Applied sciences

Archive of Mechanical Engineering

Content

Archive of Mechanical Engineering | 2020 | vol. 67 | No 3 |

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Abstract

This paper presents a new algorithm that approximates the forces that develop between a human hand and the handles of a climbing wall. A hand-to-handle model was developed using this algorithm for the Open Dynamics Engine physics solver, which can be plugged into a full-body climbing simulation to improve results. The model data are based on biomechanical measurements of the average population presented in previously published research. The main objective of this work was to identify maximum forces given hand orientation and force direction with respect to the climbing wall handles. Stated as a nonlinear programming problem, solution was achieved by applying a stochastic Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The algorithm for force approximation works consistently and provides reasonable results when gravity is neglected. However, including gravity results in a number of issues. Since the weight of the hand is small in relation to the hand-to-handle forces, neglecting gravity does not significantly affect the reliability and quality of the solution.

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Bibliography

[1] T. Bretl. Motion planning of multi-limbed robots subject to equilibrium constraints: the free-climbingrobot problem. The International Journal of Robotics Research, 25(4):317– 342, 2006. doi: 10.1177/0278364906063979.
[2] K. Naderi, J.Rajamäki, and P. Hämäläinen. Discovering and synthesizing humanoid climbing movements. ACM Transactions on Graphics, Los Angeles, 36(4):art.43, 2017. doi: 10.1145/3072959.3073707.
[3] A.T. Miller and P. K. Allen. Graspit! A versatile simulator for robotic grasping. IEEE Robotics &\ Automation Magazine, 11(4):110–122, 2004. doi: 10.1109/MRA.2004.1371616.
[4] M.R. Cutkosky. On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transactions on Robotics and Automation, 5(3):269–279, 1989.
[5] A. Herzog, P. Pastor, M. Kalakrishnan, L. Righetti, T. Asfour, and S. Schaal. Template-based learning of grasp selection. In 2012 IEEE International Conference onRobotics and Automation, pages 2379–2384, Saint Paul, USA, 14–18 May 2012. doi: 10.1109/ICRA.2012.6225271.
[6] D. Kappler, J. Bohg, and S. Schaal. Leveraging big data for grasp planning. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 4304–4311, Seattle, USA, 26–30 May 2015. doi: 10.1109/ICRA.2015.7139793.
[7] V. Lippiello, F. Ruggiero, B. Siciliano, and L. Villani. Visual grasp planning for unknown objects using a multifingered robotic hand. IEEE/ASME Transactions on Mechatronics, 18(3):1050–1059, 2013. doi: 10.1109/TMECH.2012.2195500.
[8] J. DeGol, A. Akhtar, B. Manja, and T. Bretl. Automatic grasp selection using a camera in a hand prosthesis. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 431–434, Orlando, USA, 16-20 August 2016. doi: 10.1109/EMBC.2016.7590732.
[9] C. Ferrari and J. Canny. Planning optimal grasps. In Proceedings of the 1992 IEEE International Conference on Robotics and Automation, pages 2290–2295, Nice, France, May 1992.
[10] R. Smith. Open Dynamics Engine: User Guide. 2006.
[11] C.J. Hasser. Force-Reflecting Antropomorphic Hand Masters. Technical Report AL/CF-TR- 1995-0110, Armstrong Laboratory, Ohio, USA, 1995.
[12] F. Wang, M. Shastri, C.L. Jones, V. Gupta, C. Osswald, X. Kang, D.G. Kamper, and N. Sarkar. Design and control of an actuated thumb exoskeleton for hand rehabilitation following stroke. In 2011 IEEE International Conference on Robotics and Automation, pages 3688–3693, Shanghai, China, 9-13 May 2011. doi: 10.1109/ICRA.2011.5980099.
[13] Y. Yoshii, H. Yuine, O. Kazuki, W-L. Tung, and T. Ishii. Measurement of wrist flexion and extension torques in different forearm positions. BioMedical Engineering OnLine, 14:art.115, 2015. doi: 10.1186/s12938-015-0110-9.
[14] S. Plagenhoef, F.G. Evans, and T. Abdelnour. Anatomical data for analyzing human motion. Research Quarterly for Exercise and Sport, 54(2):169–178, 1983. doi: 10.1080/02701367.1983.10605290.
[15] N. Niemi. Comparison of Open Dynamics Engine, Chrono and Mevea in simple multibody applications. PhD Thesis, LUT University, Lappeenranta, Finland, 2017.
[16] T. Erez, Y. Tassa, and E. Todorov. Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 4397–4404, Seattle, USA, 26-30 May 2015. doi: 10.1109/ICRA.2015.7139807.
[17] E. Drumwright, J. Hsu, N. Koenig, and D. Shell. Extending open dynamics engine for robotics simulation. In: N. Ando, S. Balakirsky, T. Hemker, M. Reggiani, and O. von Stryk, editors, Simulation, Modeling, and Programming for Autonomous Robots, Lecture Notes in Computer Science, 6472:38–50. Springer, Berlin, Heidelberg, 2010. doi: 10.1007/978-3-642-17319-6_7.
[18] M.J. Carré, S.E. Tomlinson, J.W. Collins, and R. Lewis. An assessment of the performance of grip enhancing agents used in sports applications. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 226(7):616–625, 2012. doi: 10.1177/1350650112439647.
[19] F.K. Fuss, G. Niegl, and A.M. Tan. Friction between hand and different surfaces under different conditions and its implication for sport climbing. In: The Engineering of Sport 5, volume 2, pages 269–275, University of California, Davis, 2004.
[20] M.G.E. Schneiders, M.J.G. van de Molengraft, and M. Steinbuch. Benefits of over-actuation in motion systems. In Proceedings of the 2004 American Control Conference, volume 1, pages 505–510, Boston, USA, June 2004. doi: 10.23919/ACC.2004.1383653.
[21] A.E. Flatt. Grasp. Baylor University Medical Center Proceedings, 13(4):343–348, 2000. doi: 10.1080/08998280.2000.11927702.
[22] M. Duan. Energy-Optimal Control of Over-Actuated Systems – with Application to a Hybrid Feed Drive. Ph.D. Thesis, University of Michigan, Ann Arbor, Michigan, USA, 2018.
[23] N. Hansen. The CMA Evolution Strategy: A Comparing Review. In: J.A. Lozano, P. Larrañaga, I. Inza, and E. Bengoetxea, editors, Towards a New Evolutionary Computation, Studies in Fuzziness and Soft Computing, vol. 192, pages 75–102. Springer, Berlin, Heidelberg, 2006. doi: 10.1007/3-540-32494-1_4.
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Authors and Affiliations

