2022
12
2
0
3891
Chaos Analysis along with Comparison of Chaos Control Algorithms in Active Suspension System
2
2
In this paper, analysis and control of the chaotic vibrations in bounce dynamic of vehicle have been studied according to the comparison of controller based on the nonlinear control and chaos controller on the basis of the chaotic system properties. After modeling the vehicle dynamic, the chaotic behavior of the uncontrolled system was determined using combination of the numerical analysis including bifurcation diagrams and max Lyapunov exponent. The system parameters values were then identified in the quasi-periodic and chaotic behavior system. In order to eliminate the chaotic vibrations, the control signal was first developed using a nonlinear fast-terminal sliding mode control algorithm that its control gains are estimated online by fuzzy logic which was designed for vehicle vertical dynamics. Then the delayed feedback control was designed based on the development of Pyragas algorithm to control the system based on the properties of the chaotic system and generation of a small control signal. Comparison of the feedback system depicts priority of the Fuzzy-Pyragas controller in less energy consumption and better behavior.
3827
3837
2022/03/6
1400/12/15
2022/06/11
1401/3/21
Yavar
Nourollahi Golouje
Yavar
Nourollahi Golouje
Ph.D. candidate, Department of Mechanical Engineering, Qazvin Islamic Azad University, Qazvin Branch, Faculty of Industrial and Mechanical Engineering, Qazvin
Iran
y.noorallahi@advmco.ir
Seyyed Mahdi
Abtahi
Seyyed Mahdi
Abtahi
Assistant professor, Department of Mechanical Engineering, Qazvin Islamic Azad University, Qazvin Branch, Faculty of Industrial and Mechanical Engineering, Qazvin
Iran
m.abtahi61@gmail.com
Majid
Majidi
Majid
Majidi
Assistant professor, Department of Mechanical Engineering, Qazvin Islamic Azad University, Qazvin Branch, Faculty of Industrial and Mechanical Engineering, Qazvin
Iran
m_majidi@qiau.ac.ir
Bifurcation diagram
Lyapunov exponent
Chaos control
fast terminal sliding mode
Pyragas algorithm
Fuzzy inference
[[1] Pyragas, K., "Continuous Control of Chaos by Self-Controlling Feedback." Physics letters A, Vol. 170, pp. 421-428, (1992)##[2] Pyragas, K. and Tamas, E.A. "[2]Experimental Control of Chaos by Delayed Self-Controlling Feedback", Phys. Lett. A, Vol. 180, pp. 99–102 (1993) ##[3] Salarieh, H., and Alasty, A., "Chaos Control in Uncertain Dynamical Systems Using Nonlinear Delayed Feedback" Chaos, Solitons & Fractals, Vol. 41, pp. 67-71, (2009)##[4] Q. Zhu, M. Ishitobi, Chaos and bifurcations in a nonlinear vehicle model, Journal of Sound and Vibration, 275(3-5) (2004) 1136-1146.##[5] Abtahi S.M., "Chaotic Study and Chaos Control in a Half-Vehicle Model with Semi-Active Suspension Using Discrete Optimal Ott–Grebogi–Yorke Method", Journal of Multi-body Dynamics, Vol. 231, pp. 148–155 (2017)##[6] Abtahi, S.M.2019. Suppression of chaotic vibrations in suspension system of vehicle dynamics using chattering-free optimal sliding mode control, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41(5) 210.##[7] Wolf, A., Swift, J. B., Swinney, H. L. and Vastano, J. A.1985. Determining lyapunov exponents from a time series, Physica D, Vol 16, pp. 285-317. ##[8] S. Laghrouche, F. Plestan, A. Glumineau, Higher order sliding mode control based on integral sliding mode, Automatica, 43(3) (2007) 531-537.##[9] H. Li, X. Liao, C. Li, C. Li, Chaos control and synchronization via a novel chatter free sliding mode control strategy, Neurocomputing, 74(17) (2011) 3212-3222.##[10] H. Wang, Z.-Z. Han, Q.-Y. Xie, W. Zhang, Finite-time chaos control via nonsingular terminal sliding mode control, Communications in Nonlinear Science and Numerical Simulation, 14(6) (2009) 2728-2733.##[11] S. Dadras, H.R. Momeni, V.J. Majd, Sliding mode control for uncertain new chaotic dynamical system, Chaos, Solitons & Fractals, 41(4) (2009) 1857-1862.##[12] M.R. Faieghi, H. Delavari, D. Baleanu, Control of an uncertain fractional-order Liu system via fuzzy fractional-order sliding mode control, Journal of Vibration and Control, 18(9) (2012) 1366-1374.##[13] Y. Hong, G. Yang, D. Cheng, S. Spurgeon, A new approach to terminal sliding mode control design, Asian Journal of Control, 7(2) (2005) 177-181.## ##]
Machine Vision–Based Measurement Approach for Engine Accessory Belt Transverse Vibration Based on Deep Learning Method
2
2
In this paper, to address the problem of using displacement sensors in measuring the transverse vibration of engine accessory belt, a novel non-contact method based on machine vision and Mask-RCNN model is proposed. Mask-RCNN model was trained using the videos captured by a high speed camera. The results showed that RCNN model had an accuracy of 93% in detection of the accessory belt during the test. Afterward, the belt curve was obtained by a polynomial regression to obtain its performance parameters. The results showed that normal vibration of the center of the belt was in the range of 2 to 3 mm, but the maximum vibration was 8.7 mm and happened in the engine speed of 4200 rpm. Also, vibration frequency of the belt was obtained 124 Hz. Moreover, the minimum belt oscillation occurred at the beginning point of the belt on the TVD pulley, whereas the maximum oscillation occurred at a point close to the center of the belt at a distance of 16 mm from it. The results show that the proposed method can effectively be used for determination of the transvers vibration of the engine accessory belts, because despite the precise measurement of the belt vibration at any point, can provide the instantaneous position curve of all belt points and the equation of the belt curve at any moment. Useful information such as the belt point having the maximum vibration, belt slope, vibration frequency and scatter band of the belt vibration can be obtained as well.
