Zawartość

Archive of Mechanical Engineering | 2020 | vol. 67 | No 2

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Abstrakt

Laminated Aluminum Composite Structure (LACS) has shown great potential for replacing traditional bulk aluminum parts, due to its ability to maintain low manufacturing costs and create complex geometries. In this study, a LACS, that consists of 20 aluminum layers joined by a structural tape adhesive, was fabricated and tested to understand its impact performance. Three impact tests were conducted: axial drop, normal and transverse three-point bending drop tests. Numerical simulations were performed to predict the peak loads and failure modes during impacts. Material models with failure properties were used to simulate the cohesive failure, interfacial failure, and aluminum fracture. Various failure modes were observed experimentally (large plastic deformation, axial buckling, local wrinkling, aluminum fracture and delamination) and captured by simulations. Cross-section size of the axial drop model was varied to understand the LACS buckling direction and force response. For three-point bending drop simulations, the mechanism causing the maximum plastic strain at various locations in the aluminum and adhesive layers was discussed. This study presents an insight to understand the axial and flexural responses under dynamic loading, and the failure modes in LACS. The developed simulation methodology can be used to predict the performance of LACS with more complex geometries.

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Bibliografia

[1] D. Zangani, M. Robinson, and A.G. Gibson. Evaluation of stiffness terms for z-cored sandwich panels. Applied Composite Materials, 14:159–175, 2007. doi: 10.1007/s10443-007-9038-y.
[2] J. Yu, E. Wang, J. Li, and Z. Zheng. Static and low-velocity impact behavior of sandwich beams with closed-cell aluminum-foam core in three-point bending. International Journal of Impact Engineering, 35(8):885–894, 2008. doi: 10.1016/j.ijimpeng.2008.01.006.
[3] Q. Sun, Z. Meng, G. Zhou, S.-P. Lin, H. Kang, S. Keten, H. Guo, and X. Su. Multi-scale computational analysis of unidirectional carbon fiber reinforced polymer composites under various loading conditions. Composite Structures, 196:30–43, 2018. doi: 10.1016/j.compstruct.2018.05.025.
[4] G.S. Dhaliwal and G.M. Newaz. Modeling low velocity impact response of carbon fiber reinforced aluminum laminates (CARALL). Journal of Dynamic Behavior of Materials, 2:181–193, 2016. doi: 10.1007/s40870-016-0057-3.
[5] G.-C. Yu, L.-Z. Wu, L. Ma, and J. Xiong. Low velocity impact of carbon fiber aluminum laminates. Composite Structures, 119:757–766, 2014. doi: 10.1016/j.compstruct.2014.09.054.
[6] M. Koc, F.O. Sonmez, N. Ersoy, and K. Cinar. Failure behavior of composite laminates under four-point bending. Journal of Composite Materials, 50(26): 3679–3697, 2016. doi: 10.1177/0021998315624251.
[7] A. Shojaei, G. Li, P.J. Tan, and J. Fish. Dynamic delamination in laminated fiber reinforced composites: A continuum damage mechanics approach. International Journal of Solid and Structures, 71:262–276, 2015. doi: 10.1016/j.ijsolstr.2015.06.029.
[8] J. Wang, R. Bihamta, T.P. Morris, and Y.-C. Pan. Numerical and experimental investigation of a laminated aluminum composite structure. Applied Composite Materials, 26:1177–1188, 2019. doi: 10.1007/s10443-019-09773-7.
[9] D. Zangani, M. Robinson, and A.G. Gibson. Energy absorption characteristics of web-core sandwich composite panels subjected to drop-weight impact. Applied Composite Materials, 15:139–156, 2008. doi: 10.1007/s10443-008-9063-5.
[10] Q.-R. Yang, J.-X. Liu, S.-K. Li, and T.-T. Wu. Bending mechanical property and failure mechanisms of woven carbon fiber-reinforced aluminum alloy composite. Rare Metals, 35(12): 915–919, 2016. doi: 10.1007/s12598-014-0271-x.
[11] M. Kinawy, R. Butler, and G.W. Hunt. Bending strength of delaminated aerospace composites. Philosophical Transactions of The Royal Society, 370:1780–1797, 2012. doi: 10.1098/rsta.2011.0337.
[12] C. Kabogu, I. Mohagheghian, J. Zhou, Z. Guan, W. Cantwell, S. John, B.R.K. Blackman, A.J. Kinloch, and J.P. Dear. High-velocity impact deformation and perforation of fibre metal laminates. Journal of Materials Science, 53:4209–4228, 2018. doi: 10.1007/s10853-017-1871-2.
[13] X. Wang, X. Zhao, Z. Wu, Z. Zhu, and Z. Wang. Interlaminar shear behavior of basalt FRP and hybrid FRP laminates. Journal of Composite Materials, 50(8):1073–1084, 2016. doi: 10.1177/0021998315587132.
[14] C. Liu, D. Du, H. Li, Y. Hu, Y. Xu, J. Tian, G. Tao, and J. Tao. Interlaminar failure behavior of GLARE laminates under short-beam three-point-bending load. Composites Part B: Engineering, 97:361–367, 2016. doi: 10.1016/j.compositesb.2016.05.003.
[15] A. Yapici and M. Metin. Effect of low velocity impact damage on buckling properties. Engineering, 1:161–166, 2009. doi: 10.4236/eng.2009.13019.
[16] J. Sarkar, T.R.G. Kutty, D.S. Wilkinson, J.D. Embury, and D.J. Lloyd. Tensile properties and bendability of T4 treated AA6111 aluminum alloys. Materials Science and Engineering: A, 369(1-2):258–266, 2004. doi: 10.1016/j.msea.2003.11.022.
[17] 3M Automotive Division, 3M TM Structureal Adhesive Tape SAT1010M Technical Data Sheet, 3M, St. Paul, 2019.
[18] C.J. Corbett, L. Laszczyk, and O. Rebuffet. Assessing and validating the crash behavior of Securalex HS, a high-strength crashworthy aluminum alloy, using the GISSMO model. In 14th International LS-Dyna Users Conference, Detroit, 2016.
[19] G. Falkinger, N. Sotirov, and P. Simon. An investigation of modeling approaches for material instability of aluminum sheet metal using the GISSMO-model. In 10th European LS-DYNA Conference, Wurzburg, 2015.
[20] Livermore Softwar Technology Corporation (LSTC), LS-DYNA®KEYWORD USER'S MANUAL VOLUME II Material Models, 2012.
[21] A. Mostafa, K. Shankar, and E.V. Morozov. Experimental, theoretical and numerical investigation of the flexural behaviour of the composite sandwich panels with PVC foam core. Applied Composite Materials, 21:661–675, 2014. doi: 10.1007/s10443-013-9361-4.
[22] G.A.O. Davies and I. Guiamatsia. The problem of the cohesive zone in numerically simulating delamination/debonding failure modes. Applied Composite Materials, 19:831–838, 2012. doi: 10.1007/s10443-012-9257-8.
[23] F. Dogan, H. Hadavinia, T. Donchev, and P.S. Bhonge. Delamination of impacted composite structures by cohesive zone interface elements and tiebreak contact. Central European Journal of Engineering, 2(4):612–626, 2012. doi: 10.2478/s13531-012-0018-0.
[24] C. Hesch and P. Betsch. Continuum mechanical considerations for rigid bodies and fluid-structure interaction problems. Archive of Mechanical Engineering, 60(1):95–108, 2013. doi: 10.2478/meceng-2013-0006.
[25] J.J.C. Remmers and R. de Borst. Delamination buckling of fibre-metal laminates. Composites Science and Technology, 61(15):2207–2213, 2001. doi: 10.1016/S0266-3538(01)00114-2.
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Autorzy i Afiliacje

Jifeng Wang
1
Tyler P. Morris
1
Reza Bihamta
1
Ye-Chen Pan
1

  1. General Motors Global Technical Center, 29360 William Durant Boulevard, Warren, Michigan 48092-2025, USA.
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Abstrakt

Product Lifecycle Management (PLM) system requires consideration and ensuring efficient operating conditions for the most loaded parts in the product, not only at the product's design stage, but also at the production stage. Operational properties of the product can be significantly improved if we take into consideration the formation of the functional surfaces wear resistance parameters already at the planning stage of the technological process structure and parameters of the product's machining. The method of constructing predictive models of the influence of the technological process structure on the formation of a complex of product's operational properties is described in the article. The relative index of operational wear resistance of the machined surface, which is characterized by the use of different variants of the structure and parameters of this surface treatment, depends on the microtopographic state of the surface layer and the presence of cutting-induced residual stress. On the example of the eject pin machining it has been shown how the change in the structure of the manufacturing process from grinding to the turning by tool with the tungsten carbide insert affects the predicted wear resistance of the machined functional surface.

