2015
8
2
2
0
Design of Optimal PID, Fuzzy and New FuzzyPID Controller for CANSAT Carrier System Thrust Vector
2
2
In this paper, multiobjective optimization based on Genetic Algorithm is used to find the design variables of PID, fuzzy and new FuzzyPID controllers applying for a thrust vector control of CANSAT carrier system. Motion vector control is considered according to the dynamic governing equation of the system which is derived using Newton’s method and defined mission in delivering payload into the specific height and flight path angle. The cost functions of the system are position error from the set point and deviation of the vector angle of carrier system with carrier body, where these cost functions must be minimized simultaneously. Results demonstrate that this new FuzzyPID controller is superior to other controllers which are exerted in the thrust vector control of a CANSAT carrier system. This FuzzyPID is capable of doing the mission with decrease in settling time and rise time with respect to the convenient minimized objective function values.
1

1
9


A.
Kosari
Department of New Sciences and Technologies,
University of Tehran, Iran
Department of New Sciences and Technologies,
Unive
Iran
kosari_a@ut.ac.ir


H.
Jahanshahi
Department of New Sciences and Technologies,
University of Tehran, Iran
Department of New Sciences and Technologies,
Unive
Iran
hadi_jahanshahi@ut.ac.ir


A. A.
Razavi
Department of New Sciences and Technologies,
University of Tehran, Iran
Department of New Sciences and Technologies,
Unive
Iran
a.razavi68@ut.ac.ir
Multiobjective optimization
Genetic Algorithm
PIDcontroller
Fuzzy controller
FuzzyPID controller
CANSAT carrier system
[[1] Aydemir, M. E., Dursun, R. C., and Pehlevan, M., “Ground Station Design Procedures for CANSAT”, the 6th International Conference on Recent Advanced in Space Technologies (RAST), Istanbul, Turkey, June 2013, pp. 909912. ##[2] Soyer, S., “Small Space Can: CANSAT,” in 5th International Conference on Recent Advanced in Space Technologies (RAST), Istanbul, Turkey, June 2011, pp. 789793. ##[3] Çabuloğlu, C., Aykiş, H., Yapacak, R., Çalişkan, E., Ağirbuş, Ö., Abur, Ş., Soyer, S., Türkmen, H., Ay, S., Karataş, Y., Aydemir, M. E., and Ҫelebi, M., “Mission Analysis and Planning of a CANSAT”, The 5th International Conference on Recent Advanced in Space Technologies (RAST), Istanbul, Turkey, June 2011, pp. 794799. ##[4] Okninski, A., Marciniak, B., Bartkowiak, B., Kaniewski, D., Matyszewski, J., Kindracki, J., and Wolanski, P., “Development of the Polish Small Sounding Rocket Program”, Acta Astronautica, Vol. 108, 2015, pp. 4656. ##[5] Zadeh, L. A., “Fuzzy algorithms”, Information and Control, Vol. 12, 1968, pp. 94102. ##[6] Zadeh, L. A., “Outline of a new approach to the analysis of complex systems and decision processes”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 3, 1973, pp. 2844. ##[7] Nasser, H., KieferKamal, E. H., Hu, H., Belouettar, S., and Barkanov, E., “Active vibration damping of composite structures using a nonlinear fuzzy controller”, Composite Structures, Vol. 94, 2012, pp. 13851390. ##[8] LI, P., JIN, F. J., “Adaptive Fuzzy Control for Unknown Nonlinear Systems with Perturbed Deadzone Inputs”, Acta Automatica Sinica, Vol. 36, 