3838
3846
2022/03/62022/05/16
1401/2/26
2022/06/112022/06/13
1401/3/23
Ashkan
Moosavian
Ashkan
Moosavian
Department of Agricultural Engineering, Technical and Vocational University (TVU), Tehran, Iran
Iran
a_moosavian@tvu.ac.ir
Alireza
Hosseini
Alireza
Hosseini
School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
Iran
alireza_hosseini96@elec.iust.ac.ir
Seyed Mohammad
Jafari
Seyed Mohammad
Jafari
Faculty of Mechanical & Energy Engineering, Shahid Beheshti University, A. C., Tehran, Iran
Iran
m_jafari@sbu.ac.ir
Iman
Chitsaz
Iman
Chitsaz
Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
Iran
i.chitsaz@iut.ac.ir
Shahriar
Baradaran Shokouhi
Shahriar
Baradaran Shokouhi
School of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
Iran
bshokouhi@iust.ac.ir
IC engine
Accessory belt
Mask-RCNN
Semantic segmentation
Machine vision
[[1] S. Jafari, M. Kazemi, M. Marzban, S. Moosavian, Health monitoring and performance investigation of accessory belt in an internal combustion engine during critical speeds, The Journal of Engine Research, Vol. 25, (2012), pp. 3-12.##[2] A. Fujii, S. Yonemoto, K. Miyazaki, S. Furumata, K. Okuda, H. Miyazawa, Analysis of the accessory belt lateral vibration in automotive engines, JSAE review, Vol. 23, (2002), pp. 41-47.##[3] P. Castellini, E. Cupido, N. Paone, E.P. Tomasini, Tracking laser doppler vibrometer for linear motion: application to a timing belt, Fourth International Conference on Vibration Measurements by Laser Techniques: Advances and Applications, International Society for Optics and Photonics, (2000), pp. 194-200.##[4] A. Agnani, M. Martarelli, E. Tomasini, V-belt transverse vibration measurement by means of laser Doppler vibrometry, Eighth International Conference on Vibration Measurements by Laser Techniques: Advances and Applications, International Society for Optics and Photonics, (2008), pp. 709819.##[5] H. Chaurasiya, Recent trends of measurement and development of vibration sensors, arXiv preprint arXiv: 1209.5333, (2012).##[6] Y. Hu, S. Zhang, Y. Yan, L. Wang, X. Qian, L. Yang, A smart electrostatic sensor for online condition monitoring of power transmission belts, IEEE Transactions on Industrial Electronics, Vol. 64, (2017), pp. 7313-7322.##[7] M. Ucar, R. Ergun, A. Cengiz, A novel failure diagnosis system design for automotive timing belts, Experimental Techniques, Vol. 38, (2014), pp. 48-53.##[8] Y. Hu, Y. Yan, L. Wang, X. Qian, Non-contact vibration monitoring of power transmission belts through electrostatic sensing, IEEE Sensors Journal, Vol. 16, (2016), pp. 3541-3550.##[9] M. Khazaee, A. Banakar, B. Ghobadian, M.A. Mirsalim, S. Minaei, Remaining useful life (RUL) prediction of internal combustion engine timing belt based on vibration signals and artificial neural network, Neural Computing and Applications, Vol. 33, (2021), pp. 7785–7801.##[10] N. Paul, C. Chung, Application of HDR algorithms to solve direct sunlight problems when autonomous vehicles using machine vision systems are driving into sun, Computers in Industry, Vol. 98, (2018), pp. 192-196.##[11] A.A. Borja, R.M.C. Amongo, D.C. Suministrado, J.P. Pabico, A machine vision assisted mechatronic seed meter for precision planting of corn, 3rd International Conference on Control and Robotics Engineering (ICCRE), IEEE, (2018), pp. 183-187.##[12] D. Bini, D. Pamela, S. Prince, Machine vision and machine learning for intelligent agrobots: A review, 5th International Conference on Devices, Circuits and Systems (ICDCS), IEEE, (2020), pp. 12-16.##[13] A. Martín, R. Lara-Cabrera, F. Fuentes-Hurtado, V. Naranjo, D. Camacho, EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation, Journal of Parallel and Distributed Computing, Vol. 117, (2018), pp. 180-191.##[14] H. Yaşar, G. Çağıl, O. Torkul, M. Şişci, Cylinder Pressure Prediction of An HCCI Engine Using Deep Learning, Chinese Journal of Mechanical Engineering, Vol. 34, (2021), pp. 1-8.