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Bibliografia

[1] V. Stupnytskyy and I. Hrytsay. Computer-aided conception for planning and researching of the functional-oriented manufacturing process. In: Tonkonogyi V. et al. (eds), Advanced Manufacturing Processes, part of the Lecture Notes in Mechanical Engineering, pages 309–320, Springer, Cham, 2020. doi: 10.1007/978-3-030-40724-7_32.
[2] J.P. Davim. Surface Integrity in Machining. Springer, London, 2010. doi: 10.1007/978-1-84882-874-2.
[3] W.E. Eder. Theory of technical systems – educational tool for engineering. Universal Journal of Educational Research, 4(6):1395–1405, 2016. doi: 10.13189/ujer.2016.040617.
[4] R.M. Rangan, S.M. Rohde, R. Peak, B. Chadha, and P. Bliznakov. Streamlining product lifecycle processes: a survey of product lifecycle management implementations, directions, and challenges. Journal of Computing and Information Science in Engineering, 5(3):227–237, 2005. doi: 10.1115/1.2031270.
[5] F. Demoly, O. Dutartre, X.-T. Yan, B. Eynard, D. Kiritsis, and S. Gomes. Product relationships management enabler for concurrent engineering and product lifecycle management. Computers in Industry, 64(7):833–848, 2013. doi: 10.1016/j.compind.2013.05.004.
[6] V. Stupnytskyy. Computer aided machine-building technological process planning by the methods of concurrent engineering. Europaische Fachhochschule: Wissenschaftliche Zeitschrift, ORT Publishing, 2:50–53, 2013.
[7] A.I. Dmitriev, A.Yu. Smolin, V.L. Popov, and S.G. Psakhie. A multilevel computer simulation of friction and wear by numerical methods of discrete mechanics and a phenomenological theory. Physical Mesomechanics, 12(1-2):11–19, 2009. doi: 10.1016/j.physme.2009.03.002.
[8] T.R. Thomas. Rough Surfaces, 2nd edition. Imperial College Press, London, 1998. doi: 10.1142/p086.
[9] G. Straffelini. Friction and Wear: Methodologies for Design and Control. Springer, Cham, 2015. doi: 0.1007/978-3-319-05894-8.
[10] H. Aramaki, H.S. Cheng, and Y. Chung. The contact between rough surfaces with longitudinal texture – part I: average contact pressure and real contact area. Journal of Tribology, 115(3):419–424, 1993. doi: 10.1115/1.2921653.
[11] Yu A. Karpenko and A. Akay. A numerical model of friction between rough surfaces. Tribology International. 34:531–545, 2001. doi: 10.1016/S0301-679X(01)00044-5.
[12] N.B. Dyomkin. Calculation and experimental study of rough contact surfaces. In Proceedings of Science Conference ``Contact Problems and Their Engineering Applications'', pages 264–271, Moscow, 1969.
[13] J. Luo, Y. Meng, T. Shao and Q. Zhao, (eds). Advanced Tribology: Proceedings of CIST2008 & ITS-IFToMM-2008. Beijing, China, 2008; Spriner, 2010. doi: 10.1007/978-3-642-03653-8.
[14] H. Hirani. Fundamentals of Engineering Tribology with Applications. Cambridge University Press, 2016.
[15] B. Bhushan. Introduction to Tribology. John Wiley & Sons, 2013.
[16] K.C. Ludema. Friction, Wear, Lubrication. A Textbook in Tribology. CRC Press, 1996.
[17] B.N.J. Persson. Sliding Friction: Physical Principles and Applications. Springer Science & Business Media, 2013.
[18] N.B. Dyomkin. Contacting of Rough Surfces. Moskow: Nauka, 1970. (in Russian).
[19] J.A. Greenwood and G. Williamson. Contact of nominally flat surfaces. In Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 295(1442):300–319, 1966. doi: 10.1098/rspa.1966.0242.
[20] S. Andersson and U. Olofsson. Simulation of plastic deformation and wear of a rough surface rubbing against a smooth wear resistant surface. In Proceedings of the 10th International Conference on Tribology, Bukharest, Romania, 2007.
[21] A. Ishlinsky and F. Chernousko. Advances in Theoretical and Applied Mechanics. Moskow: Mir, 1981.
[22] K. Hill. The Matematical Theory of Plasticity. Clarendon Press, Oxford, 1998.
[23] L.I. Sedov (ed.). Foundations of the Non-Linear Mechanics of Continua, volume 1 of International Series of Monographs in Interdisciplinary and Advanced Topics in Science and Engineering, 1966. doi: 10.1016/C2013-0-07842-5.
[24] A. Chmiel. Finite element simulation methods for dry sliding wear. M.Sc. Thesis, Air Force Institute of Technology. Wright-Patterson Air Force Base, Ochio, USA, 2008.
[25] M.W. Fu, M.S. Yong, and T. Muramatsu. Die fatigue life design and assessment via CAE simulation. The International Journal of Advanced Manufacturing Technology, 35(9–10): 843–851, 2008. doi: 10.1007/s00170-006-0762-5.
[26] I.V. Kragelsky, M.N. Dobychin, and V.S. Kombalov. Friction and Wear: Calculation Methods. Pergamon Press, Oxford, 1982.
[27] D.R. Askeland. The Science and Engineering of Materials. 3rd edition.: Springer Science & Business Media, Oxford, 1996. doi: doi.org/10.1016/s0167-8922(06)x8001-x">10.1016/s0167-8922(06)x8001-x.
[29] V. Stupnytskyy. A generalized example of structural and parametric optimization of functionally-oriented process. Bulletin of the National Technical University ``KhPI''. Series: Techniques in a machine industry. 42(1085):116–130, 2014.
[30] Z. Nazarchuk, V. Skalskyi, O. Serhiyenko. Acoustic Emission. Methodology and Application. Springer, Cham, 2017. doi: 10.1007/978-3-319-49350-3.
[31] V. Stupnytskyy and I. Hrytsay I. Simulation study of cutting-induced residual stress. In: Ivanov V. et al. (eds), Advances in Design, Simulation and Manufacturing II. DSMIE 2019, part of Lecture Notes in Mechanical Engineering, pages 341–350, 2020. doi: 10.1007/978-3-030-22365-6_34.
[32] Y. Kudryavtsev and J. Kleiman. Ultrasonic technique and device for residual stress measurement. In T. Proulx (ed.), Engineering Applications of Residual Stress, volume 8 of Conference Proceedings of the Society for Experimental Mechanics Series. Springer, New York, 2011. doi: 10.1007/978-1-4614-0225-1_8.
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Autorzy i Afiliacje

Vadym Stupnytskyy
1
Ihor Hrytsay
1

  1. Department of Mechanical Engineering Technologies, Institute of Engineering Mechanics and Transport, Lviv Polytechnic National University, Lviv, Ukraine.
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Abstrakt

Material parameters identification by inverse analysis using finite element computations leads to the resolution of complex and time-consuming optimization problems. One way to deal with these complex problems is to use meta-models to limit the number of objective function computations. In this paper, the Efficient Global Optimization (EGO) algorithm is used. The EGO algorithm is applied to specific objective functions, which are representative of material parameters identification issues. Isotropic and anisotropic correlation functions are tested. For anisotropic correlation functions, it leads to a significant reduction of the computation time. Besides, they appear to be a good way to deal with the weak sensitivity of the parameters. In order to decrease the computation time, a parallel strategy is defined. It relies on a virtual enrichment of the meta-model, in order to compute q new objective functions in a parallel environment. Different methods of choosing the qnew objective functions are presented and compared. Speed-up tests show that Kriging Believer (KB) and minimum Constant Liar (CLmin) enrichments are suitable methods for this parallel EGO (EGO-p) algorithm. However, it must be noted that the most interesting speed-ups are observed for a small number of objective functions computed in parallel. Finally, the algorithm is successfully tested on a real parameters identification problem.