2010, pp. 573579. ##[9] Lygouras, J. N., Botsaris, P. N., Vourvoulakis, J., and Kodogiannis, V., “Fuzzy logic controller implementation for a solar airconditioning system”, Applied Energy, Vol. 84, 2007, pp. 13051318. ##[10] Jee, S., Koren, Y., “Adaptive fuzzy logic controller for feed drives of a CNC machine tool”, Mechatronics, Vol. 14, 2004, pp. 299326. ##[11] Sinha, A. S. C., Lyshevski, S., “Fuzzy control with random delays using invariant cones and its application to control of energy processes in microelectromechanical motion devices”, Energy Conversion and Management, Vol. 46, 2005, pp. 1305–1318. ##[12] Zuperl, U., Cus, F., and Milfelner, M., “Fuzzy control strategy for an adaptive force control in endmilling”, Journal of Materials Processing Technology, Vol. 164165, 2005, pp. 1472–1478. ##[13] Mansour, S. E., Kember, G. C., Dubay, R., and Robertson, B., “Online optimization of fuzzyPID control of a thermal process”, ISA Transactions, Vol. 44, 2005, pp. 305314. ##[14] Duan, X. G., Li, H. X., and Deng, H., “Robustness of fuzzy PID controller due to its inherent saturation”, Journal of Process Control, Vol. 22, 2012, pp. 470476. ##[15] Oh, S. K., Jang, H. J., and Pedrycz, W., “Optimized fuzzy PD cascade controller: A comparative analysis and design”, Simulation Modelling Practice and Theory, Vol. 19, 2011, pp. 181195. ##[16] Karasakal, O., Guzelkaya, M., Eksin, I., Yesil, E., and Kumbasar, T., “Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration”, Engineering Applications of Artificial Intelligence, Vol. 26, 2013, pp. 184197. ##[17] Boubertakh, H., Tadjine, M., Glorennec, P. Y., and Labiod, S., “Tuning fuzzy PD and PI controllers using reinforcement learning”, ISA Transactions, Vol. 49, 2010, pp. 543551. ##[18] Nie, M., Tan, W. W., “Stable adaptive fuzzy PD plus PI controller for nonlinear uncertain systems”, Fuzzy Sets and Systems, Vol. 179, 2011, pp. 119. ##]
Developing a control strategy for AFM nano micro manipulation
2
2
Nowadays, with the growing use of AFM (Atomic Force Microscope) nanorobots in the fabrication of nanostructures, research in this area has been proliferated. The major limiting of manipulation process is the lack of realtime observation. Computer simulations have been widely applied to improve the feasibility of the process. The existing 2D strategies are incapable of presenting the feasibility of the process. Therefore, 3D simulations of effective forces during the manipulation process and the control mechanism of the process have been presented in this research, where the effective parameters are investigated. To evaluate the validity of the presented results, a FEM simulation is proposed. It is observed that the two sets of results (the analytical method and the finite element approach) have adequate correlation, while the discrepancy which is in an acceptable range, is due to the different solving technique of the finite element method. By applying the presented models, it is now possible to accurately predict the effective forces for fabricating the nanostructures.
1

11
16


H.
RaeisiFard
Faculty of Industrial and Mechanical Engineering,
Islamic Azad University, Qazvin Branch, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering,
Iran
raeisifard@qiau.ac.ir