##[15] B. Ramamoorthy, An accurate and robust method for the honing angle evaluation of cylinder liner surface using machine vision, The International Journal of Advanced Manufacturing Technology, Vol. 55, (2011), pp. 611-621.##[16] K.D. Lawrence, R. Shanmugamani, B. Ramamoorthy, Evaluation of image based Abbott–Firestone curve parameters using machine vision for the characterization of cylinder liner surface topography, Measurement, Vol. 55, (2014), pp. 318-334.##[17] F. Xuyun, L. Hui, S. Zhong, L. Lin, Aircraft engine fault detection based on grouped convolutional denoising autoencoders, Chinese Journal of Aeronautics, Vol. 32, (2019), pp. 296-307.##[18] S. Capela, R. Silva, S.R. Khanal, A.T. Campaniço, J. Barroso, V. Filipe, Engine Labels Detection for Vehicle Quality Verification in the Assembly Line: A Machine Vision Approach, Portuguese Conference on Automatic Control, Springer, (2020), pp. 740-751.##[19] J. Rochussen, P. Kirchen, Robust image segmentation for feature extraction from internal combustion engine in-cylinder images, Measurement Science and Technology, Vol. 32 (2020), pp. 015302.##[20] PSA flapping test procedure, No. DTI/DPMO/AMMT/MVAM, (2002).##[21] A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis, Deep learning for computer vision: A brief review, Computational intelligence and neuroscience, (2018).##[22] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature, Vol. 521, (2015), pp. 436-444.##[23] R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, (2014), pp. 580-587.##[24] R. Girshick, Fast r-cnn, Proceedings of the IEEE international conference on computer vision, (2015), pp. 1440-1448.##[25] S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, Vol. 28, (2015), pp. 91-99.##[26] C. Nuzzi, S. Pasinetti, M. Lancini, F. Docchio, G. Sansoni, Deep learning based machine vision: first steps towards a hand gesture recognition set up for collaborative robots, Workshop on Metrology for Industry 4.0 and IoT, IEEE, (2018), pp. 28-33.##[27] K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask r-cnn, Proceedings of the IEEE international conference on computer vision, (2017), pp. 2961-2969.##[28] E. Ostertagová, Modelling using polynomial regression, Procedia Engineering, Vol. 48, (2012), pp. 500-506.##[29] Y.W. Chang, C.J. Hsieh, K.W. Chang, M. Ringgaard, C.J. Lin, Training and testing low-degree polynomial data mappings via linear SVM, Journal of Machine Learning Research, Vol. 11, (2010), pp. 1471−1490.## ##]
Adaptive size-independent control of UD and BD vehicle convoys with partial measurement based on constant distance plan
2
2
The adaptive size-independent consensus problem of uni-directional (UD) and bi-directional (BD) decentralized large-scale vehicle convoys with uncertain dynamics has been investigated in this research work. The constant distance plan (CDP) is employed to adjust the distances between successive vehicles. We assume that only relative displacement information between adjacent vehicles is accessible (partial measurement) and other information such as relative velocity and acceleration are not provided. The stability of the convoy can be performed by the analysis of each couple of consecutive vehicles. The main objective is to design an adaptive size-independent control protocol maintaining internal and string stability based on CDP with only partial measurement. Appropriate adaptive rules are derived to estimate the uncertain dynamics by utilizing only relative displacement. It will be proved that the presented adaptive protocol assures both internal stability (asymptotic stability of closed-loop convoy) and string stability (tracking error attenuation) of large-scale decentralized UD and BD convoys under the CDP. Simulations demonstrate the efficiency of the presented control framework.