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Bibliografia

[1] P.A. Prates, M.C Oliveira, and J.V. Fernandes. Identification of material parameters for thin sheets from single biaxial tensile test using a sequential inverse identification strategy. International Journal of Material Forming, 9:547–571, 2016. doi: 10.1007/s12289-015-1241-z.
[2] M. Gruber, N. Lebaal, S. Roth, N. Harb, P. Sterionow, and F. Peyraut. Parameter identification of hardening laws for bulk metal forming using experimental and numerical approach. International Journal of Material Forming, 9:21–33. doi: 10.1007/s12289-014-1196-5.
[3] R. Amaral, P. Teixeira, A.D. Santos, and J.C. de Sá. Assessment of different ductile damage models and experimental validation. International Journal of Material Forming, 11, 435–444, 2018. doi: 10.1007/s12289-017-1381-4.
[4] J. Nocedal and S. Wright. Numerical Optimization, 2nd ed. Springer-Verlag, New York, 2006.
[5] J.A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 1965. doi: 10.1093/comjnl/7.4.308.
[6] K.Y. Lee and F.F. Yang. Optimal reactive power planning using evolutionalry algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming. IEEE Transactions on Power Systems, 13(1):101–108, 1998. doi: 10.1109/59.651620.
[7] N. Stander, K.J. Craig, H. Müllerschön, and R. Reichert. Material identification in structural optimization using response surfaces. Structural and Multidisciplinary Optimization, 29:93–102, 2005. doi: 10.1007/s00158-004-0476-y.
[8] M. Ageno, G. Bolzon, and G. Maier. An inverse analysis procedure for the material parameter identification of elastic- plastic free-standing foils. Structural and Multidisciplinary Optimization, 38:229–243, 2009. doi: 10.1007/s00158-008-0294-8.
[9] M. Abendroth and M. Kuna. Identification of ductile damage and fracture parameters from the small punch test using neural networks. Engineering Fracture Mechanics, 73(6):710–725, 2006. doi: 10.1016/j.engfracmech.2005.10.007.
[10] R. Franchi, A. Del Prete, and D. Umbrell. Inverse analysis procedure to determine flow stress and friction data for finite element modeling of machining. International Journal of Material Forming, 10:685–695, 2017. doi: 10.1007/s12289-016-1311-x.
[11] N. Souto, A. Andrade-Campos, and S. Thuillier. Mechanical design of a heterogeneous test for material parameters identification. International Journal of Material Forming, 10:353–367, 2017. doi: 10.1007/s12289-016-1284-9.
[12] M. Rackl, K.J. Hanley, and W.A. Günthner. Verification of an automated work flow for discrete element material parameter calibration. In: Li X., Feng Y., Mustoe G. (eds.), Proceedings of the 7th International Conference on Discrete Element Methods. DEM 2016, volume 188, pages 201–208. Springer, Singapore 2017. doi: 10.1007/978-981-10-1926-5_23.
[13] K. Levenberg. A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics, 2(2):164–168, 1944.
[14] C. Richter, T. Rößler, G. Kunze, A. Katterfeld, and F. Will. Development of a standard calibration procedure for the DEM parameters of cohesionless bulk materials – Part II: Efficient optimization-based calibration. Powder Technology, 360:967–976, 2020. doi: 10.1016/j.powtec.2019.10.052.
[15] D.R. Jones, M. Schonlau, and W.J. Welch. Efficient global optimization of expensive black-box function. Journal of Global Optimization, 13:455–492, 1998. doi: 10.1023/A:1008306431147.
[16] M.T.M. Emmerich, K.C. Giannakoglou, and B. Naujoks. Single- and multi-objective evolutionary optimization assisted by gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4):421–439, 2006. doi: 10.1109/TEVC.2005.859463.
[17] T.J. Santner, B.J. Williams, and W.I. Notz. The Design and Analysis of Computer Experiments. Springer, New York, 2018. doi: 10.1007/978-1-4939-8847-1.
[18] E. Brochu, V.M. Cora, and N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical Report TR-2009-23, Department of Computer Science, University of British Columbia, Canada, November 2009.
[19] D. Ginsbourger, R. Riche, and L. Carraro. Kriging is well-suited to parallelize optimization. In Y. Tenne, Ch-K. Goh (eds.), Computational Intelligence in Expensive Optimization Problems, volume 2, pages 131–162, Springer-Verlag, Berlin, 2010.
[20] E. Roux and P.-O. Bouchard. Kriging metamodel global optimization of clinching joining processes accounting for ductile damage. Journal of Materials Processing Technology, 213(7):1038–1047, 2013. doi: 10.1016/j.jmatprotec.2013.01.018.
[21] E. Roux and P.-O. Bouchard. On the interest of using full field measurements in ductile damage model calibration. International Journal of Solids and Structures, 72:50–62, 2015. doi: 10.1016/j.ijsolstr.2015.07.011.
[22] J. Sacks, W.J. Welch, T.J. Mitchell, and H.P. Wynn. Design and analysis of computer experiments. Statistical Science, 4(4):409–423, 1989.
[23] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer M. et al. (eds,), Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, volume 1917, pages 849–858. Springer, Berlin, Heidelberg, 2000. doi: 10.1007/3-540-45356-3_83.
[24] M. Hamdaoui, F.Z. Oujebbour, A. Habbal, P. Breitkopf, and P. Villon. Kriging surrogates for evolutionary multi-objective optimization of CPU intensive sheet metal forming applications. International Journal of Material Forming, 8:469–480, 2015. doi: 10.1007/s12289-014-1190-y.
[25] J.J. Droesbeke, M. Lejeune, and G. Saporta. Statistical Analysis of Spatial Data. Editions TECHNIP, 1997 (in French).
[26] C.E. Rasmussen and C.K.I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
[27] J.D. Martin and T.W. Simpson. Use of kriging models to approximate deterministic computer models. AIAA Journal, 43(4):853–863, 2005. doi: 10.2514/1.8650.
[28] J. Laurenceau and P. Sagaut. Building efficient response surface of aerodynamic function with kriging and cokriging. AIAA Journal, 46(2):498–507, 2008. doi: 10.2514/1.32308.
[29] D.R. Jones. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21:345–383, 2001. doi: 10.1023/A:1012771025575.
[30] V.A. Dirk and H.G. Beyer. A comparison of evolution strategies with other direct search methods in the presence of noise. Computational Optimization and Applications, 24:135–159, 2003. doi: 10.1023/A:1021810301763.
[31] J.A. Nelder and R. Mead. A simplex method for function minimization. The Computer Journal, 7(4):308–313, 1965. doi: 10.1093/comjnl/7.4.308.
[32] P.-O. Bouchard, J.-M. Gachet, and E. Roux. Ductile damage parameters identification for cold metal forming applications. AIP Conference Proceedings, 1353(1):47–52, 2011. doi: 10.1063/1.3589490.
[33] D. Huang, T.T. Allan, W.I. Notz, and N. Zeng. Global optimization of stochastic black-box systems via sequential kriging meta-models. Journal of Global Optimization, 34:441–466, 2006. doi: 10.1007/s10898-005-2454-3.
[34] H.H. Rosenbrock. An automatic method for finding the greatest or least value of a function. The Computer Journal, 3(3):175–184, 1960. doi: 10.1093/comjnl/3.3.175.
[35] M. Pillet. The Taguchi Method Experiment Plans. Les Edition d'Organisation, 2005 (in French).
[36] H. Digonnet, L. Silva, and T. Coupez. Cimlib: A Fully Parallel Application For Numerical Simulations Based On Components Assembly. AIP Conference Proceedings, 908:269–274, 2007. doi: 10.1063/1.2740823.
[37] P.-O. Bouchard, L. Bourgeon, S. Fayolle, and K. Mocellin. An enhanced Lemaitre model formulation for materials processing damage computation. International Journal of Material Forming, 4:299–315, 2011. doi: 10.1007/s12289-010-0996-5.
[38] E. Roux, M. Thonnerieux, and P.-O. Bouchard. Ductile damage material parameter identification: numerical investigation. In Proceedings of the Tenth International Conference on Computational Structures Technology, paper 135, Civil-Comp Press, 2010. doi: 10.4203/ccp.93.135.
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Autorzy i Afiliacje

Emile Roux
1
Yannick Tillier
2
Salim Kraria
2
Pierre-Olivier Bouchard
2

  1. Université Savoie Mont-Blanc, SYMME, F-74000 Annecy, France.
  2. MINES ParisTech, PSL Research University, CEMEF-Centre de mise en forme des matériaux, CNRS UMR 7635, CS 10207 rue Claude Daunesse, 06904 Sophia Antipolis Cedex, France
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Abstrakt

Resistance spotwelding is the most significant joining technique utilized in various industries, like automotive, boilers, vessels, etc., that are commonly subjected to variable tensile-shear forces due to the unsuitable use of the input spot welding variables, which mainly cause the welded joints failure during the service life of the welded assembly. So, in order to avoid such failures, the welding quality of some materials like aluminum must be improved taking into consideration the performance and weight saving of the welded structure. Thus, the need for optimizing the used welding parameters becomes essential for predicting a goodwelded joint.Accordingly, this study aims at investigating the influence of the spot welding variables, including the squeeze time, welding time, and current on the tensile-shear force of the similar and dissimilar lap joints for aluminum and steel sheets. It was concluded that the use of Taguchi design can improve the welded joints strength through designing the experiments according to the used levels of the input parameters in order to obtain their optimal values that give the optimum tensile-shear force as the response. As a consequence of the present work, the optimal spot welding parameters were successfully obtained.

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Bibliografia

[1] Metals Handbook. Welding, Brazing, and Soldering, volume 6. ASM International, Materials Park, OH,1993.
[2] N. Mookam. Optimization of resistance spot brazing process parameters in AHSS and AISI 304 stainless steel joint using filler metal. Defence Technology, 15(3):450–456, 2019. doi: 10.1016/j.dt.2019.03.005.
[3] J. Valera, V. Miguel, A. Martínez, J. Naranjo, and M. Cañas.. Optimization of electrical parameters in Resistance Spot Welding of dissimilar joints of micro-alloyed steels TRIP sheets. Procedia Manufacturing, 13:291–298, 2017. doi: 10.1016/j.promfg.2017.09.074.
[4] T.R. Mahmood, Q.M. Doos, and A.M. Al-Mukhtar. Failure mechanisms and modeling of spot welded joints in low carbon mild sheets steel and high strength low alloy steel. Procedia Structural Integrity, 9:71–85, 2018. doi: 10.1016/j.prostr.2018.06.013.
[5] S.K. Hussein and O.S. Barrak. Analysis and optimization of resistance spot welding parameter of dissimilar metals mild steel and aluminum using design of experiment method. Engineering and Technology Journal, 33(8):1999–2011, 2015.
[6] Y. Lu, E. Mayton, H. Song, M. Kimchi, and W. Zhang. Dissimilar metal joining of aluminum to steel by ultrasonic plus resistance spot welding – Microstructure and mechanical properties. Materials and Design, 165:107585, 2019. doi: 10.1016/j.matdes.2019.107585.
[7] A. Subrammanian, D.B. Jabaraj, and J. Jayaprakash. Multi-objective optimization of resistance spot welding of AISI 409M ferritic stainless steel. Journal of Scientific & Industrial Research, 77:271–275, 2018.
[8] B. Vijaya Sankar, I.D. Lawrence, and S. Jayabal. Prediction of spot welding parameters for dissimilar weld joints. Bonfring International Journal of Industrial Engineering and Management Science, 6(4):123–127, 2016. doi: 10.9756/bijiems.7542.
[9] M. Pradeep, N.S. Mahesh, and R.M. Hussain. Process parameter optimization in resistance spot welding of dissimilar thickness materials. International Journal of Mechanical and Mechatronics Engineering, 8(1):80–83, 2014.
[10] M.J. Zedan and Q.M. Doos. New method of resistance spot welding for dissimilar 1008 low carbon steel-5052 aluminum alloy. Procedia Structural Integrity, 9:37–46, 2018. doi: 10.1016/j.prostr.2018.06.008.
[11] T.P. Bagchi. Taguchi Methods Explained: Practical Steps to Robust Design. Prentice-Hall, New Delhi, India 1993.
[12] M. Sarikaya. Optimization of the surface roughness by applying the Taguchi technique for the turning of stainless steel under cooling conditions. Materiali in Tehnologije/Materials and Technology, 49(6):941–948, 2015. doi: 10.17222/mit.2014.282.
[13] A.K. Hussein, L.K. Abbas, and W.N. Hasan. Optimization of heat treatment parameters for the tensile properties of medium carbon steel. Engineering and Technology Journal, 36(10A):1091–1099, 2018. doi: 10.30684/etj.36.10a.10.
[14] J. Chen, X. Yuan, Z. Hu, C. Sun, Y. Zhang, and Y. Zhang. Microstructure and mechanical properties of resistance-spot-welded joints for A5052 aluminum alloy and DP 600 steel. Materials Characterization, 120:45–52, 2016. doi: 10.1016/j.matchar.2016.08.015.
[15] Q. Jia, L. Liu, W. Guo, Y. Peng, G. Zou, Z. Tian, and Y.N. Zhou. Microstructure and tensile-shear properties of resistance spot-welded medium Mn steel. Metals, 8(1):48, 2018. doi: 10.3390/met8010048.
[16] A. Subrammanian, D.B. Jabaraj, J. Jayaprakash, and V.K. Bupesh Raja. Mechanical properties and phase transformations in resistance spot welded dissimilar joints of AISI409M/AISI301 steel. Indian Journal of Science and Technology, 9(41):1–8, 2016. doi: 10.17485/ijst/2016/v9i41/101971.
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Autorzy i Afiliacje

Najmuldeen Yousif Mahmood
1

  1. Mechanical Engineering Department, University of Technology-Iraq, Baghdad, Iraq.
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Abstrakt

Structural design analyses of industrial dye mixing machines, concerning mixing impeller geometries, mixing performances, and power requirements aren't generally of scientific quality. Our aim is to propose a practical method for minimizing execution time, using parametric design. In this study, Visual Basic API codes are developed in order to model the impellers in SolidWorks software, and then flow analyses are conducted. Thus, velocity values and moment/torque values required for mixing operation are determined. This study is carried out for different shaft rotational speeds and different impeller diameters. Flow trajectories are obtained. After that, frequency analyses are conducted and natural frequency values are obtained. In the scope of this study, two different impeller types are investigated.