A. K.
Hoshiar
Faculty of Industrial and Mechanical Engineering,
Islamic Azad University, Qazvin Branch, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering,
Iran
hoshiar@qiau.ac.ir
AFM Nano robot
Controlled Manipulation
Finite Element Method
Manipulation
[[1] Stroscio, J. A, Eigler, D. M., “Atomic and Molecular Manipulation with the Scanning Tunneling Microscope”, Science, Vol. 254, No. 5036, 1996, pp. 13221326. ##[2] Schaefer, D. M, Reifenberger, R., “Fabrication of twodimensional of Nanometer –size clusters with the atomic force microscope”, Applied Physics Letters, Vol. 66, No. 8, 1995, pp. 10121014. ##[3] Junno, T., Deppert, K., and Montelius, L., “Controlled Manipulation of Nanoparticles with an Atomic Force Microscope”, Applied Physics Letters, Vol. 66, No. 3627, 1995, pp. 36273629. ##[4] Sitti, M., Hashimoti, H., “Controlled Pushing of Nanoparticles: Modeling and Experiments”, IEEE/ASME Transaction On Mechatronics, Vol. 5, No. 2, 2000, pp. 199211. ##[5] Sitti, M., “Survey of Nanomanipulation Systems”, IEEE Nanotechnology Conference, Maui, 2001, pp. 75  80. ##[6] Tafazzoli, A., Sitti, M., “Dynamic behavior and simulation of Nanoparticle sliding during Nanoprobe based positioning”, ASME International Mechanical Engineering Congress, California, 2004, pp. 965972. ##[7] Korayem, M. H., Hoshiar, A. K, “Modeling and simulation of dynamic modes in the manipulation of nanorods, Micro & Nano letters”, Vol. 8, No. 6, 2013, pp. 284287. ##[8] Korayem, M. H., Hoshiar, A. K., “Dynamic 3D modeling and simulation of nanoparticles manipulation using an AFM nanorobot”, Robotica, Vol. 32, No. 4, 2014, pp. 625–641. ##[9] Li, G., Xi, N., Chen, H., Saeed, A., and Yu, M., “Assembly of nanostructure using AFM based nanomanipulation system”, International Conference on Robotics and Automation (ICRA), Shanghai, Vol. 1, 2011, pp. 428433. ##[10] Hou, J., Liu, L., Wang, Z., Wang, Z., Xi, N., Wang, Y., Wu, C., Dong, Z., and Yuan, S., “AFMBased robotic nanohand for stable manipulation at nanoscale”, Transaction on automation science and engineering, Vol. 10, No.2, 2013, pp. 285295. ##[11] Sri Muthu Mrinalini, R., Sriramshankar, R., and Jayanth, G. R., “Direct Measurement of ThreeDimensional Forces in Atomic Force Microscopy”, IEEE/ASME Transaction on mechatronics, Vol. PP, No. 99, 2014, pp. 46744679. ##[12] Falvo, M. R, Clary, G, Hesler, A., and Paulson, S., “Nanomanipulation experiments exploring friction and mechanical properties of carbon Nanotube”, Micros Microanal, Vol. 4, No. 5, 1999, pp. 504512. ##[13] Jiangbo, Z., Guangyong, L., and Xi, N., “Modeling and Control of Active End Effectors for the AFM Based Nano Robotic Manipulators”, International Conference on Robotics and Automation IEEE, Barcelona, 2005, pp. 163168. ##[14] Lianqing, L., Xi, N., Yilun, L., Jiangbo, Z., and Guangyong, L., “Realtime Position Error Detecting in Nanomanipulation Using Kalman Filter”, Proceedings of the 7th IEEE International Conference on Nanotechnology, Hong Kong, 2007, pp. 100105. ##[15] Lianqing, L., Peng, Y., Xiaojun, T., Yuechao, W., Zaili, D., and Xi, N., “Force Analysis of TopDown Forming CNT Electrical Connection Using Nanomanipulation Robot”, IEEE International Conference on Mechatronics and Automation, Luoyang, 2006, pp. 113117. ##[16] Wang, Zh., Liu, L., Hou, Ji., Wang, Zh., Yuan, Sh, and Dong, Z., “Virtual nanohand: A stable pushing strategy in AFM based sensorless nanomanipulation”, IEEE International Conference on Robotics and Biomimetics (ROBIO), Phuket, 2011, pp. 1409–1414. ##[17] Fang, Y., Zhang, Y, Qi, N., and Dong, Xi., “AMAFM System Analysis and Output