Grzegorz Orzechowski
1 2
Perttu Hämäläinen
3
Aki Mikkola
1

  1. Department of Mechanical Engineering, LUT University, Lappeenranta, Finland.
  2. Mevea Ltd., Lappeenranta, Finland.
  3. Department of Computer Science, Aalto University, Espoo, Finland.
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Abstract

Shaft is a machine element which is used to transmit rotary motion or torque. During transmission of motion, however, the machine shaft doesn't always rotate with a constant angular velocity. Because of unstable current or due to sudden acceleration and deceleration, the machine shaft will rotate at a variable angular velocity. It is this rotary motion that generates the moment of inertial force, causing the machine shaft to have torsional deformation. However, due to the elasticity of the material, the shaft produces torsional vibration. Therefore, the main objective of this paper is to determine the optimal parameters of dynamic vibration absorber to eliminate torsional vibration of the rotating shaft that varies with time. The new results in this paper are summarized as follows: Firstly, the author determines the optimal parameters by using the minimum quadratic torque method. Secondly, the maximization of equivalent viscous resistance method is used for determining the optimal parameters. Thirdly, the author gives the optimal parameters of dynamic vibration absorber based on the fixed-point method. In this paper, the optimum parameters are found in an explicit analytical solutions, helping the scientists to easily find the optimal parameters for eliminating torsional vibration of the rotating shaft.

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Bibliography

[1] G.B. Warburton. Optimum absorber parameters for various combinations of response and excitation parameters. Earthquake Engineering and Structural Dynamics, 10(3):381–401, 1982. doi: 10.1002/eqe.4290100304.
[2] R.W. Luft. Optimal tuned mass dampers for buildings. Journal of the Structural Division, 105(12): 2766–2772, 1979.
[3] J.P. Den Hartog. Mechanical Vibrations. 4th edition, McGraw-Hill, New York, 1956.
[4] E.S. Taylor. Eliminating crankshaft torsional vibration in radial aircraft engines. SAE Technical Paper 360105, 1936. doi: 10.4271/360105.
[5] R.R.R. Sarazin. Means adapted to reduce the torsional oscillations of crankshafts. Patent 2079226, USA, 1937.
[6] J.F. Madden. Constant frequency bifilar vibration absorber. Patent 4218187, USA, 1980.
[7] H.H. Denman. Tautochronic bifilar pendulum torsion absorbers for reciprocating engines. Journal of Sound and Vibration, 159(2):251–277, 1992. doi: 10.1016/0022-460X(92)90035-V.
[8] C.P. Chao, S.H. Shaw, and C.T. Lee. Stability of the unison response for a rotating system with multiple tautochronic pendulum vibration absorbers. Journal of Applied Mechanics, 64(1):149–156, 1997. doi: 10.1115/1.2787266.
[9] C.T. Lee, S.W. Shaw, and V.T. Coppola. A subharmonic vibration absorber for rotating machinery. Journal of Vibration and Acoustics, 119(4):590–595, 1997. doi: 10.1115/1.2889766.
[10] A.S. Alsuwaiyan and S.W. Shaw. Performance and dynamic stability of general-path centrifugal pendulum vibration absorbers. Journal of Sound and Vibration, 252(5):791–815, 2002. doi: 10.1006/jsvi.2000.3534.
[11] S.W. Shaw, P.M. Schmitz, and A.G. Haddow. Tautochronic vibration absorbers for rotating systems. Journal of Computational and Nonlinear Dynamics, 1(4):283–293, 2006. doi: 10.1115/1.2338652.
[12] J. Mayet and H. Ulbrich. Tautochronic centrifugal pendulum vibration absorbers: General design and analysis. Journal of Sound and Vibration, 333(3):711–729, 2014. doi: 10.1016/j.jsv.2013.09.042.
[13] E. Vitaliani, D. Di Rocco, and M. Sopouch. Modelling and simulation of general path centrifugal pendulum vibration absorbers. SAE Technical Paper 2015-24-2387, 2015. doi: 10.4271/2015-24-2387.
[14] C. Shi, S.W. Shaw, and R.G. Parker. Vibration reduction in a tilting rotor using centrifugal pendulum vibration absorbers. Journal of Sound and Vibration, 385:55–68, 2016. doi: 10.1016/j.jsv.2016.08.035.
[15] K. Liu and J. Liu. The damped dynamic vibration absorbers: revisited and new result. Journal of Sound and Vibration, 284(3-5):1181–1189, 2005. doi: 10.1016/j.jsv.2004.08.002.
[16] N. Hoang, Y. Fujino, and P. Warnitchai. Optimal tuned mass damper for seismic applications and practical design formulas. Engineering Structures, 30(3):707–715, 2008. doi: 10.1016/j.engstruct.2007.05.007.
[17] G. Bekdaş and S.M. Nigdeli. Estimating optimum parameters of tuned mass dampers using harmony search. Engineering Structures, 33(9):2716–2723, 2011. doi: 10.1016/j.engstruct.2011.05.024.
[18] K. Ikago, K. Saito, and N. Inoue. Seismic control of single-degree-of-freedom structure using tuned viscous mass damper. Earthquake Engineering and Structural Dynamics, 41(3):453–474, 2012. doi: 10.1002/eqe.1138.
[19] H. Garrido, O. Curadelli, and D. Ambrosini. Improvement of tuned mass damper by using rotational inertia through tuned viscous mass damper. Engineering Structures, 56:2149–2153, 2013. doi: 10.1016/j.engstruct.2013.08.044.
[20] M.G. Soto and H. Adeli. Tuned mass dampers. Archives of Computational Methods in Engineering, 20(4):419–431, 2013. doi: 10.1007/s11831-013-9091-7.
[21] X.T. Vu, N.D. Chinh, D.D. Khong, and V.C Tong. Closed-form solutions to the optimization of dynamic vibration absorber attached to multi-degree-of-freedom damped linear systems under torsional excitation using the fixed-point theory. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multibody Dynamics, 232(2):237–252, 2018. doi: 10.1177/1464419317725216.
[22] N.D. Chinh. Determination of optimal parameters of the tuned mass damper to reduce the torsional vibration of the shaft by using the principle of minimum kinetic energy. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multibody Dynamics, 233(2):327–335, 2019. doi: 10.1177/1464419318804064.
[23] N.D. Chinh. Optimal parameters of tuned mass dampers for machine shaft using the maximum equivalent viscous resistance method. Journal of Science and Technology in Civil Engineering, 14(1): 127–135, 2020. doi: 10.31814/stce.nuce2020-14(1)-11.
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Authors and Affiliations

Nguyen Duy Chinh
1

  1. Faculty of Mechanical Engineering, Hung Yen University of Technology and Education, HungYen, Vietnam.
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Abstract

In this work, transient and free vibration analyses are illustrated for a functionally graded Timoshenko beam (FGM) using finite element method. The governing equilibrium equations and boundary conditions (B-Cs) are derived according to the principle of Hamilton. The materials constituents of the FG beam that vary smoothly along the thickness of the beam (along beam thickness) are evaluated using the rule of mixture method. Power law index, slenderness ratio, modulus of elasticity ratio, and boundary conditions effect of the cantilever and simply supported beams on the dynamic response of the beam are studied. Moreover, the influence of mass distribution and continuous stiffness of the FGM beam are deeply investigated. Comparisons between the current free vibration results (fundamental frequency) and other available studies are performed to check the formulation of the current mathematical model. Good results have been obtained. A significant effect is noticed in the transient response of both simply supported and cantilever beams at the smaller values of the power index and the modulus elasticity ratio.