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Bibliografia

[1] R.R. Hemrajani and G.B. Tatterson. Mechanically stirred vessels. In: E.L. Paul, V.A. Atemio-Obeng, S.M. Kresta, editors, Handbook of Industrial Mixing Science and Practice, chapter 6, pages 345–390. John Wiley & Sons, 2004.
[2] S.M. Kresta and D.S. Dickey. Flow patterns and mixing. In: E.L. Paul, A.W. Etchells III, S.M. Kresta, V.A. Atemio-Obeng, editors, Advances in industrial mixing: A companion to the handbook of industrial mixing, chapter 6b, pages 153–187. John Wiley & Sons, 2016.
[3] Mixing Equipment (Impeller Type). Equipment Testing Procedures Committee of the Americal Institute of Chemical Engineers. 3rd edition, New York: MixTech, 2001.
[4] T.M.M. Shahin. Feature-based design – an overview. Computer-Aided Design and Applications, 5(5):639–653, 2008. doi: 10.3722/cadaps.2008.639-653.
[5] Y. Shan and W. Zhang. Parametric design of straight bevel gears based on SolidWorks. In Proceedings of the 2nd International Conference on Computer Application and System Modeling, pages 591–594, Taiyuan, Shanxi, China, 27–29 July, 2012. doi: 10.2991/iccasm.2012.150.
[6] Y. Bodein, B. Rose, and E. Caillaud. Explicit reference modeling methodology in parametric CAD system. Computers in Industry, 65(1):136–147, 2014. doi: 10.1016/j.compind.2013.08.004.
[7] A.C. Lad and A.S. Rao. Design and drawing automation using SolidWorks application programming interface. International Journal of Emerging Engineering Research and Technology, 2(7):157–167, 2014.
[8] J.D. Camba, M. Contero, and P. Company. Parametric CAD modeling: An analysis of strategies for design reusability. Computer-Aided Design, 74:18–31, 2016. doi: 10.1016/j.cad.2016.01.003.
[9] M.H. Vakili and M.N. Esfahany. CFD analysis of turbulence in a baffled stirred tank, a three-compartment model. Chemical Engineering Science, 64(2):351–362, 2009. doi: 10.1016/j.ces.2008.10.037.
[10] H.C. Ayaz and Z. Kıral. On the parametric design and analysis of industrial dye mixing machines. In Proceedings of the 3rd International Conference on Engineering and Natural Science, pages 686–693, Budapest, Hungary, 3–7 May, 2017.
[11] J.Y. Lee and K. Kim. Geometric reasoning for knowledge-based parametric design using graph representation. Computer-Aided Design, 28(10):831–841, 1996. doi: 10.1016/0010-4485(96)00016-4.
[12] O.O. Akçalı. Parametric design of automotive ball joint using computer assisted 3D modelling. Master's Thesis, Çukurova University, Adana, Turkey, 2015.
[13] C.R.B. Hernandez. Thinking parametric design: introducing parametric Gaudi. Design Studies, 27(3):309–324, 2006. doi: 10.1016/j.destud.2005.11.006.
[14] S. Myung and S. Han. Knowledge-based parametric design of mechanical products based on configuration design method. Expert Systems with Applications, 21(2):99–107, 2001. doi: 10.1016/S0957-4174(01)00030-6.
[15] B. Bettig and J. Shah. Derivation of a standard set of geometric constraints for parametric modeling and data exchange. Computer-Aided Design, 33(1):17–33, 2001. doi: 10.1016/S0010-4485(00)00058-0.
[16] J. Monedero. Parametric design: a review and some experiences. Automation in Construction, 9(4):369–377, 2000. doi: 10.1016/S0926-5805(99)00020-5.
[17] A. Titus and L.X. Bin. Secondary development of SolidWorks for standard components based on database. International Journal of Science and Research, 2(10):162–164, 2013.
[18] U. Farhan, S. O'Brien, and M.T. Rad. SolidWorks secondary development with Visual Basic 6 for an automated modular fixture assembly approach. International Journal of Engineering, 6(6):290–304, 2012.
[19] S.P. Prince, R.G. Ryan, and T. Mincer. Common API: using Visual Basic to communicate between engineering design and analytical software tools. In Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition, 2005.
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Autorzy i Afiliacje

Hatice Cansu Ayaz Ümütlü
1
Zeki Kıral
2

  1. Dokuz Eylül University, The Graduate School of Natural and Applied Sciences, Department of Mechatronics Engineering, Tınaztepe-Buca/İzmir, Turkey
  2. Dokuz Eylül University, Faculty of Engineering, Department of Mechanical Engineering, Tınaztepe-Buca/İzmir, Turkey
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Abstrakt

This paper explores the parametric appraisal and machining performance optimization during drilling of polymer nanocomposites reinforced by graphene oxide/carbon fiber. The consequences of drilling parameters like cutting velocity, feed, and weight % of graphene oxide on machining responses, namely surface roughness, thrust force, torque, delamination (In/Out) has been investigated. An integrated approach of a Combined Quality Loss concept, Weighted Principal Component Analysis (WPCA), and Taguchi theory is proposed for the evaluation of drilling efficiency. Response surface methodology was employed for drilling of samples using the titanium aluminum nitride tool. WPCA is used for aggregation of multi-response into a single objective function. Analysis of variance reveals that cutting velocity is the most influential factor trailed by feed and weight % of graphene oxide. The proposed approach predicts the outcomes of the developed model for an optimal set of parameters. It has been validated by a confirmatory test, which shows a satisfactory agreement with the actual data. The lower feed plays a vital role in surface finishing. At lower feed, the development of the defect and cracks are found less with an improved surface finish. The proposed module demonstrates the feasibility of controlling quality and productivity factors.