Feedback Control Design With Sensor Saturation”, IEEE Transactions on Nanotechnology, Vol. 12, No. 2, 2013, pp. 190202. ##[18] Polyakov, B., Vlassov, S., Dorogin, L.M., Butikova, J., Antsov, M., Oras, S., Lõhmus, R., and Kink, I., “Manipulation of nanoparticles of different shapes inside a scanning electron microscope”, Beilstein Journal of Nanotechnology, Vol. 5, 2014, pp. 133–140. ##[19] Kumar, S., Das, M., Singh, R. P., Datir, S., Chauhan, D. S., and Jain, S., “Mathematical models for the oxidative functionalization of multiwalled carbon nanotubes”, Colloids and Surfaces A: Physicochemical and Engineering Aspects, Vol. 419, 2013, pp. 156165. ##[20] Sun, Z., Song, B., Xi, N., Yang, R., Hao, L., and Chen, L., “Scan range adaptive hysteresis/creep hybrid compensator for AFM based nanomanipulations”, American Control Conference, Portland 2014, pp. 1619–1624. ##[21] Zhao, J., Song, B., Xi, N., “Nonvector space stochastic control for nano robotic manipulations”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, 2014, pp. 852–857. ##[22] Amari, N., Folio, D., and Ferreira, A., “Robust Nanomanipulation Control based on Laser Beam Feedback”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, 2014, pp. 4674–4679. ##[23] Korayem, M. H., Hoshiar, A. K., and Kordi, F., “Dynamic Modeling and Simulation of Cylindrical Nanoparticles in Liquid Medium” International Journal of Advanced Manufacture Technology, 2014, Vol. 75, pp. 197208. ##[24] Korayem, A. H., Hoshiar, A. K., Ashtiani, N. N., and Korayem, M. H., “Using a Virtual Reality Environment to Simulate the Pushing of Cylindrical Nanoparticles” International Journal of Nanoscience Nanotechnology, 2014, Vol. 10, pp. 133144. ##[25] Korayem, A. H., Hoshiar, A. K., and Korayem, M. H., “Modeling and simulation of critical forces in the manipulation of cylindrical nanoparticles”, International Journal of Advanced Manufacture Technology, 2015, Vol. 79, pp. 15051517. ##]
Design and Control of a 3 DOF Hand Skeleton for Rehabilitation after Stroke
2
2
Stroke is one of the most common diseases among the elderly with high personal and societal costs. In recent years, robotic rehabilitation for stroke has become an active area of research for assistance, monitoring and qualifying the rehabilitation treatments. The key issue needed for improving rehabilitation system is that patient feedback should be taken into account by the robotic rehabilitation systems for providing rehabilitation treatment. Changes in the delivery of rehabilitation treatment are an important issue since the patient or specialist should be able to express their sense about doing things and apply the needed improvements in treatment. Therefore, in this study, a three degreeoffreedom (3DOF) exoskeleton design of a thumb has been investigated. Then, a control structure is provided for greater security in which the patient feedback is evaluated in order to make necessary automatic changes in method of treatment (changing speed and force). In this design, a versatile framework with high performance is offered to simultaneously control thumb force and position regarding the patients’ feedback. This may help to keep the patient in the treatment process, reduce interventions and therapist caseload, effective automatic transmission of treatment and pain relief during the course of treatment. The results of the study suggest that the force and speed on the thumb can be changed during the rehabilitation period according to the patient's needs. This advantage may be considered as an essential step for improvement of the rehabilitation efficiency.
1