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Bibliography

[1] B.V. Sankar. An elasticity solution for functionally graded beams. Composites Science and Technology,61(5):689–96, 2001. doi: 10.1016/S0266-3538(01)00007-0.
[2] M. Şimşek. . Static analysis of a functionally graded beam under a uniformly distributed load by Ritz method. International Journal of Engineering and Applied Sciences, 1(3):1–11, 2009.
[3] S.A. Sina, H.M. Navazi, and H. Haddadpour. An analytical method for free vibration analysis of functionally graded beams. Materials & Design, 30(3):741–747, 2009. doi: 10.1016/j.matdes.2008.05.015.
[4] A. Chakrabarty, S. Gopalakrishnan, and J.N. Reddy. A new beam finite element for the analysis of functionally graded materials. International Journal of Mechanical Sciences, 45(3):519–539, 2003. doi: 10.1016/S0020-7403(03)00058-4.
[5] M. Al-Shujairi and Ç. Mollamahmutoğlu. Dynamic stability of sandwich functionally graded micro-beam based on the nonlocal strain gradient theory with thermal effect. Composite Structures, 201:1018–1030, 2018. doi: 10.1016/j.compstruct.2018.06.035.
[6] H.T. Thai and T.P. Vo. Bending and free vibration of functionally graded beams using various higher-order shear deformation beam theories. International Journal of Mechanical Sciences, 62(1):57–66, 2012. doi: 10.1016/j.ijmecsci.2012.05.014.
[7] M. Al-Shujairi and Ç. Mollamahmutoğlu. Buckling and free vibration analysis of functionally graded sandwich micro-beams resting on elastic foundation by using nonlocal strain gradient theory in conjunction with higher order shear theories under thermal effect. Composites Part B: Engineering, 154:292–312, 2018. doi: 10.1016/j.compositesb.2018.08.103.
[8] M. Şimşek and M. Al Shujairi. Static, free and forced vibration of functionally graded (FG) sandwich beams excited by two successive moving harmonic loads. Composites Part B: Engineering,108:18–34, 2017. doi: 10.1016/j.compositesb.2016.09.098.
[9] M. Aydogdu and V. Taskin. Free vibration analysis of functionally graded beams with simply supported edges. Materials & Design, 28(5):1651–1656, 2007. doi: 10.1016/j.matdes.2006.02.007.
[10] M. Şimşek and T. Kocatürk. Free and forced vibration of a functionally graded beam subjected to a concentrated moving harmonic load. Composite Structures, 90(4):465–473, 2009. doi: 10.1016/j.compstruct.2009.04.024.
[11] K.K. Pradhan and S. Chakrabaty. Free vibration of Euler and Timoshenko functionally graded beams by Rayleigh-Ritz method. Composites Part B: Engineering, 51:175–184, 2013. doi: 10.1016/j.compositesb.2013.02.027.
[12] Y. Yang, C.C. Lam, K.P. Kou, and V.P. Iu. Free vibration analysis of the functionally graded sandwich beams by a meshfree boundary-domain integral equation method. Composite Structures, 117:32–39, 2014. doi: 10.1016/j.compstruct.2014.06.016.
[13] L.L. Ke, J. Yang, S. Kitipornchai, and Y. Xiang. Flexural vibration and elastic buckling of a cracked Timoshenko beam made of functionally graded materials. Mechanics of Advanced Materials and Structures, 16(6):488–502, 2009. doi: 10.1080/15376490902781175.
[14] M. Şimşek. Fundamental frequency analysis of functionally graded beams by using different higher-order beam theories. Nuclear Engineering and Design, 240(4):697–705, 2010. doi: 10.1016/j.nucengdes.2009.12.013.
[15] M. Şimşek. Bi-directional functionally graded materials (BDFGMs) for free and forced vibration of Timoshenko beams with various boundary conditions. Composite Structures, 133:968–78, 2015. doi: 10.1016/j.compstruct.2015.08.021.
[16] J. Yang, Y. Chen, Y. Xiang, and X.L. Jia. Free and forced vibration of cracked inhomogeneous beams under an axial force and a moving load. Journal of Sound and Vibration, 312(1–2):166–181, 2008. doi: 10.1016/j.jsv.2007.10.034.
[17] S. Kapuria, M. Bhattacharyya, and A.N. Kumar. Bending and free vibration response of layered functionally graded beams: a theoretical model and its experimental validation. Composite Structures, 82(3):390–402, 2008. doi: 10.1016/j.compstruct.2007.01.019.
[18] J. Yang and Y. Chen. Free vibration and buckling analyses of functionally graded beams with edge cracks. Composite Structures, 83(1):48–60, 2008. doi: 10.1016/j.compstruct.2007.03.006.
[19] A. Doroushi, M.R. Eslami, and A. Komeili. Vibration analysis and transient response of an FGPM beam under thermo-electro-mechanical loads using higher-order shear deformation theory. Journal of Intelligent Material Systems and Structures, 22(3):231–243, 2011. doi: 10.1177/1045389X11398162.
[20] M.J. Aubad, S.O.W. Khafaji, M.T. Hussein, and M.A. Al-Shujairi. Modal analysis and transient response of axially functionally graded (AFG) beam using finite element method. Materials Research Express, 6(10):1065g4, 2019. doi: 10.1088/2053-1591/ab4234.
[21] Z. Su, G. Jin, and T. Ye. Vibration analysis and transient response of a functionally graded piezoelectric curved beam with general boundary conditions. Smart Materials and Structures, 25(6):065003, 2016. doi: 10.1088/0964-1726/25/6/065003.
[22] A. Daga, N. Ganesan, and K. Shankar. Transient dynamic response of cantilever magneto-electro-elastic beam using finite elements. International Journal for Computational Methods in Engineering Science and Mechanics, 10(3):173–185, 2009. doi: 10.1080/15502280902797207.
[23] Z.N. Li, Y.X. Hao, W. Zhang, and J.H. Zhang. Nonlinear transient response of functionally graded material sandwich doubly curved shallow shell using new displacement field. Acta Mechanica Solida Sinica, 31(1):108–126, 2018. doi: 10.1007/s10338-018-0008-8.
[24] Y. Huang and Y. Huang. A real-time transient analysis of a functionally graded material plate using reduced-basis methods. Advances in Linear Algebra & Matrix Theory, 5(3):98–108, 2015. doi: 10.4236/alamt.2015.53010.
[25] T. Yokoyama. Vibrations and transient response of Timoshenko beams resting on elastic foundation. Ingenieur Archiv, 57:81–90, 1987. doi: 10.1007/BF00541382.
[26] A.E. Alshorbagy, M.A. Eltaher, and F.F. Mahmoud. Free vibration characteristics of a functionally graded beam by finite element method. Applied Mathematical Modelling, 35(1):412–425, 2011. doi: 10.1016/j.apm.2010.07.006.
[27] S. Taeprasartsit. Nonlinear free vibration of thin functionally graded beams using the finite element method. Journal of Vibration and Control, 21(1):29–46, 2015. doi: 10.1177/1077546313484506.
[28] N. Pradhan and S.K. Sarangi. Free vibration analysis of functionally graded beams by finite element method. IOP Conference Series: Materials Science and Engineering, 377:012211, 2018. doi: 10.1088/1757-899X/377/1/012211.
[29] N. Fouda, T. El-Midany, and A.M. Sadoun. Bending, buckling and vibration of a functionally graded porous beam using finite elements. Journal of Applied and Computational Mechanics, 3(4):274–282, 2017. doi: 10.22055/jacm.2017.21924.1121.
[30] L.L. Jing, P.J. Ming, W.P. Zhang, L.R. Fu, and Y.P. Cao. Static and free vibration analysis of functionally graded beams by combination Timoshenko theory and finite volume method. Composite Structures, 138:192–213, 2016. doi: 10.1016/j.compstruct.2015.11.027.
[31] V. Kahya and M. Turan. Finite element model for vibration and buckling of functionally graded beams based on the first-order shear deformation theory. Composites Part B: Engineering, 109:108–115, 2017. doi: 10.1016/j.compositesb.2016.10.039.
[32] H. Su, J.R. Banerjee, and C.W. Cheung. Dynamic stiffness formulation and free vibration analysis of functionally graded beams. Composite Structures, 106:854–862, 2013. doi: 10.1016/j.compstruct.2013.06.029.
[33] Z. Friedman and J.B. Kosmatka. An improved two-node Timoshenko beam finite element. Computers & Structures, 47(3):473–481, 1993. doi: 10.1016/0045-7949(93)90243-7.
[34] Y.H. Lin. Vibration analysis of Timoshenko beams traversed by moving loads. Journal of Marine Science and Technology. 2(1):25–35, 1994.
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Authors and Affiliations