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Bibliografia

[1] Y.A. Roy, K. Gobivel, K.S.V Sekar, and S.S. Kumar. Impact of cutting forces and chip microstructure in high speed machining of carbon fiber – Epoxy composite tube. Archives of Metallurgy and Materials, 62(3):1771–1777, 2017. doi: 10.1515/amm-2017-0269.
[2] R. Sengupta, M. Bhattacharya, S. Bandyopadhyay, and A.K. Bhowmick. A review on the mechanical and electrical properties of graphite and modified graphite reinforced polymer composites. Progress in Polymer Science, 36(5):638–670, 2011. doi: 10.1016/j.progpolymsci.2010.11.003.
[3] P.F. Mayuet, F. Girot, A. Lamíkiz, S.R. Fernández-Vidal, J. Salguero, and M. Marcos. SOM/SEM based characterization of internal delaminations of CFRP samples machined by AWJM. Procedia Engineering, 132:693–700, 2015. doi: 10.1016/j.proeng.2015.12.549.
[4] A. Caggiano. Machining of fibre reinforced plastic composite materials. Materials, 11(3):442, 2018. doi: 10.3390/ma11030442.
[5] V. Sonkar, K. Abhishek, S. Datta, and S.S. Mahapatra. Multi-objective optimization in drilling of GFRP composites: A degree of similarity approach. Procedia Materials Science, 6:538–543, 2014. doi: 10.1016/j.mspro.2014.07.068.
[6] P. Kuppan, A. Rajadurai, and S. Narayanan. Influence of EDM process parameters in deep hole drilling of Inconel 718. The International Journal of Advanced Manufacturing Technology, 38(1–2):74–84, 2008. doi: 10.1007/s00170-007-1084-y.
[7] K. Abhishek, S. Datta, and S.S. Mahapatra. Multi-objective optimization in drilling of CFRP (polyester) composites: Application of a fuzzy embedded harmony search (HS) algorithm. Measurement, 77:222–239, 2016. doi: 10.1016/j.measurement.2015.09.015.
[8] B.C. Routara, S.D. Mohanty, S. Datta, A. Bandyopadhyay, and S.S. Mahapatra. Combined quality loss (CQL) concept in WPCA-based Taguchi philosophy for optimization of multiple surface quality characteristics of UNS C34000 brass in cylindrical grinding. The International Journal of Advanced Manufacturing Technology, 51(1–4):135–143, 2010. doi: 10.1007/s00170-010-2599-1.
[9] M.K. Das, K. Kumar, T.K. Barman, and P. Sahoo. Optimization of MRR and surface roughness in PAC of EN 31 steel using weighted principal component analysis. Procedia Technology, 14:211–218, 2014. doi: 10.1016/j.protcy.2014.08.028.
[10] S. Grieu, A. Traoré, M. Polit, and J. Colprim. Prediction of parameters characterizing the state of a pollution removal biologic process. Engineering Applications of Artificial Intelligence, 18(5):559–573, 2005. doi: 10.1016/j.engappai.2004.11.008.
[11] S.D. Lahane, M.K. Rodge, and S.B. Sharma. Multi-response optimization of wire-EDM process using principal component analysis. IOSR Journal of Engineering, 2(8):38–47, 2012. doi: 10.9790/3021-02833847.
[12] R. Ramanujam, K. Venkatesan, V. Saxena, R. Pandey, T. Harsha, and G. Kumar. Optimization of machining parameters using fuzzy based principal component analysis during dry turning operation of inconel 625 – A hybrid approach. Procedia Engineering, 97:668–676, 2014. doi: 10.1016/j.proeng.2014.12.296.
[13] H. Yang, R. Luo, S. Han, and M. Li. Effect of the ratio of graphite/pitch coke on the mechanical and tribological properties of copper-carbon composites. Wear, 268(11–12):1337–1341, 2010. doi: 10.1016/j.wear.2010.02.007.
[14] R.K. Verma, P.K. Pal, and B.C. Kandpal. Machining performance optimization in drilling of GFRP composites: A utility theory (UT) based approach. In: Proceedings of 2016 International Conference on Control, Computing, Communication and Materials, pages 1–5, Allahbad, India, 21-22 Oct. 2016. doi: 10.1109/ICCCCM.2016.7918255.
[15] K. Palanikumar, J.C. Rubio, A. Abrão, A. Esteves, and J.P. Davim. Statistical analysis of delamination in drilling Glass Fiber-Reinforced Plastics (GFRP). Journal of Reinforced Plastics and Composites, 27(15):1615–1623, 2008. doi: 10.1177/0731684407083012.
[16] P.E. Faria, J.C. Campos Rubio, A.M. Abrão, and J.P. Davim. Dimensional and geometric deviations induced by drilling of polymeric composite. Journal of Reinforced Plastics and Composites, 28(19):2353–2363, 2009. doi: 10.1177/0731684408092067.
[17] V.N. Gaitonde, S.R. Karnik, J.C.C. Rubio, W. de Oliveira Leite, and J.P. Davim. Experimental studies on hole quality and machinability characteristics in drilling of unreinforced and reinforced polyamides. Journal of Composite Materials, 48(1):21–36, 2014. doi: 10.1177/0021998312467552.
[18] Niharika, B.P. Agrawal, I.A. Khan, and Z.A. Khan. Effects of cutting parameters on quality of surface produced by machining of titanium alloy and their optimization. Archive of Mechanical Engineering, 63(4):531–548, 2016. doi: 10.1515/meceng-2016-0030.
[19] S. Chakraborty and P.P. Das. Fuzzy modeling and parametric analysis of non-traditional machining processes. Management and Production Engineering Review, 10(3):111–123, 2019. doi: 10.24425/mper.2019.130504.
[20] S. Prabhu and B.K. Vinayagam. Multiresponse optimization of EDM process with nanofluids using TOPSIS method and Genetic Algorithm. Archive of Mechanical Engineering, 63(1):45–71, 2016. doi: 10.1515/meceng-2016-0003.
[21] D. Palanisamy and P. Senthil. Optimization on turning parameters of 15-5PH stainless steel using taguchi based grey approach and TOPSIS. Archive of Mechanical Engineering, 63(3):397–412, 2016. doi: 10.1515/meceng-2016-0023.
[22] M.S. Węglowski. Experimental study and response surface methodology for investigation of FSP process. Archive of Mechanical Engineering, 61(4):539–552, 2014. doi: 10.2478/meceng-2014-0031.
[23] H. Majumder, T.R. Paul, V. Dey, P. Dutta, and A. Saha. Use of PCA-grey analysis and RSM to model cutting time and surface finish of Inconel 800 during wire electro discharge cutting. Measurement, 107:19–30, 2017. doi: 10.1016/j.measurement.2017.05.007.
[24] P.K. Kharwar and R.K. Verma. Grey embedded in artificial neural network (ANN) based on hybrid optimization approach in machining of GFRP epoxy composites. FME Transactions, 47(3):641–648, 2019. doi: 10.5937/fmet1903641P.
[25] R. Arun Ramnath, P.R. Thyla, N. Mahendra Kumar, and S. Aravind. Optimization of machining parameters of composites using multi-attribute decision-making techniques: A review. Journal of Reinforced Plastics and Composites, 37(2):77–89, 2018. doi: 10.1177/0731684417732840.
[26] K. Żak. Cutting mechanics and surface finish for turning with differently shaped CBN tools. Archive of Mechanical Engineering, 64(3):347–357, 2017. doi: 10.1515/meceng-2017-0021.
[27] R. Bielawski, M. Kowalik, K. Suprynowicz, W. Rządkowski, and P. Pyrzanowski. Experimental study on the riveted joints in Glass Fibre Reinforced Plastics (GFRP). Archive of Mechanical Engineering, 64(3):301–313, 2017. doi: 10.1515/meceng-2017-0018.
[28] A.K. Parida, R. Das, A.K. Sahoo, and B.C. Routara. Optimization of cutting parameters for surface roughness in machining of GFRP composites with graphite/fly ash filler. Procedia Materials Science, 6:1533–1538, 2014. doi: 10.1016/j.mspro.2014.07.134.
[29] M.C. Yip, Y.C. Lin, and C.L. Wu. Effect of multi-walled carbon nanotubes addition on mechanical properties of polymer composites laminate. Polymers and Polymer Composites, 19(2–3):131–140, 2011.
[30] I. Burmistrov, N. Gorshkov, I. Ilinykh, D. Muratov, E. Kolesnikov, S. Anshin, I. Mazov, J.-P. Issi, and D. Kusnezov. Improvement of carbon black based polymer composite electrical conductivity with additions of MWCNT. Composites Science and Technology, 129:79–85, 2016. doi: 10.1016/j.compscitech.2016.03.032.
[31] N.S. Mohan, A. Ramachandra, and S. M. Kulkarni. Influence of process parameters on cutting force and torque during drilling of glass-fiber polyester reinforced composites. Composite Structures, 71(3–4):407–413, 2005. doi: 10.1016/j.compstruct.2005.09.039.
[32] R. Bhat, N. Mohan, S. Sharma, R.A. Agarwal, A. Rathi, and K.A. Subudhi. Multi-response optimization of the thrust force, torque and surface roughness in drilling of glass fiber reinforced polyester composite using GRA-RSM. Materials Today: Proceedings, 19:333–338, 2019. doi: 10.1016/j.matpr.2019.07.608.
[33] T. Miyake, K. Mukae, and M. Futamura. Evaluation of machining damage around drilled holes in a CFRP by fiber residual stresses measured using micro-Raman spectroscopy. Mechanical Engineering Journal, 3(6):1–16, 2016. doi: 10.1299/mej.16-00301.
[34] G.V.G. Rao, P. Mahajan, and N. Bhatnagar. Micro-mechanical modeling of machining of FRP composites – Cutting force analysis. Composites Science and Technology, 67(3–4):579–593, 2007. doi: 10.1016/j.compscitech.2006.08.010.
[35] R.K. Verma, K. Abhishek, S. Datta, P.K. Pal, and S.S. Mahapatra. Multi-response optimization in machining of GFRP (epoxy) composites: An integrated approach. Journal for Manufacturing Science and Production, 15(3):267–292, 2015. doi: 10.1515/jmsp-2014-0054.
[36] K. Pearson. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11):559–572, 1901. doi: 10.1080/14786440109462720.
[37] D. Zhao, H. Qi, and J. Pan. A predication analysis of the factors influencing minimum ignition temperature of coal dust cloud based on principal component analysis and support vector machine. Archives of Mining Sciences, 64(2):335–350, 2019. doi: 10.24425/ams.2019.128687.
[38] M. Ukamanal, P.C. Mishra, and A.K. Sahoo. Effects of spray cooling process parameters on machining performance AISI 316 steel: a novel experimental technique. Experimental Techniques, 44(1):19–36, 2020. doi: 10.1007/s40799-019-00334-y.
[39] G. Karuna Kumar, C. Maheswara Rao, and V.V.S. KesavaRao. Application of WPCA & CQL methods in the optimization of mutiple responses. Materials Today: Proceedings, 18:25–36, 2019. doi: 10.1016/j.matpr.2019.06.273.
[40] D. Das, P.C. Mishra, S. Singh, A.K. Chaubey, and B.C. Routara. Machining performance of aluminium matrix composite and use of WPCA based Taguchi technique for multiple response optimization. International Journal of Industrial Engineering Computations, 9(4):551–564, 2018. doi: 10.5267/j.ijiec.2017.10.001.
[41] S.D. Mohanty, S.S. Mahapatra, and R.C. Mohanty. PCA based hybrid Taguchi philosophy for optimization of multiple responses in EDM. SADHANA, 44(1):1–9, 2019. doi: 10.1007/s12046-018-0982-z.
[42] U.A. Khashaba. Delamination in drilling GFR-thermoset composites. Composite Structures, 63(3–4):313–327, 2004. doi: 10.1016/S0263-8223(03)00180-6.
[43] L. Gemi, S. Morkavuk, U. Köklü, and D.S. Gemi. An experimental study on the effects of various drill types on drilling performance of GFRP composite pipes and damage formation. Composites Part B: Engineering, 172:186–194, 2019. doi: 10.1016/j.compositesb.2019.05.023.
[44] L. Li, C. Yan, H. Xu, D. Liu, P. Shi, Y. Zhu, G. Chen, X. Wu, and W. Liu. Improving the interfacial properties of carbon fiber–epoxy resin composites with a graphene-modified sizing agent. Journal of Applied Polymer Science, 136(9):1–10, 2019. doi: 10.1002/app.47122.
[45] U. Aich, R.R. Behera, and S. Banerjee. Modeling of delamination in drilling of glass fiber-reinforced polyester composite by support vector machine tuned by particle swarm optimization. International Journal of Plastics Technology, 23(1):77–91, 2019. doi: 10.1007/s12588-019-09233-8.
[46] D. Kumar and K.K. Singh. Investigation of delamination and surface quality of machined holes in drilling of multiwalled carbon nanotube doped epoxy/carbon fiber reinforced polymer nanocomposite. Journal of Materials: Design and Applications, 233(4):647–663, 2019. doi: 10.1177/1464420717692369.
[47] P. Kyratsis, A.P. Markopoulos, N. Efkolidis, V. Maliagkas, and K. Kakoulis. Prediction of thrust force and cutting torque in drilling based on the response surface methodology. Machines, 6(2):24, 2018. doi: 10.3390/MACHINES6020024.
[48] C.C. Tsao. Thrust force and delamination of core-saw drill during drilling of carbon fiber reinforced plastics (CFRP). The International Journal of Advanced Manufacturing Technology, 37(1–2):23–28, 2008. doi: 10.1007/s00170-007-0963-6.
[49] A.M. Abrão, J.C.C. Rubio, P.E. Faria, and J.P. Davim. The effect of cutting tool geometry on thrust force and delamination when drilling glass fibre reinforced plastic composite. Materials & Design, 29(2):508–513, 2008. doi: 10.1016/j.matdes.2007.01.016.
[50] A. Janakiraman, S. Pemmasani, S. Sheth, C. Kannan, and A.S.S. Balan. Experimental investigation and parametric optimization on hole quality assessment during drilling of CFRP/GFRP/Al stacks. Journal of The Institution of Engineers (India): Series C, 101:291–302, 2020. doi: 10.1007/s40032-020-00563-w.
[51] S.Y. Park, W.J. Choi, C.H. Choi, and H.S. Choi. Effect of drilling parameters on hole quality and delamination of hybrid GLARE laminate. Composite Structures, 185:684–698, 2018. doi: 10.1016/j.compstruct.2017.11.073.
[52] R. Świercz, D. Oniszczuk-Świercz, J. Zawora, and M. Marczak. Investigation of the influence of process parameters on shape deviation after wire electrical discharge machining. Archives of Metallurgy and Materials, 64(4):1457–1462, 2019. doi: 10.24425/amm.2019.130113.
[53] K. Palanikumar. Modeling and analysis of delamination factor and surface roughness in drilling GFRP composites. Materials and Manufacturing Processes, 25(10):1059–1067, 2010. doi: 10.1080/10426910903575830.
[54] S.K. Rathore, J. Vimal, and D.K. Kasdekar. Determination of optimum parameters for surface roughness in CNC turning by using GRA-PCA. International Journal of Engineering, Science and Technology, 10(2):37–49, 2018. doi: 10.4314/ijest.v10i2.5.
[55] A. Gok. A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement, 70:100–109, 2015. doi: 10.1016/j.measurement.2015.03.037.
[56] N.L. Bhirud and R.R. Gawande. Optimization of process parameters during end milling and prediction of work piece temperature rise. Archive of Mechanical Engineering, 64(3):327–346, 2017. doi: 10.1515/meceng-2017-0020.
[57] B.A. Rezende, F. de Castro Magalhães, and J.C. Campos Rubio. Study of the measurement and mathematical modelling of temperature in turning by means equivalent thermal conductivity. Measurement, 152:107275, 2020. doi: 10.1016/j.measurement.2019.107275.
[58] A. Bhattacharya, S. Das, P. Majumder, and A. Batish. Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA. Production Engineering, 3(1):31–40, 2009. doi: 10.1007/s11740-008-0132-2.
[59] A. Taşkesen and K. Kütükde. Experimental investigation and multi-objective analysis on drilling of boron carbide reinforced metal matrix composites using grey relational analysis. Measurement, 47:321–330, 2014. doi: 10.1016/j.measurement.2013.08.040.
[60] B.B. Nayak, K. Abhishek, S.S. Mahapatra, and D. Das. Application of WPCA based Taguchi method for multi-response optimization of abrasive jet machining process. Materials Today: Proceedings, 5(2):5138–5144, 2018. doi: 10.1016/j.matpr.2017.12.095.
[61] K. Palanikumar, L. Karunamoorthy, and N. Manoharan. Mathematical model to predict the surface roughness on the machining of glass fiber reinforced polymer composites. Journal of Reinforced Plastics and Composites, 25(4):407–419, 2006. doi: 10.1177/0731684405060568.
[62] R. Świercz, D. Oniszczuk-Świercz, and L. Dabrowski. Electrical discharge machining of difficult to cut materials. Archive of Mechanical Engineering, 65(4):461–476, 2018. doi: 10.24425/ame.2018.125437.
[63] A. Hamdi, S.M. Merghache, and T. Aliouane. Effect of cutting variables on bearing area curve parameters (BAC-P) during hard turning process. Archive of Mechanical Engineering, 67(1):73–95, 2020. doi: 10.24425/ame.2020.131684.
[64] V. Kavimani, K.S. Prakash, and T. Thankachan. Influence of machining parameters on wire electrical discharge machining performance of reduced graphene oxide/magnesium composite and its surface integrity characteristics. Composites Part B: Engineering, 167:621–630, 2019. doi: 10.1016/j.compositesb.2019.03.031.
[65] Y. Quan and L. Sun. Investigation on drilling-induced delamination of CFRP with infiltration method. Advanced Materials Research, 139–141:55–58, 2010. doi: 10.4028/www.scientific.net/AMR.139-141.55.
[66] O. Isbilir and E. Ghassemieh. Delamination and wear in drilling of carbon-fiber reinforced plastic composites using multilayer TiAlN/TiN PVD-coated tungsten carbide tools. Journal of Reinforced Plastics and Composites, 31(10):717–727, 2012. doi: 10.1177/0731684412444653.
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Autorzy i Afiliacje