17
28


M.
Dehghani Rorani
Department of Mechanical Engineering,
Islamic Azad University, Majlesi Branch, Isfahan, Iran
Department of Mechanical Engineering,
Islamic
Iran
valiasr_mahdi@yahoo.com


S.
Rahmati
Department of Mechanical and Aerospace Engineering,
Islamic Azad University, Science and Research Branch, Tehran, Iran
Department of Mechanical and Aerospace Engineering
Iran
rahmati@rapidtoolpart.com
Patient Feedback
Thumb Exoskeleton
Thumb Force Control
Thumb Position Control
[[1] American Heart Association, Heart and Stroke Statistical Update,Available: http://www. Americanheart. org/ statistics/ stroke. htm, 2010. ##[2] Patten, C., Christine, E, Dairaghi, A., and Lum P., “Concurrent Neuromechanical and Functional Gains Following UpperExtremity Power Training PostStroke”, J Neuroeng Rehabil, Vol. 10, No. 1, 2013. ##[3] Albert, C., Lo, Peter, D., Guarino and et al., “RobotAssisted Therapy for LongTerm UpperLimb Impairment after Stroke”, New England Journal of Medicine, Vol. 362, No. 19, 2010, pp. 17721783. ##[4] Michielsen, ME., Selles, RW., Vander Geest JN., Eckhardt, M., Yavuzer, G., Stam, HJ., Smits, M., Ribbers GM., and Bussmann, JB., “Motor Recovery and Cortical Reorganization after Mirror Therapy in Chronic Stroke Patients a Phase Ii Randomized Controlled Trial”, Neurorehabilitation and Neural Repair, Vol. 25, No. 3, 2011, pp. 223215. ##[5] Hayner, K., Gibson, G., and Giles, G, M., “Comparison of ConstraintInduced Movement Therapy and Bilateral Treatment of Equal Intensity in People with Chronic UpperExtremity Dysfunction after Cerebrovascular Accident”, The American Journal of Occupational Therapy, Vol. 64, No. 4, 2010, pp. 528539. ##[6] Hammer, A, M., Lindmark, B., “Effects of Forced Use on Arm Function in the Subacute Phase after Stroke: A Randomized, Clinical Pilot Study”, Physical Therapy, Vol. 89, No. 6, 2009, pp. 526539. ##[7] Novak, D., Ziherl, Z., Olensek, A., and Milavec, M., et al., “Psychophysiological Responses to Robotic Rehabilitation Tasks in Stroke”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 18, No. 4, 2010, pp. 171361. ##[8] Choi, H., Gordon, J., Kim, D., and Schweighofer, N., “An Adaptive Automated Robotic TaskPractice System for Rehabilitation of Arm Functions after Stroke”, IEEE Transactions on Robotics, Vol. 25, No. 3, 2009, pp. 556568. ##[9] Poli, P., Morone G., Rosati G., and Masiero S., “Robotic Technologies and Rehabilitation: New Tools for Stroke Patients’ Therapy”, BioMed Research International, 2013, pp. 153872. ##[10] Esmzade, R., and Khosrojerdi M., “Modeling and Control of the Exoskeleton for Rehabilitation of Shoulder, Elbow and Wrist Motions”, nineteenth Conference on Biomedical Engineering, 2012. ##[11] Jamshidi, M., Rahmanzade, H., and Kaboudi, T., “Robotic Modeling for Rehabilitation of Arm and Knee Muscles”, First Conference of Rehabilitation Robotics, 2012. ##[12] Abdolvahab, M., Bagheri H., Movahedian, M., Olyaei GR., Jalili, M., and Baghestani, AR., “The effect of constraintinduced therapy on Activityof Daily Living of adults hemiplegic patients”, (in Persian), Modern Rehabilitation, Vol. 3. No. 1, 2, 2008, pp. 2832. ##[13] Worsnopp, T. T., Peshkin, M. A., Colgate, J. E., and Kamper, D. G., “An actuated finger exoskeleton for hand rehabilitation following stroke”, Proceedings of the IEEE 10th International Conference on Rehabilitation Robotics, Vol. 1, 2007. ##[14] Santos, V., ValeroCuevas, F., “Reported Anatomical Variability Naturally leads to multimodal distributions of Denavit_Hartenberg parameters for the human thumb”, IEEE Transactions on Biomedical Engineering. Vol. 53, No. 2, 2006, pp. 15563. ##[15] Abdallah, M. E., Platt, R., and Wampler, C. W., “Hargrave, B., Applied JointSpace Torque and Stiffness Control of TendonDriven