3847
3859
2022/03/62022/05/162022/03/1
1400/12/10
2022/06/112022/06/132022/06/7
1401/3/17
Hossein
Chehardoli
Hossein
Chehardoli
Department of Mechanical Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran.
Iran
h.chehardoli@abru.ac.ir
Uni-directional topology (UD)
Bi-directional topology (BD)
Large-scale vehicle convoy
Adaptive consensus
Robust performance
Partial measurement.
[[1] Y. Toor, P. Muhlethaler, A. Laouiti, and A. De La Fortelle, "Vehicle ad hoc networks: Applications and related technical issues," IEEE communications surveys & tutorials, vol. 10, no. 3, pp. 74-88, 2008.##[2] S. E. Shladover, "Cooperative (rather than autonomous) vehicle-highway automation systems," IEEE Intelligent Transportation Systems Magazine, vol. 1, no. 1, pp. 10-19, 2009.##[3] J. Zhang, T. Feng, F. Yan, S. Qiao, and X. Wang, "Analysis and design on intervehicle distance control of autonomous vehicle platoons," ISA Transactions, vol. 100, pp. 446-453, 2020.##[4] L. D. Baskar, B. De Schutter, and H. Hellendoorn, "Optimal routing for automated highway systems," Transportation Research Part C: Emerging Technologies, vol. 30, pp. 1-22, 2013.##[5] P. V. Manivannan and P. Ramakanth, "Vision Based Intelligent Vehicle Steering Control Using Single Camera for Automated Highway System," Procedia Computer Science, vol. 133, pp. 839-846, 2018.##[6] H. Mokhtar, M. Krishnamoorthy, N. R. Dayama, and P. N. R. Kumar, "New approaches for solving the convoy movement problem," Transportation Research Part E: Logistics and Transportation Review, vol. 133, pp. 1-18, 2020.##[7] P. Liu, A. Kurt, and U. Ozguner, "Synthesis of a behavior-guided controller for lead vehicles in automated vehicle convoys," Mechatronics, vol. 50, pp. 366-376, 2018.##[8] M. Shang and R. E. Stern, "Impacts of commercially available adaptive cruise control vehicles on highway stability and throughput," Transportation Research Part C: Emerging Technologies, vol. 122, pp. 1-22, 2021.##[9] H. Liu and R. Jiang, "Improving comfort level in traffic flow of CACC vehicles at lane drop on two-lane highways," Physica A: Statistical Mechanics and its Applications, vol. 575, pp. 1-9, 2021.##[10] Q. Xu and R. Sengupta, "Simulation, analysis, and comparison of ACC and CACC in highway merging control," in IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No. 03TH8683), 2003: IEEE, pp. 237-242. ##[11] F. Gao, S. E. Li, Y. Zheng, and D. Kum, "Robust control of heterogeneous vehicular platoon with uncertain dynamics and communication delay," IET Intelligent Transport Systems, vol. 10, no. 7, pp. 503-513, 2016.##[12] G. J. Naus, R. P. Vugts, J. Ploeg, M. J. van De Molengraft, and M. Steinbuch, "String-stable CACC design and experimental validation: A frequency-domain approach," IEEE Transactions on vehicular technology, vol. 59, no. 9, pp. 4268-4279, 2010.##[13] R. Rajamani, Vehicle dynamics and control. Springer Science & Business Media, 2011.##[14] H. Chehardoli, "Multi look‐ahead consensus of vehicular networks in the presence of random data missing," Journal of Vibration and Control, vol. 27, no. 6, pp. 717-728, 2021.##[15] P. Wijnbergen and B. Besselink, "Existence of decentralized controllers for vehicle platoons: On the role of spacing policies and available measurements," Systems & Control Letters, vol. 145, pp. 1-9, 2020.##[16] Y. Bian, Y. Zheng, W. Ren, S. E. Li, J. Wang, and K. Li, "Reducing time headway for platooning of connected vehicles via V2V communication," Transportation Research Part C: Emerging Technologies, vol. 102, pp. 87-105, 2019.##[17] S. Feng, Y. Zhang, S. E. Li, Z. Cao, H. X. Liu, and L. Li, "String stability for vehicular platoon control: Definitions and analysis methods," Annual Reviews in Control, vol. 47, pp. 81-97, 2019.##[18] D. Swaroop and J. K. Hedrick, "Constant spacing strategies for platooning in automated highway systems," J. Dyn. Sys., Meas., Control., vol. 121, no. 3, pp. 462-470, 1999.##[19] P. Seiler, A. Pant, and K. Hedrick, "Disturbance propagation in vehicle strings," IEEE Transactions on Automatic Control, vol. 49, no. 10, pp. 1835-1842, 2004.##[20] F. Gao, X. Hu, S. E. Li, K. Li, and Q. Sun, "Distributed adaptive sliding mode control of vehicular platoon with uncertain interaction topology," IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6352-6361, 2018.##[21] J.-W. Kwon and D. Chwa, "Adaptive bidirectional platoon control using a coupled sliding mode control method," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2040-2048, 2014.##[22] H. Chehardoli and A. Ghasemi, "Adaptive centralized/decentralized control and identification of 1-D heterogeneous vehicular platoons based on constant time headway policy," IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 10, pp. 3376-3386, 2018.