Kumar Jogendra
1
Rajesh Kumar Verma
1
Arpan Kumar Mondal
2

  1. Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.
  2. Department of Mechanical Engineering, National Institute of Technical Teachers Training and Research, Kolkata, India.

Instrukcja dla autorów

About the Journal
Archive of Mechanical Engineering is an international journal publishing works of wide significance, originality and relevance in most branches of mechanical engineering. The journal is peer-reviewed and is published both in electronic and printed form. Archive of Mechanical Engineering publishes original papers which have not been previously published in other journal, and are not being prepared for publication elsewhere. The publisher will not be held legally responsible should there be any claims for compensation. The journal accepts papers in English.

Archive of Mechanical Engineering is an Open Access journal. The journal does not have article processing charges (APCs) nor article submission charges.

Original high quality papers on the following topics are preferred:

  • Mechanics of Solids and Structures,
  • Fluid Dynamics,
  • Thermodynamics, Heat Transfer and Combustion,
  • Machine Design,
  • Computational Methods in Mechanical Engineering,
  • Robotics, Automation and Control,
  • Mechatronics and Micro-mechanical Systems,
  • Aeronautics and Aerospace Engineering,
  • Heat and Power Engineering.

All submissions to the AME should be made electronically via Editorial System - an online submission and peer review system at: https://www.editorialsystem.com/ame

More detailed instructions for Authors can be found there.

Recenzenci


The Editorial Board of the Archive of Mechanical Engineering (AME) sincerely expresses gratitude to the following individuals who devoted their time to review papers submitted to the journal. Particularly, we express our gratitude to those who reviewed papers several times.