Salwan Obaid Waheed Khafaji
1
Mohammed A. Al-Shujairi
1
Mohammed Jawad Aubad
1

  1. Department of Mechanical Engineering, Faculty of Engineering, University of Babylon, BabylonProvince, Iraq.
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Abstract

The article describes a test stand with a spindle equipped with an active bearing preload system using piezoelectric actuators. The proper functioning of the spindle and the active system was associated with the correct alignment of the spindle shaft and the drive motor. The article presents two methods of shaft alignment. The use of commonly known shaft alignment methods with dial indicators is insufficient from the viewpoint of being able to control this preload. This work aims at making the readers aware that, for systems with active bearing preload, the latest measuring devices should be used to align the shaft. The use of commonly known methods of equalization with dial gauges is insufficient from the point of view of controlling this preload. Increasing the accuracy of shaft alignment from 0.1 to 0.01 mm made it possible to obtain a 50% reduction in the displacement of the outer bearing ring during spindle operation.

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Bibliography

[1] F. Chen and G. Liu. Active damping of machine tool vibrations and cutting force measurement with a magnetic actuator. The International Journal of Advanced Manufacturing Technology, 89(1–4):691–700, 2017. doi: 10.1007/s00170-016-9118-y.
[2] A.H. Hadi Hosseinabadi and Y. Altintas. Modelling and active damping of structural vibrations in machine tools. CIRP Journal of Manufacturing Science and Technology, 7(3):246–257, 2014. doi: 10.1016/j.cirpj.2014.05.001.
[3] Y.K. Hwang and Ch.M. Lee. Development of a newly structured variable preload control device for a spindle rolling bearing by using an electromagnet. International Journal of Machine Tools and Manufacture, 50(3):253–259, 2010. doi: 10.1016/j.ijmachtools.2009.12.002.
[4] G. Quintana, J. de Ciurana, and F.J. Campa. Machine tool spindles. In: L.N. Lopez de Lacalle and Lamikiz (Eds.) Machine Tools for High Performance Machining, chapter 3, pages 75–126, Springer–Verlag, London, 2009.
[5] J. Sikorski and W. Pawłowski. Innovative designs of angular contact ball bearings systems preload mechanisms. Mechanik, 92(2):138–140, 2018. doi: 10.17814/mechanik.2018.2.29.
[6] J.S. Chen and K.W. Chen. Bearing load analysis and control of a motorized high speed spindle. International Journal of Machine Tools and Manufacture, 45(12-13):1487–1493, 2005. doi: 10.1016/j.ijmachtools.2005.01.024.
[7] P. Harris, B. Linke, and S. Spence. An energy analysis of electric and pneumatic ultra-high speed machine tool spindles. Procedia CIRP, 29:239–244, 2015.
[8] J. Dwojak and M. Rzepiela. Vibration Diagnostics of Machines and Devices. 2nd ed. Wyd. Biuro Gamma, Warsaw, Poland, 2005. (in Polish).
[9] G. Hagiu and B. Dragan. Feedback preload systems for high speed rolling bearings assemblies. The Annals of University Dunarea De Jos of Galati Fascicle VIII, 43–47, 2004.
[10] J. Kosmol and K. Lehrich. Electro spindle thermal model. Modelowanie Inżynierskie, 39:119–126, 2010. (in Polish).
[11] J. Vyroubal. Compensation of machine tool thermal deformation in spindle axis direction based on decomposition method. Precision Engineering, 36(1):121–127, 2012. doi: 10.1016/j.precisioneng.2011.07.013.
[12] J. Piotrowski. Shaft Alignment Handbook. 3rd edition. CRC Press, Boca Raton, 2006.
[13] S. Szymaniec. Research, Operation and Diagnostics of Machine Sets with Squirrel Cage Induction Motors. Wyd. Oficyna Wydawnicza Politechniki Opolskiej, Studia i Monografie, 333, Opole 2013. (in Polish).
[14] K.P. Anandan and O.B. Ozdoganlar. A multi-orientation error separation technique for spindle metrology of miniature ultra-high-speed spindles. Precision Engineering, 43:119–131, 2016. doi: 10.1016/j.precisioneng.2015.07.002.
[15] Z. Plutecki, S. Szymaniec, and J. Smykała. A new method for setting industrial drives. Zeszyty problemowe – maszyny elektryczne, 2(102), 201–207, 2014. (in Polish).
[16] J. Dwojak. The use of a laser to determine the alignment of machine shafts is a revolution in alignment. Transport Przemysłowy, 3, 2005. (in Polish).
[17] Shaft alignment, a professional system for measuring and aligning rotor machines. The Easy Laser Catalog. (in Polish).
[18] H. Krzemiński–Freda. Rolling Bearings. PWN, Warszawa, 1985. (in Polish).
[19] S. Waczyński. Shaft bearing using angular contact roller bearings and elastic element. Problems of unconventional bearing systems. A collection of Conference Works edited by J. Burcan, Łódź, 71–74, 1995. (in Polish).
[20] A. Parus, M. Pajor, and M. Hoffmann. Suppression of self-excited vibration in cutting process using piezoelectric and electromagnetic actuators. Advances in Manufacturing Science and Technology, 33(4):35–50, 2009.
[21] Operating Manual, Universal Amplifier QuantumX MX840A HBM, 2011.
[22] W. Modrzycki. Identification and compensation of machine tool errors. Inżynieria Maszyn, 13(3-4):91–100, 2008. (in Polish).
[23] P. Turek, W. Skoczyński, and M. Stembalski. Comparison of methods for adjusting and controlling the preload of angular-contact bearings. Journal of Machine Engineering. 16(2):71–85, 2016.
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Authors and Affiliations