##[23] X. Guo, J. Wang, F. Liao, and R. S. H. Teo, "Distributed adaptive integrated-sliding-mode controller synthesis for string stability of vehicle platoons," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 9, pp. 2419-2429, 2016.##[24] G. Guo and D. Li, "Adaptive Sliding Mode Control of Vehicular Platoons With Prescribed Tracking Performance," IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 7511-7520, 2019.##[25] F. Ma et al., "Distributed Control of Cooperative Vehicular Platoon With Nonideal Communication Condition," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8207-8220, 2020.##[26] S. Baldi, D. Liu, V. Jain, and W. Yu, "Establishing platoons of bidirectional cooperative vehicles with engine limits and uncertain dynamics," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 2679-2691, 2020.##[27] G. Guo, J. Kang, H. Lei, and D. Li, "Finite-Time Stabilization of a Collection of Connected Vehicles Subject to Communication Interruptions," IEEE Transactions on Intelligent Transportation Systems, pp. 1-9, 2021.##[28] W. Yue and G. Guo, "Guaranteed cost adaptive control of nonlinear platoons with actuator delay," Journal of dynamic systems, measurement, and control, vol. 134, no. 5, pp. 136-144, 2012.##[29] Y. Zheng, S. E. Li, J. Wang, D. Cao, and K. Li, "Stability and scalability of homogeneous vehicular platoon: Study on the influence of information flow topologies," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 1, pp. 14-26, 2016.##[30] Y. Zheng, S. E. Li, K. Li, and L.-Y. Wang, "Stability margin improvement of vehicular platoon considering undirected topology and asymmetric control," IEEE Transactions on Control Systems Technology, vol. 24, no. 4, pp. 1253-1265, 2016.##[31] M. Di Bernardo, A. Salvi, and S. Santini, "Distributed consensus strategy for platooning of vehicles in the presence of time-varying heterogeneous communication delays," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 102-112, 2014.##[32] A. Petrillo, A. Salvi, S. Santini, and A. S. Valente, "Adaptive multi-agents synchronization for collaborative driving of autonomous vehicles with multiple communication delays," Transportation research part C: emerging technologies, vol. 86, pp. 372-392, 2018.##[33] A. Ghasemi, R. Kazemi, and S. Azadi, "Stability analysis of longitudinal control of a platoon of vehicles by considering lags subject to communication delay," The International Journal of Innovative Computing, Information and Control, vol. 10, no. 5, pp. 1625-1641, 2014.## ##]
Investigating the construction of three-way connection tubes for hydraulic systems in vehicles by dynamic-impact loading in hydroforming
2
2
Background: Hydroforming is employed in the manufacture of hollow monolithic products to reduce the number of joints. This method can reduce the weight and enhance the quality of fluid transfer parts in a vehicle’s hydraulic system. Hydroforming is a process in which parts are formed into the shape of a mold using fluid pressure. An important issue in this process is adopting an optimal loading path. Methods: In the present research, a drop hammer was used to implement the dynamic loading path in the tests. Accordingly, a single energy source was used simultaneously to provide axial feeding and internal pressure. To this end, designing a mold suitable for the dynamic loading path was necessary. Results: This numerical study investigates tubes’ deformation based on the applied impact and the amount of fluid in the mold. Moreover, axial feeding was provided with the help of different punches on the sides of the tube. Hence, the kinetic energy, amount of fluid, sealing, lubrication, and the material and thickness of the tube must be proportional for the correct forming of the tube. From the smoothed-particle hydrodynamics perspective, it is a meshless method based on interpolation that uses a particle system to examine the system state and predict fields such as displacement, stress, and pressure. Conclusions: One of the main observations of this research is that selecting side punches with a smaller central hole radius is proportional to the kinetic energy and the amount of fluid. that is effective in achieving the optimal loading path.
3860
3872
2022/03/62022/05/162022/03/12022/05/20
1401/2/30
2022/06/112022/06/132022/06/72022/06/19
1401/3/29
Arman
Mohseni
Arman
Mohseni
Department of Mechanical Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
Iran
armanmohseni15@yahoo.com
Javad
Rezapour
Javad
Rezapour
Department of Mechanical Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
Iran
javad.rs82@gmail.com
Sina
Gohari Rad
Sina
Gohari Rad
2Department of Mechanical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.