List of reviewers in 2023

Sara I. ABDELSALAM – University of California Riverside, United States
M. ARUNA – Liwa College of Technology, United Arab Emirates
Krzysztof BADYDA – Warsaw University of Technology, Poland
Nathalie BÄSCHLIN – Kunstmuseum Bern, Germany
Joanna BIJAK – Silesian University of Technology, Gliwice, Poland
Tomas BODNAR – The Czech Academy of Sciences, Prague, Czech Republic
Dariusz BUTRYMOWICZ – Białystok University of Technology, Poland
Suleyman CAGAN – Mechanical Engineering, Mersin University, Turkey
Claudia CASAPULLA – University of Naples Federico II, Italy
Peng CHEN – Northwestern Polytechnical University, Xi’an, China
Yao CHENG – Southwest Jiaotong University, Chengdu, China
Jan de JONG – University of Twente, Netherlands
Mariusz DEJA – Gdańsk University of Technology, Poland
Jerzy EJSMONT – Gdańsk University of Technology, Poland
İsmail ESEN – Karabuk University, Turkey
Pedro Javier GAMEZ-MONTERO – Universitat Politecnica de Catalunya, Spain
Aman GARG – National Institute of Technology, Kurukshetra, India
Michał HAĆ – Warsaw University of Technology, Poland
Satoshi ISHIKAWA – Kyushu University, Japan
Jacek JACKIEWICZ – Kazimierz Wielki University, Bydgoszcz, Poland
Krzysztof JAMROZIAK – Wrocław University of Technology, Poland
Hong-Lae JANG – Changwon National University, Korea (South)
Łukasz JANKOWSKI – Institute of Fluid-Flow Machinery, PAS, Gdansk, Poland
Albizuri JOSEBA – University of the Basque Country, Spain
Łukasz KAPUSTA – Warsaw University of Technology, Poland
Dariusz KARDAŚ – Institute of Fluid-Flow Machinery, PAS, Gdansk, Poland
Panagiotis KARMIRIS-OBRATAŃSKI – AGH University of Science and Technology, Cracow, Poland
Sivakumar KARTHIKEYAN – SRM Nagar
Tarek KHELFA – Hunan University of Humanities Science and Technology, China
Sven-Joachim KIMMERLE – Universität der Bundeswehr München, Germany
Thomas KLETSCHKOWSKI – HAW Hamburg, Germany
Piotr KLONOWICZ – Institute of Fluid-Flow Machinery, PAS, Gdansk, Poland
Vladis KOSSE – Queensland University of Technology, Australia
Mariusz KOSTRZEWSKI – Warsaw University of Technology, Poland
Maria KOTELKO – Lodz University of Technology, Poland
Michał KOWALIK – Warsaw University of Technology, Poland
Zbigniew KRZEMIANOWSKI – Institute of Fluid-Flow Machinery, Gdańsk, Poland
Slawomir KUBACKI – Warsaw University of Technology, Poland
Mieczysław KUCZMA – Poznan University of Technology, Poland
Waldemar KUCZYŃSKI – The Koszalin University of Technology, Poland
Rafał KUDELSKI – AGH University of Science and Technology, Cracow, Poland
Rajesh KUMAR – Sant Longowal Institute of Engineering and Technology, India
Mustafa KUNTOĞLU – Selcuk University, Turkey
Anna LEE – Pohang University of Science and Technology, South Korea, Korea (South)
Guolong LI – Chongqing University, China
Luxian LI – Xi'an Jiaotong University, China
Yingchao LI – Ludong University, Yantai, China
Xiaochuan LIN – Nanjing Tech University, China
Zhihong LIN – HuaQiao University, China
Yakun LIU – Massachusetts Institute of Technology, United States
Jinjun LU – Northwest University, Xiʼan, China
Paweł MACIĄG – Warsaw University of Technology, Poland
Paweł MALCZYK – Warsaw University of Technology, Poland
Emil MANOACH – Bulgarian Academy of Sciences, Sofia, Bulgaria
Mihaela MARIN – “Dunărea de Jos” University of Galati, Romania
Miloš MATEJIĆ – University of Kragujevac, Serbia
Krzysztof MIANOWSKI – Warsaw University of Technology, Poland
Tran MINH TU – Hanoi University of Civil Engineering, Viet Nam
Farhad Sadegh MOGHANLOU – University of Mohaghegh Ardabili, Ardabil, Iran
Mohsen MOTAMEDI – University of Isfahan, Iran
Adis MUMINOVIC – University of Sarajevo, Bosnia and Herzegovina
Mohamed NASR – National Research Centre, Giza, Egypt
Huu-That NGUYEN – Nha Trang University, Viet Nam
Tan-Luy NGUYEN – Ho Chi Minh City University of Technology, Viet Nam
Viorel PALEU – Gheorghe Asachi Technical University of Iasi, Romania
Nicolae PANC – Technical University of Cluj-Napoca, Romania
Marcin PĘKAL – Warsaw University of Technology, Poland
Van Vinh PHAM – Le Quy Don Technical University, Hanoi, Viet Nam
Vaclav PISTEK – Brno University of Technology, Czech Republic
Paweł PYRZANOWSKI – Warsaw University of Technology, Poland
Lei QIN – Beijing Information Science & Technology University, China
Milan RACKOV – University of Novi Sad, Serbia
Yuriy ROMASEVYCH – National University of Life and Environmental Sciences of Ukraine, Kiev, Ukraine
Artur RUSOWICZ – Warsaw University of Technology, Poland
Andrzej SACHAJDAK – Silesian University of Technology, Gliwice, Poland
Mirosław SEREDYŃSKI – Warsaw University of Technology, Poland
Maciej SUŁOWICZ – Cracow University of Technology, Poland
Biswajit SWAIN – National Institute of Technology, Rourkela, India
Tadeusz SZYMCZAK – Motor Transport Institute, Warsaw, Poland
Reza TAHERDANGKOO – Institute of Geotechnics, Freiberg, Germany
Rulong TAN – Chongqing University of Technology, China
Daniel TOBOŁA – Łukasiewicz Research Network - Cracow Institute of Technology, Poland
Milan TRIFUNOVIĆ – University of Niš, Serbia
Duong VU – Duy Tan University, Viet Nam
Shaoke WAN – Xi’an Jiaotong University, China
Dong WEI – Northwest A&F University, Yangling , China
Marek WOJTYRA – Warsaw University of Technology, Poland
Mateusz WRZOCHAL – Kielce University of Technology, Poland
Hugo YAÑEZ-BADILLO – TecNM: Tecnológico de Estudios Superiores de Tianguistenco, Mexico
Guichao YANG – Nanjing Tech University, China
Xiao YANG – Chongqing Technology and Business University, China
Yusuf Furkan YAPAN – Yildiz Technical University, Turkey
Luhe ZHANG – Chongqing University, China
Xiuli ZHANG – Shandong University of Technology, Zibo, China

List of reviewers in 2022
Isam Tareq ABDULLAH – Middle Technical University, Baghdad, Iraq
Ahmed AKBAR – University of Technology, Iraq
Nandalur AMER AHAMMAD – University of Tabuk, Saudi Arabia
Ali ARSHAD – Riga Technical University, Latvia
Ihsan A. BAQER – University of Technology, Iraq
Thomas BAR – Daimler AG, Stuttgart, Germany
Huang BIN – Zhejiang University, Zhoushan, China
Zbigniew BULIŃSKI – Silesian University of Technology, Poland
Onur ÇAVUSOGLU – Gazi University, Turkey
Ali J CHAMKHA – Duy Tan University, Da Nang , Vietnam
Dexiong CHEN – Putian University, China
Xiaoquan CHENG – Beihang University, Beijing, China
Piotr CYKLIS – Cracow University of Technology, Poland
Agnieszka DĄBSKA – Warsaw University of Technology, Poland
Raphael DEIMEL – Berlin University of Technology, Germany
Zhe DING – Wuhan University of Science and Technology, China
Anselmo DINIZ – University of Campinas, São Paulo, Brazil
Paweł FLASZYŃSKI – Institute of Fluid-Flow Machinery, Gdańsk, Poland
Jerzy FLOYRAN – University of Western Ontario, London, Canada
Xiuli FU – University of Jinan, China
Piotr FURMAŃSKI – Warsaw University of Technology, Poland
Artur GANCZARSKI – Cracow University of Technology, Poland
Ahmad Reza GHASEMI– University of Kashan, Iran
P.M. GOPAL – Anna University, Regional Campus Coimbatore, India
Michał GUMNIAK – Poznan University of Technology, Poland
Bali GUPTA – Jaypee University of Engineering and Technology, India
Dmitriy GVOZDYAKOV – Tomsk Polytechnic University, Russia
Jianyou HAN – University of Science and Technology, Beijing, China
Tomasz HANISZEWSKI – Silesian University of Technology, Poland
Juipin HUNG – National Chin-Yi University of Technology, Taichung, Taiwan
T. JAAGADEESHA – National Institute of Technology, Calicut, India
Jacek JACKIEWICZ – Kazimierz Wielki University, Bydgoszcz, Poland
JC JI – University of Technology, Sydney, Australia
Feng JIAO – Henan Polytechnic University, Jiaozuo, China
Daria JÓŹWIAK-NIEDŹWIEDZKA – Institute of Fundamental Technological Research, Warsaw, Poland
Rongjie KANG – Tianjin University, China
Dariusz KARDAŚ – Institute of Fluid-Flow Machinery, Gdansk, Poland
Leif KARI – KTH Royal Institute of Technology, Sweden
Daria KHANUKAEVA – Gubkin Russian State University of Oil and Gas, Russia
Sven-Joachim KIMMERLE – Universität der Bundeswehr München, Germany
Yeong-Jin KING – Universiti Tunku Abdul Rahman, Malaysia
Kaushal KISHORE – Tata Steel Limited, Jamshedpur, India
Nataliya KIZILOVA – Warsaw University of Technology, Poland
Adam KLIMANEK – Silesian University of Technology, Poland
Vladis KOSSE – Queensland University of Technology, Australia
Maria KOTEŁKO – Lodz University of Technology, Poland
Roman KRÓL – Kazimierz Pulaski University of Technology and Humanities in Radom, Poland
Krzysztof KUBRYŃSKI – Airforce Institute of Technology, Warsaw, Poland
Mieczysław KUCZMA – Poznan University of Technology, Poland
Paweł KWIATOŃ – Czestochowa University of Technology, Poland
Lihui Lang – Beihang University, China
Rafał LASKOWSKI – Warsaw University of Technology, Poland
Guolong Li – Chongqing University, China
Leo Gu LI – Guangzhou University, China
Pengnan LI – Hunan University of Science and Technology, China
Nan LIANG – University of Toronto, Mississauga, Canada
Michał LIBERA – Poznan University of Technology, Poland
Wen-Yi LIN – Hungkuo Delin University of Technology, Taiwan
Wojciech LIPINSKI – Austrialian National University, Canberra, Australia
Linas LITVINAS – Vilnius University, Lithuania
Paweł MACIĄG – Warsaw University of Technology, Poland
Krishna Prasad MADASU – National Institute of Technology Raipur, Chhattisgarh, India
Trent MAKI – Amino North America Corporation, Canada
Marco MANCINI – Institut für Energieverfahrenstechnik und Brennstofftechnik, Germany
Piotr MAREK – Warsaw University of Technology, Poland
Miloš MATEJIĆ – University of Kragujevac, Serbia
Phani Kumar MEDURI – VIT-AP University, Amaravati, India
Fei MENG – University of Shanghai for Science and Technology, China
Saleh MOBAYEN – University of Zanjan, Iran
Vedran MRZLJAK – Rijeka University, Croatia
Adis MUMINOVIC – University of Sarajevo, Bosnia and Herzegovina
Mohamed Fawzy NASR – National Research Centre, Giza, Egypt
Paweł OCŁOŃ – Cracow University of Technology, Poland
Yusuf Aytaç ONUR – Zonguldak Bulent Ecevit University, Turkey
Grzegorz ORZECHOWSKI – LUT University, Lappeenranta, Finland
Halil ÖZER – Yıldız Technical University, Turkey
Muthuswamy PADMAKUMAR – Technology Centre Kennametal India Ltd., Bangalore, India
Viorel PALEU – Gheorghe Asachi Technical University of Iasi, Romania
Andrzej PANAS – Warsaw Military Academy, Poland
Carmine Maria PAPPALARDO – University of Salerno, Italy
Paweł PARULSKI – Poznan University of Technology, Poland
Antonio PICCININNI – Politecnico di Bari, Italy
Janusz PIECHNA – Warsaw University of Technology, Poland
Vaclav PISTEK – Brno University of Technology, Czech Republic
Grzegorz PRZYBYŁA – Silesian University of Technology, Poland
Paweł PYRZANOWSKI – Warsaw University of Technology, Poland
K.P. RAJURKARB – University of Nebraska-Lincoln, United States
Michał REJDAK – Institute of Chemical Processing of Coal, Zabrze, Poland
Krzysztof ROGOWSKI – Warsaw University of Technology, Poland
Juan RUBIO – University of Minas Gerais, Belo Horizonte, Brazil
Artur RUSOWICZ – Warsaw University of Technology, Poland
Wagner Figueiredo SACCO – Universidade Federal Fluminense, Petropolis, Brazil
Andrzej SACHAJDAK – Silesian University of Technology, Poland
Bikash SARKAR – NIT Meghalaya, Shillong, India
Bozidar SARLER – University of Lubljana, Slovenia
Veerendra SINGH – TATA STEEL, India
Wieńczysław STALEWSKI – Institute of Aviation, Warsaw, Poland
Cyprian SUCHOCKI – Institute of Fundamental Technological Research, Warsaw, Poland
Maciej SUŁOWICZ – Cracov University of Technology, Poland
Wojciech SUMELKA – Poznan University of Technology, Poland
Tomasz SZOLC – Institute of Fundamental Technological Research, Warsaw, Poland
Oskar SZULC – Institute of Fluid-Flow Machinery, Gdansk, Poland
Rafał ŚWIERCZ – Warsaw University of Technology, Poland
Raquel TABOADA VAZQUEZ – University of Coruña, Spain
Halit TURKMEN – Istanbul Technical University, Turkey
Daniel UGURU-OKORIE – Federal University, Oye Ekiti, Nigeria
Alper UYSAL – Yildiz Technical University, Turkey
Yeqin WANG – Syndem LLC, United States
Xiaoqiong WEN – Dalian University of Technology, China
Szymon WOJCIECHOWSKI – Poznan University of Technology, Poland
Marek WOJTYRA – Warsaw University of Technology, Poland
Guenter WOZNIAK – Technische Universität Chemnitz, Germany
Guanlun WU – Shanghai Jiao Tong University, China
Xiangyu WU – University of California at Berkeley, United States
Guang XIA – Hefei University of Technology, China
Jiawei XIANG – Wenzhou University, China
Jinyang XU – Shanghai Jiao Tong University,China
Jianwei YANG – Beijing University of Civil Engineering and Architecture, China
Xiao YANG – Chongqing Technology and Business University, China
Oguzhan YILMAZ – Gazi University, Turkey
Aznifa Mahyam ZAHARUDIN – Universiti Teknologi MARA, Shah Alam, Malaysia
Zdzislaw ZATORSKI – Polish Naval Academy, Gdynia, Poland
S.H. ZHANG – Institute of Metal Research, Chinese Academy of Sciences, China
Yu ZHANG – Shenyang Jianzhu University, China
Shun-Peng ZHU – University of Electronic Science and Technology of China, Chengdu, China
Yongsheng ZHU – Xi’an Jiaotong University, China