Paweł Turek
1
Marek Stembalski
1

  1. Wrocław University of Science and Technology, Faculty of Mechanical Engineering, Wrocław, Poland.
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Abstract

In this paper, the energy losses in big band saw machines are investigated. These losses are caused by the geometric and angular inaccuracies with which the leading wheels are made. Expressions for calculating the kinetic energy of the mechanical system in the ideal and the real cases are obtained. For this purpose, expressions for calculating the velocities of the centers of the masses in two mutually perpendicular planes are obtained. A dependence for calculation of the kinetic energy losses of the mechanical system in final form is received. Optimization procedure is used to determine the values of the parameters at which these losses have minimum values. The proposed study can be used to minimize energy losses in other classes of woodworking machines.

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Bibliography

[1] M. Sarwar, M. Persson, H. Hellbergh, and J. Haider. Measurement of specific cutting energy for evaluating the efficiency of band sawing different workpiece materials. International Journal of Machine Tools and Manufacture, 49(11-12):958–965, 2009. doi: 10.1016/j.ijmachtools.2009.06.008.
[2] M. Mandic, S. Svrzic, and G. Danon. The comparative analysis of two methods for the power consumption measurement in circular saw cutting of laminated particle board. Wood Research, 60(1):125–136, 2015.
[3] Z. Kopecký, L. Hlaskova, and K. Orlowski. An innovative approach to prediction energetic effects of wood cutting process with circular-saw blades. Wood Research, 59(5):827–834, 2014.
[4] K. Orlowski, T. Ochrymiuk, A. Atkins, and D. Chuchala. Application of fracture mechanics for energetic effects predictions while wood sawing. Wood Science and Technology, 47(5):949–963, 2013. doi: 10.1007/s00226-013-0551-x.
[5] P. Iskra, C. Tanaka, and T. Ohtani. Energy balance of the orthogonal cutting process. Holz Als Roh- und Werkstoff, 63:358–364, 2005. doi: 10.1007/s00107-005-0021-8.
[6] P. Obreshkov. Woodworking Machines. Publishing House ``BM'', 1995. (in Bulgarian).
[7] A. Pisarev, Ts. Paraskov, and C. Bachvarov. Course in Theoretical Mechanics. Second part – Dynamics. State Publishing House Technics, 1988. (in Bulgarian).
[8] R.M. Dreizler, and C.S. Lüdde. Theoretical Mechanics: Theoretical Physics 1. Springer, Berlin, Heidelberg, 2010. doi: 10.1007/978-3-642-11138-9.
[9] F. Scheck. Mechanics. From Newton's Laws to Deterministic Chaos. 5th edition, Springer, Berlin, Heidelberg, 2010.
[10] B. Marinov. Dynamic and Shock Processes in Some Classes of Woodworking Machines. Analysis and Optimization. Omniscriptum Publishing Group-Germany/LAP LAMBERT Academic Publishing, 2018.
[11] B. Cheshankov. Theory of the Vibrations. Publishing House in TU, 1992. (in Bulgarian).
[12] B. Marinov. Spatial deformations in the transmissions of certain classes of woodworking machines. Mechanism and Machine Theory, 82:1–16, 2014. doi: 10.1016/j.mechmachtheory.2014.07.010.
[13] Zh. Gochev. Handbook for Exercise of Wood Cutting and Woodworking Tools. Publishing House in LTU, 2005. (in Bulgarian).
[14] Yo. Tonchev. Matlab, Part 3. Publishing House Technique, 2009. (in Bulgarian).
[15] R. Peters. Band Saw Fundamentals: The Complete Guide. Hearst Communications Inc, 2006.
[16] L. Bird. The Bandsaw Book. Taunton Press Inc, 2000.
[17] W. Turner. A Comprehensive Handbook on Uses and Applications of the Band Saw and Jig Saw. Literary Licensing LLC, 2013.
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Authors and Affiliations

Boycho Marinov
1

  1. The Institute of Mechanics, Bulgarian Academy of Sciences, Sofia, Bulgaria.
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Abstract

In manufacturing industries, the selection of machine parameters is a very complicated task in a time-bound manner. The process parameters play a primary role in confirming the quality, low cost of manufacturing, high productivity, and provide the source for sustainable machining. This paper explores the milling behavior of MWCNT/epoxy nanocomposites to attain the parametric conditions having lower surface roughness (Ra) and higher materials removal rate (MRR). Milling is considered as an indispensable process employed to acquire highly accurate and precise slots. Particle swarm optimization (PSO) is very trendy among the nature-stimulated metaheuristic method used for the optimization of varying constraints. This article uses the non-dominated PSO algorithm to optimize the milling parameters, namely, MWCNT weight% (Wt.), spindle speed (N), feed rate (F), and depth of cut (D). The first setting confirmatory test demonstrates the value of Ra and MRR that are found as 1:62 μm and 5.69 mm3/min, respectively and for the second set, the obtained values of Ra and MRR are 3.74 μm and 22.83 mm3/min respectively. The Pareto set allows the manufacturer to determine the optimal setting depending on their application need. The outcomes of the proposed algorithm offer new criteria to control the milling parameters for high efficiency.