Iran
sina.g.rad@gmail.com
Reza
Rajabiehfard
Reza
Rajabiehfard
2Department of Mechanical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.
Iran
r.rajabiehfard@gmail.com
Hydroforming Internal pressure Axial feeding SPH Simulation model Dynamic loading path
[[1] A. Günaydın, M. Halkacı, F. Ateş, H. Selçuk, Experimental Research of the Usability on Double Acting Intensifiers in Hydroforming, MATEC Web of Conferences, (January 2018).##[2] H. Faraji, Kh. Khalili, A. Ashrafi, The Use of Internal Mechanical Insert to Prevent Wrinkling Defects in T-joint Hydroforming Process, Modares Mechanical Engineering, Vol. 8, No. 19, (2019), pp. 1989-2000. (in Persian)##[3] C. Han, Q. Liu, H. Lu, G. L.Gao, W. C. Xie, S. J. Yuan, Thickness improvement in hydroforming of a variable diameter tubular component by using wrinkles and preforms, The International Journal of Advanced Manufacturing Technology, Vol. 99, No. 9-11, (2018), pp. 2993-3003.##[4] C. Churiaque, J. Amaya, F. Caamano, J. Martinez, J. Botana, Springback Estimation in the Hydroforming Process of UNS A92024 T3 Aluminum Alloy by FEM Simulations, Multidisciplinary Digital Publishing Institute, Vol. 8, No. 6, (2018).##[5] F. Ying, Zh. Ge, L. An, W. Lin, Loading path optimization of T tube in hydroforming process using response surface method, The International Journal of Advanced Manufacturing Technology, Vol. 101, No. 5-8, (201), pp. 1979-1995.##[6] G. Cai, Ch. Wu, Z. Gao, L. Lang, S. Alexandrov, Investigation on the effect of pressure rate on formability of aluminum alloy during warm/hot sheet hydroforming, American Institute of Physics, Vol. 8, No. 9, (2018).##[7] H. Dardaei, A. Tekkaya, F. Legat, A. Henke, Investigation of the effects of process and geometrical parameters on formability in tube hydroforming using a modular hydroforming tool, Proceedings of the 21st International ESAFORM Conference on Material Forming, (2018).##[8] X. Guo, Zh. Liu, H. Wang, J. Tao, Hydroforming simulation and experiment of clad T shapes, International Journal of Advanced Manufacturing Technology, Vol. 83, No. 1-4, (2016), pp. 381-387.##[9] M. Hermes, A. keskin, Ch. Berlinger, New Device and Technology for High Speed Hydroforming, 5th International Conference on New Forming Technology, (2018).##[10] J. Ma, L. Fa, Y. He, Dynamic Frictional Characteristics of TP2 Copper Tubes during Hydroforming under Different Loading and Fluid Velocities, Journal of Materials Engineering and Performance, Vol. 28, (2019), pp. 3661-3672.##[11] P. Reddy, B. Reddy, P. Ramulu, An investigation on tube hydroforming process considering the effect of frictional coefficient and corner radius, Advances in Materials and Processing Technologies, Vol. 6, No. 1, (2020), pp. 84-103.##[12] T. Hama, M. Asakawa, H. Fukiharu, A. Makinuchi, Simulation of Hammering Hydroforming by Static Explicit FEM, Iron and Steel Institute of Japan, Vol. 44, No. 1, (2004), pp. 123-128.##[13] J. Yang, B. Jeon, S. lk oh, Design sensitivity analysis and optimization of the hydrofoming process, Journal of Materials Processing Technology, Vol. 113, No. 1-3, (2001), pp. 666-672.##[14] H. Ahmadi, M. Zohoor, Investigation of the effective parameters in tube hydroforming process by using experimental and finite element methodfor manufacturing of tee joint products, The International Journal of Advanced Manufacturing Technology, Vol. 93, No. 1-4, (2017), pp. 393-495.##[15] M. Alitavoli, H. Babaei, A. Mohseni, R. Rajabiehfard, Experimental and numerical forming of T shaped metallic tubes subjected to hydrodynamic loading, Modares Mechanical Engineering, Vol. 16, No. 9, (2016), pp. 223-232. (in Persian)##[16] M.B. Liu, G. Liu, Smoothed particle hydrodynamics (SPH): an overview and recent developments. Archives of Computational Methods in Engineering, Vol. 1, No. 19, (2010), pp. 25-76.## ##]
Forming limit curves and mechanical properties for AA6061 sheet after solution treatment and during ageing
2
2
This paper aims to examine the influences of heat treatment on forming limit diagrams and mechanical properties of aluminum alloy AA6061 sheets with thicknesses of 1.5 mm. The uniaxial tensile and the micro-hardness tests are employed to specify the mechanical properties and their variations. The Nakazima test is performed to characterize the strain forming limits of this aluminum alloy. Comparison between the results of micro-hardness and forming limit diagrams indicates that by increasing the temperature up to the peaked ageing temperature, the strength of the alloy is increased, but the forming limits are decreased, and after the peaked aged in over the aged state, the strength is decreased and the forming limits are increased. The peaked-aging is touched in this specific alloy after 4 hours heat treatment at 180 oC.