List of reviewers of volume 68 (2021)
Ahmad ABDALLA – Huaiyin Institute of Technology, China
Sara ABDELSALAM – University of California, Riverside, United States
Muhammad Ilman Hakimi Chua ABDULLAH – Universiti Teknikal Malaysia Melaka, Malaysia
Hafiz Malik Naqash AFZAL – University of New South Wales, Sydney, Australia
Reza ANSARI – University of Guilan, Rasht, Iran
Jeewan C. ATWAL – Indian Institute of Technology Delhi, New Delhi, India
Hadi BABAEI – Islamic Azad University, Tehran, Iran
Sakthi BALAN – K. Ramakrishnan college of Engineering, Trichy, India
Leszek BARANOWSKI – Military University of Technology, Warsaw, Poland
Elias BRASSITOS – Lebanese American University, Byblos, Lebanon
Tadeusz BURCZYŃSKI – Institute of Fundamental Technological Research, Warsaw, Poland
Nguyen Duy CHINH – Hung Yen University of Technology and Education, Hung Yen, Vietnam
Dorota CHWIEDUK – Warsaw University of Technology, Poland
Adam CISZKIEWICZ – Cracow University of Technology, Poland
Meera CS – University of Petroleum and Energy Studies, Duhradun, India
Piotr CYKLIS – Cracow University of Technology, Poland
Abanti DATTA – Indian Institute of Engineering Science and Technology, Shibpur, India
Piotr DEUSZKIEWICZ – Warsaw University of Technology, Poland
Dinesh DHANDE – AISSMS College of Engineering, Pune, India
Sufen DONG – Dalian University of Technology, China
N. Godwin Raja EBENEZER – Loyola-ICAM College of Engineering and Technology, Chennai, India
Halina EGNER – Cracow University of Technology, Poland
Fehim FINDIK – Sakarya University of Applied Sciences, Turkey
Artur GANCZARSKI – Cracow University of Technology, Poland
Peng GAO – Northeastern University, Shenyang, China
Rafał GOŁĘBSKI – Czestochowa University of Technology, Poland
Andrzej GRZEBIELEC – Warsaw University of Technology, Poland
Ngoc San HA – Curtin University, Perth, Australia
Mehmet HASKUL – University of Sirnak, Turkey
Michal HATALA – Technical University of Košice, Slovak Republic
Dewey HODGES – Georgia Institute of Technology, Atlanta, United States
Hamed HONARI – Johns Hopkins University, Baltimore, United States
Olga IWASINSKA – Warsaw University of Technology, Poland
Emmanuelle JACQUET – University of Franche-Comté, Besançon, France
Maciej JAWORSKI – Warsaw University of Technology, Poland
Xiaoling JIN – Zhejiang University, Hangzhou, China
Halil Burak KAYBAL – Amasya University, Turkey
Vladis KOSSE – Queensland University of Technology, Brisbane, Australia
Krzysztof KUBRYŃSKI – Air Force Institute of Technology, Warsaw, Poland
Waldemar KUCZYŃSKI – Koszalin University of Technology, Poland
Igor KURYTNIK – State Higher School in Oswiecim, Poland
Daniel LESNIC – University of Leeds, United Kingdom
Witold LEWANDOWSKI – Gdańsk University of Technology, Poland
Guolu LI – Hebei University of Technology, Tianjin, China
Jun LI – Xi’an Jiaotong University, China
Baiquan LIN – China University of Mining and Technology, Xuzhou, China
Dawei LIU – Yanshan University, Qinhuangdao, China
Luis Norberto LÓPEZ DE LACALLE – University of the Basque Country, Bilbao, Spain
Ming LUO – Northwestern Polytechnical University, Xi’an, China
Xin MA – Shandong University, Jinan, China
Najmuldeen Yousif MAHMOOD – University of Technology, Baghdad, Iraq
Arun Kumar MAJUMDER – Indian Institute of Technology, Kharagpur, India
Paweł MALCZYK – Warsaw University of Technology, Poland
Miloš MATEJIĆ – University of Kragujevac, Serbia
Norkhairunnisa MAZLAN – Universiti Putra Malaysia, Serdang, Malaysia
Dariusz MAZURKIEWICZ – Lublin University of Technology, Poland
Florin MINGIREANU – Romanian Space Agency, Bucharest, Romania
Vladimir MITYUSHEV – Pedagogical University of Cracow, Poland
Adis MUMINOVIC – University of Sarajevo, Bosnia and Herzegovina
Baraka Olivier MUSHAGE – Université Libre des Pays des Grands Lacs, Goma, Congo (DRC)
Tomasz MUSZYŃSKI – Gdansk University of Technology, Poland
Mohamed NASR – National Research Centre, Giza, Egypt
Driss NEHARI – University of Ain Temouchent, Algeria
Oleksii NOSKO – Bialystok University of Technology, Poland
Grzegorz NOWAK – Silesian University of Technology, Gliwice, Poland
Iwona NOWAK – Silesian University of Technology, Gliwice, Poland
Samy ORABY – Pharos University in Alexandria, Egypt
Marcin PĘKAL – Warsaw University of Technology, Poland
Bo PENG – University of Huddersfield, United Kingdom
Janusz PIECHNA – Warsaw University of Technology, Poland
Maciej PIKULIŃSKI – Warsaw University of Technology, Poland
T.V.V.L.N. RAO – The LNM Institute of Information Technology, Jaipur, India
Andrzej RUSIN – Silesian University of Technology, Gliwice, Poland
Artur RUSOWICZ – Warsaw University of Technology, Poland
Benjamin SCHLEICH – Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Jerzy SĘK – Lodz University of Technology, Poland
Reza SERAJIAN – University of California, Merced, USA
Artem SHAKLEIN – Udmurt Federal Research Center, Izhevsk, Russia
G.L. SHI – Guangxi University of Science and Technology, Liuzhou, China
Muhammad Faheem SIDDIQUI – Vrije University, Brussels, Belgium
Jarosław SMOCZEK – AGH University of Science and Technology, Cracow, Poland
Josip STJEPANDIC – PROSTEP AG, Darmstadt, Germany
Pavel A. STRIZHAK – Tomsk Polytechnic University, Russia
Vadym STUPNYTSKYY – Lviv Polytechnic National University, Ukraine
Miklós SZAKÁLL – Johannes Gutenberg-Universität Mainz, Germany
Agnieszka TOMASZEWSKA – Gdansk University of Technology, Poland
Artur TYLISZCZAK – Czestochowa University of Technology, Poland
Aneta USTRZYCKA – Institute of Fundamental Technological Research, Warsaw, Poland
Alper UYSAL – Yildiz Technical University, Turkey
Gabriel WĘCEL – Silesian University of Technology, Gliwice, Poland
Marek WĘGLOWSKI – Welding Institute, Gliwice, Poland
Frank WILL – Technische Universität Dresden, Germany
Michał WODTKE – Gdańsk University of Technology, Poland
Marek WOJTYRA – Warsaw University of Technology, Poland
Włodzimierz WRÓBLEWSKI – Silesian University of Technology, Gliwice, Poland
Hongtao WU – Nanjing University of Aeronautics and Astronautics, China
Jinyang XU – Shanghai Jiao Tong University, China
Zhiwu XU – Harbin Institute of Technology, China
Zbigniew ZAPAŁOWICZ – West Pomeranian University of Technology, Szczecin, Poland
Zdzislaw ZATORSKI – Polish Naval Academy, Gdynia, Poland
Wanming ZHAI – Southwest Jiaotong University, Chengdu, China
Xin ZHANG – Wenzhou University of Technology, China
Su ZHAO – Ningbo Institute of Materials Technology and Engineering, China



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