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Bibliography

[1] M. Liu, H. Younes, H. Hong, and G.P. Peterson. Polymer nanocomposites with improved mechanical and thermal properties by magnetically aligned carbon nanotubes. Polymer, 166:81–87, 2019. doi: 10.1016/j.polymer.2019.01.031.
[2] S.K. Singh and V.K. Verma. Exact solution of flow in a composite porous channel. Archive of Mechanical Engineering, 67(1):97–110, 2020, doi: 10.24425/ame.2020.131685.
[3] N. Pundhir, S. Zafar, and H. Pathak. Performance evaluation of HDPE/MWCNT and HDPE/kenaf composites. Journal of Thermoplastic Composite Materials, 2019. doi: 10.1177/0892705719868278.
[4] N. Muralidhar, V. Kaliveeran, V. Arumugam, and I.S. Reddy. Dynamic mechanical characterization of epoxy composite reinforced with areca nut husk fiber. Archive of Mechanical Engineering, 67(1):57–72, 2020, doi: 10.24425/ame.2020.131683.
[5] F. Mostaani, M.R. Moghbeli, and H. Karimian. Electrical conductivity, aging behavior, and electromagnetic interference (EMI) shielding properties of polyaniline/MWCNT nanocomposites. Journal of Thermoplastic Composite Materials, 31(10):1393–1415, 2018. doi: 10.1177/0892705717738294.
[6] M.R. Sanjay, P. Madhu, M. Jawaid, P. Senthamaraikannan, S. Senthil, and S. Pradeep. Characterization and properties of natural fiber polymer composites: A comprehensive review. Journal of Cleaner Production, 172:566–581, 2018. doi: 10.1016/j.jclepro.2017.10.101.
[7] A.J. Valdani and A. Adamian. Finite element-finite volume simulation of underwater explosion and its impact on a reinforced steel plate. Archive of Mechanical Engineering, 67(1):5–30, 2020, doi: 10.24425/ame.2020.131681.
[8] A. Kausar, I. Rafique, and B. Muhammad. Review of applications of polymer/carbon nanotubes and epoxy/CNT composites. Polymer-Plastics Technology and Engineering, 55(11):1167–1191, 2016. doi: 10.1080/03602559.2016.1163588.
[9] E. Vajaiac, et al. Mechanical properties of multiwall carbon nanotube-epoxy composites. Digest Journal of Nanomaterials and Biostructures, 10(2):359–369, 2015.
[10] A.E Douba, M. Emiroglu, R.A Tarefder, U.F Kandil, and M.R. Taha. Use of carbon nanotubes to improve fracture toughness of polymer concrete. Journal of the Transportation Research Board, 2612(1):96–103, 2017. doi: 10.3141/2612-11.
[11] W. Khan, R. Sharma, and P. Saini. Carbon nanotube-based polymer composites: synthesis, properties and applications. In M.R. Berber and I.H. Hafez (eds.). Carbon Nanotubes. Current Progress and their Polymer Composites. chapter 1, pages 1-46. IntechOpen, Rijeka, Croatia, 2016. doi: 10.5772/62497.
[12] W.M. da Silva, H. Ribeiro, J.C. Neves, A.R. Sousa, and G.G. Silva. Improved impact strength of epoxy by the addition of functionalized multiwalled carbon nanotubes and reactive diluent. Journal of Applied Polymer Science, 132(39):1–12, 2015, doi: 10.1002/app.42587.
[13] S. Dixit, A. Mahata, D.R. Mahapatra, S.V. Kailas, and K. Chattopadhyay. Multi-layer graphene reinforced aluminum – Manufacturing of high strength composite by friction stir alloying. Composites Part B: Engineering,136: 63–71, 2018. doi: 10.1016/j.compositesb.2017.10.028.
[14] C. Kostagiannakopoulou, X. Tsilimigkra, G. Sotiriadis, and V. Kostopoulos. Synergy effect of carbon nano-fillers on the fracture toughness of structural composites. Composites Part B: Engineering, 129:18–25, 2017. doi: 10.1016/j.compositesb.2017.07.012.
[15] G. Romhány and G. Szebényi. Preparation of MWCNT reinforced epoxy nanocomposite and examination of its mechanical properties. Plastics, Rubber and Composites, 37(5-6):214–218, 2008. doi: 10.1179/174328908X309376.
[16] G. Mittal, V. Dhand, K.Y. Rhee, S.J. Park, and W.R. Lee. A review on carbon nanotubes and graphene as fillers in reinforced polymer nanocomposites. Journal of Industrial and Engineering Chemistry, 21:11–25, 2015. doi: 10.1016/j.jiec.2014.03.022.
[17] S. H. Behzad, M.J. Kimya, G. Mehrnaz. Mechanical properties of multi-walled carbon nanotube/epoxy polysulfide nanocomposite. Journal of Materials & Design, 50:62–67, 2013.
[18] N. Yu, Z.H. Zhang, and S.Y. He. Fracture toughness and fatigue life of MWCNT/epoxy composites. Materials Science and Engineering: A, 494(1-2):380:384, 2018. doi: 10.1016/j.msea.2008.04.051.
[19] J.G. Park, et al. Thermal conductivity of MWCNT/epoxy composites: The effects of length, alignment and functionalization. Carbon, 50(6):2083–2090, 2012. doi: 10.1016/j.carbon.2011.12.046.
[20] B. Singaravel and T. Selvaraj. Optimization of machining parameters in turning operation using combined TOPSIS and AHP method. Tehnički Vjesnik, 22 (6):1475–1480, 2015. doi: 10.17559/TV-20140530140610.
[21] N. Kaushik and S. Singhal. Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiC p matrix composite. Production & Manufacturing Research, 6(1):171–189, 2018. doi: 10.1080/21693277.2018.1479666.
[22] S.O.N. Raj and S. Prabhu. Analysis of multi objective optimisation using TOPSIS method in EDM process with CNT infused copper electrode. International Journal of Machining and Machinability of Materials, 19(1):76–94, 2017. doi: 10.1504/IJMMM.2017.081190.
[23] S. Chakraborty. Applications of the MOORA method for decision making in manufacturing environment. International Journal of Advanced Manufacturing Technology, 54(9-12):1155–1166, 2011. doi: 10.1007/s00170-010-2972-0.
[24] M.P. Jenarthanan and R. Jeyapaul. Optimisation of machining parameters on milling of GFRP composites by desirability function analysis using Taguchi method. International Journal of Engineering, Science and Technology, 5(4):23–36, 2013. doi: 10.4314/ijest.v5i4.3.
[25] T.V. Sibalija. Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018). Applied Soft Computing, 84:105743, ISSN 1568-4946, doi: 10.1016/j.asoc.2019.105743.
[26] J. Kennedy and R. Eberhart. Particle swarm optimization. In Proceedings of the ICNN'95 – International Conference on Neural Networks, pages 1942–1948, Perth, Australia, 27 Nov.–1 Dec. 1995. doi: 10.1109/ICNN.1995.488968.