3873
3880
2022/03/62022/05/162022/03/12022/05/202020/08/22
1399/6/1
2022/06/112022/06/132022/06/72022/06/192022/06/27
1401/4/6
Majid
Fallah Tafti
Majid
Fallah Tafti
School of Mechanical Engineering, Iran University of Science and Technology
Iran
fallah.t.majid@gmail.com
Ramin
Hashemi
Ramin
Hashemi
School of Mechanical Engineering, Iran University of Science and Technology
Iran
rhashemi@iust.ac.ir
Mohammad
Sedighi
Mohammad
Sedighi
School of Mechanical Engineering, Iran University of Science and Technology
Iran
sedighi@iust.ac.ir
Mechanical properties
Ageing
Forming limit curves
Solution
Sheet
[[1] L. P. Troeger and E. S. Jr, “Microstructural and mechanical characterization of a superplastic 6xxx aluminum alloy,” Mater. Sci. Eng. A, vol. 277, no. 1–2, pp. 102–113, 2000.##[2] S. Abis, a Boeuf, R. Caciuffo, P. Fiorini, M. Magnani, S. Melone, F. Rustichelli, and M. Stefanon, “Investigation of Mg2Si Precipitation in an Al-Mg-Si Alloy By Small-Angle Neutron-Scattering,” J. Nucl. Mater., vol. 135, no. 2–3, pp. 181–189, 1985.##[3] J. Buha, R. N. Lumley, a. G. Crosky, and K. Hono, “Secondary precipitation in an Al-Mg-Si-Cu alloy,” Acta Mater., vol. 55, no. 9, pp. 3015–3024, 2007.##[4] L. Zhen, W. D. Fei, S. B. Kang, and H. W. Kim, “Precipitation behaviour of Al-Mg-Si alloys with high silicon content,” J. Mater. Sci., vol. 32, no. 7, pp. 1895–1902, 1997.##[5] M. Murayama, K. Hono, M. Saga, and M. Kikuchi, “Atom probe studies on the early stages of precipitation in Al-Mg-Si alloys,” Mater. Sci. Eng. a-Structural Mater. Prop. Microstruct. Process., vol. 250, no. 1, pp. 127–132, 1998.##[6] C. D. Marioara, S. J. Andersen, J. Jansen, and H. W. Zandbergen, “The influence of temperature and storage time at RT on nucleation of the β" phase in a 6082 Al-Mg-Si alloy,” Acta Mater., vol. 51, no. 3, pp. 789–796, 2003.##[7] S. P. Keeler and W. A. Backofen, “Plastic instability and fracture in sheets stretched over rigid punches,” Asm Trans Q, vol. 56, no. 1, pp. 25–48, 1963.##[8] D. W. a Rees, “Factors influencing the FLD of automotive sheet metal,” J. Mater. Process. Technol., vol. 118, no. 1–3, pp. 1–8, 2001.##[9] H. Demir and S. Gündüz, “The effects of aging on machinability of 6061 aluminium alloy,” Mater. Des., vol. 30, pp. 1480–1483, 2009.##[10] F. Ozturk, A. Sisman, S. Toros, S. Kilic, and R. C. Picu, “Influence of aging treatment on mechanical properties of 6061 aluminum alloy,” Mater. Des., vol. 31, no. 2, pp. 972–975, 2010.##[11] C. F. Tan and M. R. Said, “Effect of hardness test on precipitation hardening aluminium alloy 6061-t6,” Chiang Mai J. Sci., vol. 36, no. 3, pp. 276–286, 2009.##[12] C. K. S. Moy, M. Weiss, J. Xia, G. Sha, S. P. Ringer, and G. Ranzi, “Influence of heat treatment on the microstructure , texture and formability of 2024 aluminium alloy,” Mater. Sci. Eng. A, vol. 552, pp. 48–60, 2012.##[13] A. S. M. Handbook, Volume 4. 1991.##[14] M. Hajian and A. Assempour, “Experimental and numerical determination of forming limit diagram for 1010 steel sheet: a crystal plasticity approach,” Int. J. Adv. Manuf. Technol., vol. 76, no. 9–12, pp. 1757–1767, 2014.##[15] H. Rahimi, M. Sedighi, and R. Hashemi, “Forming limit diagrams of fine-grained Al 5083 produced by equal channel angular rolling process,” Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl., 2016: 1464420716655560.##[16] A. K. Ghosh, “The Influence of Strain Hardening and Strain-Rate Sensitivity on Sheet Metal Forming,” J. Eng. Mater. Technol., vol. 99, no. 1, p. 264, 1977.##[17] K. W. Neale and E. Chater, “Limit strain predictions for strain-rate sensitive anisotropic sheets,” Int. J. Mech. Sci., vol. 22, no. 9, pp. 563–574, 1980.