[27] F. Cus and J. Balic. Optimization of cutting process by GA approach. Robotics and Computer-Integrated Manufacturing, 19(1-2):113–121, 2003. doi: 10.1016/S0736-5845(02)00068-6.
[28] M.N. Ab Wahab, S. Nefti-Meziani, and A. Atyabi. A comprehensive review of swarm optimization algorithms. PLoS One, 10(5): e0122827, 2015. doi: 10.1371/journal.pone.0122827.
[29] A. Del Prete, R. Franchi, and D. De Lorenzis. Optimization of turning process through the analytic flank wear modelling. AIP Conference Proceedings, 1960:070008, 2018.doi: 10.1063/1.5034904.
[30] G. Xu and Z. Yang. Multiobjective optimization of process parameters for plastic injection molding via soft computing and grey correlation analysis. International Journal of Advanced Manufacturing Technology, 78(1–4):525–536, 2015. doi: 10.1007/s00170-014-6643-4.
[31] H. Juan, S.F. Yu, and B.Y. Lee. The optimal cutting parameter selection of production cost in HSM for SKD61 tool steels. International Journal of Machine Tools and Manufacturing, 43 (7):679–686, 2003. doi: 10.1016/S0890-6955(03)00038-5.
[32] U. Zuperl and F. Cus. Optimization of cutting conditions during cutting by using neural networks. Robotics and Computer-Integrated Manufacturing, 19(1-2):189–199, 2003. doi: 10.1016/S0736-5845(02)00079-0.
[33] P.E. Amiolemhen and A.O.A. Ibhadode. Application of genetic algorithms – determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile. International Journal of Machine Tools and Manufacture, 44(12-13):1403–1412, 2004. doi: 10.1016/j.ijmachtools.2004.02.001.
[34] E.O. Ezugwu, D.A. Fadare, J. Bonney, R.B. Da Silva, and W.F. Sales. Modeling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. International Journal of Machine Tools and Manufacturing, 45(12-13):1375–1385, 2005. doi: 10.1016/j.ijmachtools.2005.02.004.
[35] P. Asokan, N. Baskar, K. Babu, G. Prabhakaran, and R. Saravanan. Optimization of surface grinding operation using particle swarm optimization technique. Journal of Manufacturing Science and Engineering, 127(4):885–892, 2015. doi: 10.1115/1.2037085.
[36] R.Q. Sardinas, M.R. Santana, and E.A. Brindis. Genetic algorithm-based multio-bjective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence, 19(2):127–133, 2006. doi: 10.1016/j.engappai.2005.06.007.
[37] C. Jia and H. Zhu. An improved multiobjective particle swarm optimization based on culture algorithms. Algorithms, 10(2):46–56, 2017. doi: 10.3390/a10020046.
[38] C.A. Coello Coello, G.T. Pulido, and M.S. Lechuga. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3):256–279, 2004. doi: 10.1109/TEVC.2004.826067.
[39] C.R. Raquel and P.C. Naval. An effective use of crowding distance in multiobjective particle swarm optimization. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pages 257–264, Washington DC, USA, 2005. doi: 10.1145/1068009.1068047.
[40] G.T. Pulido and C.A. Coello Coello. Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 225-237, Seattle, USA, 2004. doi: 10.1007/978-3-540-24854-5_20.
[41] S. Mostaghim and J. Teich. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS'03), pages 26–33, Indianapolis, IN, USA, 26 April 2003. doi: 10.1109/SIS.2003.1202243.
[42] J. Branke and S. Mostaghim. About selecting the personal best in multi-objective particle swarm optimization. In Proceedings of the Parallel Problem Solving From Nature (PPSN Ix) International Conference, pages 523–532, Reykjavik, Iceland, 9–13 September 2006. doi: 10.1007/11844297_53.
[43] T.M. Chenthil Jegan and D. Ravindran. Electrochemical machining process parameter optimization using particle swarm optimization. Computational Intelligence, 33:1019–1037, 2017. doi: 10.1111/coin.12139.
[44] C.P. Mohanty, S.S. Mahapatra, and M.R. Singh. A particle swarm approach for multi-objective optimization of electrical discharge machining process. Journal of Intelligent Manufacturing, 27:1171–1190, 2016. doi: 10.1007/s10845-014-0942-3.
[45] U. Natarajan, V.M. Periasamy, and R. Saravanan. Application of particle swarm optimisation in artificial neural network for the prediction of tool life. The International Journal of Advanced Manufacturing Technology, 31:871–876, 2007. doi: 10.1007/s00170-005-0252-1.
[46] A.K. Gandhi, S.K. Kumar, M.K. Pandey, and M.K. Tiwari. EMPSO-based optimization for inter-temporal multi-product revenue management under salvage consideration. Applied Soft Computing, 11(1):468–476, 2011. doi: 10.1016/j.asoc.2009.12.006.
[47] J.J. Yang, J.Z. Zhou, W. Wu, and F. Liu. Application of improved particle swarm optimization in economic dispatching. Power System Technology, 29(2):1–4, 2005.
[48] T. Sibalija, S. Pentronic, and D. Milovanovic. Experimental optimization of nimonic 263 laser cutting using a particle swarm approach. Metals, 9:1147, 2019. doi: 10.3390/met9111147.
[49] X. Luan, H. Younse, H. Hong, G.P. Peterson. Improving mechanical properties of PVA based nano composite using aligned single-wall carbon nanotubes. Materials Research Express, 6 (10):1050a6, 2019. doi: 10.1088/2053-1591/ab4058.
[50] H. Younes, R.A. Al-Rub, M.M. Rahman, A. Dalaq, A.A. Ghaferi, and T. Shah. Processing and property investigation of high-density carbon nanostructured papers with superior conductive and mechanical properties. Diamond and Related Materials, 68:109–117, 2016. doi: 10.1016/j.diamond.2016.06.016.
[51] G. Christensen, H. Younes, H. Hong, and G.P. Peterson. Alignment of carbon nanotubes comprising magnetically sensitive metal oxides by nonionic chemical surfactants. Journal of Nanofluids, 2(1): 25–28, 2013. doi: 10.1166/jon.2013.1031.
[52] H. Younes, M.M. Rahman, A.A. Ghaferi, and I. Saadat. Effect of saline solution on the electrical response of single wall carbon nanotubes-epoxy nanocomposites. Journal of Nanomaterials, 2017: 6843403, 2017 doi: 10.1155/2017/6843403.