##[18] S.M. Mirfalah-Nasiri, A. Basti, R. Hashemi, “Forming limit curves analysis of aluminum alloy considering the through-thickness normal stress, anisotropic yield functions and strain rate,” Int. J. Mech. Sci., vol. 117, pp. 93-101, 2016.##[19] M. Habibi, R. Hashemi, E. Sadeghi, A. Fazaeli, A. Ghazanfari, and H. Lashini, “Enhancing the Mechanical Properties and Formability of Low Carbon Steel with Dual-Phase Microstructures,” J. Mater. Eng. Perform., vol. 25, no. 2, pp. 382–389, 2016.##[20] F. Ozturk, E. Esener, S. Toros, and C. R. Picu, “Effects of aging parameters on formability of 6061-O alloy,” Mater. Des., vol. 31, no. 10, pp. 4847–4852, 2010.##[21] M. K. Sharma, J. Mukhopadyay, "Evaluation of Forming limit Diagram of Aluminium Alloy 6061-T6 at Ambient Temperature," Light Metals 2015, pp309-314, Springer. ##[22] F. Djavanroodi, A. Derogar, "Experimental and numerical investigation of forming limit diagrams for Ti6Al4V titanium and Al6061-T6 aluminium alloys sheets," Mater. Des., vol. 31, no. 10, pp. 4866-4875, 2010. ##[23] D. Raja Satish, F. Feyissa, D. Ravi Kumar, "Cryorolling and warm forming of AA6061 aluminium alloy sheets," Mater. Manuf. Process., pp1-8, 2017. ##[24] V.K. Barnwal, A. Tewari, K. Narasimban, "Effect of plastic anisotropy on forming behaviour of AA-6061 aluminium alloy sheet," J. Strain Anal. Eng., vol. 51, no. 7, pp. 507-517, 2016. ##[25] J. Liu, M.J. Tan, A.E.W. Jarfors, Y. Aue-u-lan, S. Castagne, "Formability in AA5083 and AA6061 sheet alloy for light weight applications," Mater. Des., vol. 31, 566-570, 2010.## ##]
Decision Making of an Autonomous Vehicle in a Freeway Travelling
2
2
This paper introduces a trajectory planning algorithm for long-term freeway driving for autonomous vehicles including different modes of motion. In the autonomous driving in a freeway, different maneuvers are needed, including free flow, distance adaption, speed adaption, lane change and overtaking. This paper introduces an algorithm that provides all of these driving scenarios in the trajectory planning for an autonomous vehicle. All maneuvers are classified and proper formulation for each driving mode formulated. Then, an algorithm is introduced to show the procedure of decision making and switching between all driving modes. The relative distances and velocities of the other peripheral and front vehicle from autonomous vehicle are considered as the main factors for decision making during the travelling in the freeway. By the developed simulation programming, validity and effectiveness of the algorithm are verified, and pseudo code and flowchart for the simulation programming are introduced. Later in two simulation studies, different driving conditions are generated and results have been discussed and analyzed by detail.
3881
3891
2022/03/62022/05/162022/03/12022/05/202020/08/222021/08/20
1400/5/29
2022/06/112022/06/132022/06/72022/06/192022/06/272022/07/12
1401/4/21
Hashem
Ghariblu
Hashem
Ghariblu
University of Zanjan
Iran
ghariblu@znu.ac.ir
Autonomous Vehicle
Trajectory Planning
Long-term
Freeway
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(2013), pp. 4161–4166.##[25] Houenou A, Bonnifait P, Cherfaoui V, et al., “Vehicle Trajectory Prediction based on Motion Model and Maneuver Recognition. IEEE/RSJ International Conference on Intelligent Robots and Systems, , Tokyo Japan, (2013), pp. 4363-4369.##[26] Kanaris A, Kosmatopoulos, EB, Loannou PA, Strategies and spacing requirements for lane changing and merging in automated highway systems,” IEEE T Veh Technol., Vol. 50, No. 61, (2001), pp. 586–1581.##[27] Gazis, D., Herman, R., & Rothery, R. “Nonlinear follow the leader models of traffic flow”, Oper. Res., Vol. 9, (1961), pp. 545+.## ##]