[53] H. Younes, G. Christensen, L. Groven, H. Hong, and P. Smith. Three dimensional (3D) percolation network structure: Key to form stable carbon nano grease. Journal of Applied Research and Technology, 14(6):375–382, 2016. doi: 10.1016/j.jart.2016.09.002.
[54] J. Jerald, P. Asokan, G. Prabaharan, and R. Saravanan. Scheduling optimization of flexible manufacturing systems using particle swarm optimization algorithm. The International Journal of Advanced Manufacturig Technology, 25:964–971, 2005. doi: 10.1007/s00170-003-1933-2.
[55] M. Ghasemi, E. Akbari, A. Rahimnejad, S.E. Razavi, S. Ghavidel, and L. Li. Phasor particle swarm optimization: a simple and efficient variant of PSO. Soft Computing, 23:9701–9718, 2019. doi: 10.1007/s00500-018-3536-8.
[56] M.R. Singh and S.S. Mahapatra. A swarm optimization approach for flexible flow shop scheduling with multiprocessor tasks. The International Journal of Advanced Manufacturing Technology, 62(1–4), 267–277, 2012. doi: 10.1007/s00170-011-3807-3.
[57] H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Transactions on Power Systems, 15(4):1232–1239, 2000. doi: 10.1109/59.898095.
[58] F. Belmecheri, C. Prins, F. Yalaoui, and L. Amodeo. Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of Intelligent Manufacturing, 24(4):775–789, 2013. doi: 10.1007/s10845-012-0627-8.
[59] M. Bachlaus, M.K. Pandey, C. Mahajan, R. Shankar, and M.K. Tiwari. Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing, 19(6):747–761, 2008. doi: 10.1007/s10845-008-0125-1.
[60] B. Brandstatter and U. Baumgartner. Particle swarm optimization – mass-spring system analogon. IEEE Transactions on Magnetics, 38(2):997–1000, 2002. doi: 10.1109/20.996256.
[61] B. Kim and S. Son. A probability matrix-based particle swarm optimization for the capacitated vehicle routing problem. Journal of Intelligent Manufacturing, 23(4):1119–1126, 2012. doi: 10.1007/s10845-010-0455-7.
[62] C.H. Wu, D.Z. Wang, A. Ip, D.W. Wang, C.Y. Chan, and H.F. Wan. A particle swarm optimization approach for components placement inspection on printed circuit boards. Journal of Intelligent Manufacturing, 20(5):535–549, 2009. doi: 10.1007/s10845-008-0140-2.
[63] S.B. Raja and N. Baskar. Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Systems with Applications, 39(5):5982–5989, 2012. doi: 10.1016/j.eswa.2011.11.110.
[64] N. Yusup, A.M. Zain, and S.Z.M. Hashim. Overview of PSO for optimizing process parameters of machining. Procedia Engineering, 29:914–923, 2012. doi: 10.1016/j.proeng.2012.01.064.
[65] R.L. Malghan, K.M.C. Rao, A.K. Shettigar, S.S. Rao, and R.J. D'Souza. Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(9):2541–3553, 2017. doi: 10.1007/s40430-016-0675-7.
[66] A. Hadidi, A. Kaveh, B. Farahmand Azar, S. Talatahari, and C. Farahmandpour. An efficient optimization algorithm based on particle swarm and simulated annealing for space trusses. International Journal of Optimization in Civil Engineering, 3:377–395, 2011.
[67] T. Chaudhary, A.N. Siddiquee, A.K. Chanda, and Z.A. Khan. On micromachining with a focus on miniature gears by non conventional processes: a status report. Archive of Mechanical Engineering, 65(1):129–169, 2018. doi: 10.24425/119413.
[68] D. Kumar and K.K Singh. An experimental investigation of surface roughness in the drilling of MWCNT doped carbon/epoxy polymeric composite material. IOP Conference Series: Materials Science and Engineering, 149:012096, 2016. doi: 10.1088/1757-899X/149/1/012096.
[69] 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.
[70] N.S. Kumar, A. Shetty, Ashay Shetty, K. Ananth, and H. Shetty. Effect of spindle speed and feed rate on surface roughness of carbon steels in CNC turning. Procedia Engineering, 38:691– 697, 2012. doi: 10.1016/j.proeng.2012.06.087.
[71] E.T. Akinlabi, I. Mathoho, M.P. Mubiayi, C. Mbohwa, and M.E. Makhatha. Effect of process parameters on surface roughness during dry and flood milling of Ti-6A-l4V. In: 2018 IEEE 9th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), pages 144–147, Cape Town, South Africa, 10-13 February 2018. doi: 10.1109/ICMIMT.2018.8340438.
[72] J.P. Davim, L.R. Silva, A. Festas, and A.M. Abrão. Machinability study on precision turning of PA66 polyamide with and without glass fiber reinforcing. Materials & Design, 30(2):228– 234, 2009. doi: 10.1016/j.matdes.2008.05.003.
[73] J. Cha, J. Kim, S. Ryu, and S.H. Hong. Comparison to mechanical properties of epoxy nanocomposites reinforced by functionalized carbon nanotubes and graphene nanoplatelets. Composites Part B: Engineering, 162:283–288, 2018. doi: 10.1016/j.compositesb.2018.11.011.
[74] R.V. Rao, P.J. Pawar, and R. Shankar. Multi-objective optimization of electrochemical machining process parameters using a particle swarm optimization algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(8):949–958, 2008. doi: 10.1243/09544054JEM1158.
[75] R. Farshbaf Zinati, M.R. Razfar, and H. Nazockdast. Surface integrity investigation for milling PA6/ MWCNT. Materials and Manufacturing Processes, 30(8):1035–1041, 2014. doi: 10.1080/10426914.2014.961473.
[76] I. Shyha, G.Y. Fu, D.H. Huo, B. Le, F. Inam, M.S. Saharudin, and J.C. Wei. Micro-machining of nano-polymer composites reinforced with graphene and nano-clay fillers. Key Engineering Materials, 786:197–205, 2018. doi: 10.4028/www.scientific.net/kem.786.197.
[77] G. Fu, D. Huo, I. Shyha, K. Pancholi, and M.S. Saharudin. Experimental investigation on micro milling of polyester/halloysite nano-clay nanocomposites. Nanomaterials, 9(7):917, 2019. doi: 10.3390/nano9070917.
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Authors and Affiliations

Prakhar Kumar Kharwar
1
Rajesh Kumar Verma
1
Nirmal Kumar Mandal
2
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.

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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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