Fingers”, In Proceedings of the 10th IEEERAS International Conference on Humanoid Robots (Humanoids), Nashville, TN, USA, 6–8 December, 2010, pp. 74–79. ##[16] Borghesan, G., Palli, G., and Melchiorri, C., “Design of TendonDriven Robotic Fingers: Modeling and Control Issues”, In Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, AK, USA, 3–7 May 2010,pp. 793–798. ##[17] Otadi, K., Hadian, MR., Olyaei, GR., Rasoulian, B., Emamdoost, S., Barikani, E., Torbatian, E., and Ghasemi, A., “The effect of modified constraint induced movement therapy on quality and amount of upper limb movements in chronic hemiplegic patients in comparison with traditional rehabilitation” (in Persian), Modern Rehabilitation, Vol. 6, No. 1, 2012, pp. 1318. ##[18] Bagheri, H., Abdolvahab, M., Dehghan L., Jalili M., and Beheshti, S., Z., “The effect of task oriented training on upper extremity function in children with spastic diplegia 812 years old”, (in persian), Modern Rehabilitatio, Vol. 3, No. 3, 2010, pp. 5761. ##[19] Sung, HoCA, J., YunHee, K., SangHyun, CH., JinHee, L., JiWon, P., and YongHyun, K., “Cortical reorganization induced by taskoriented training in chronic hemiplegic stroke patients”, Neuroreport, Vol. 14, No. 1, 2003, pp. 137141. ##[20] Lu, E., Wang, R, Boger, Hebert, J, D., and Mihailidis, A., “Development of a rehabilitation robot: national differences in therapist practice”, in Rehabil. Eng. Assist. Technol. Soc., 2011. ##[21] Dovat, L., Lambercy, O., et al., “HandCARE: a cableactuated rehabilitation system to train hand function after stroke”, IEEE Trans Neural Syst. Rehabil. Eng., Vol. 16, 2013, pp. 58291. ##[22] Li, J. W., Bu, C. G., and Wang, L., “The application of Macro command in ADAMS in building the virtual prototype of cable drill”, Mach. Tool Hydraul., Vol. 39, 2011, pp. 150–153. ##]
A study on the numerical simulation of thermomechanical behavior of the novel functionally graded thermal barrier coating under thermal shock
2
2
An attempt was made to investigate the thermal and residual stress distribution in a novel three layer (La2Zr2O7/8YSZ/NiCrAlY) during a reallike heating regime which includes heating, service time and final cooling. For achieving maximum accuracy and consistency in calculation of thermal and mechanical properties of hybrid coating system, all related and required properties were introduced to the software in temperaturedependent mode. Element modification approaches like mass scaling leads to a considerable reduction in running time while satisfying and not violating accuracy and converging criteria and constrains. Applying adaptive hybrid meshing techniques which applies both mesh–part dependency and independency during numerical iterative solution avoids element distortion and diverging in coupled problem. Heat flux and nodal temperature contours indicated that, most of damaging and harmful thermal load and residual stresses concentrate on ceramic top coats and this may lead less harm and life time reduction in the substrate.
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29
36


N.
Nayebpashaee
Department of Material & Metallurgy Engineering,
Iran University of Science and Technology
Department of Material & Metallurgy Engineerin
Iran
nayebpashaee@iust.ac.ir


H.
Vafaeenezhad
Department of Material & Metallurgy Engineering,
Iran University of Science and Technology
Department of Material & Metallurgy Engineerin
Iran
nayebpashae@iust.ac.ir


S. M. M.
Hadavi
Department of Material & Metalurgy Engineering,
Malek Ashtar University of Technology
Department of Material & Metalurgy Engineering
Iran
mehdihadavi@gmail.com


S. H.
Seyedein
Department of Material & Metallurgy Engineering,
Iran University of Science and Technology
Department of Material & Metallurgy Engineerin
Iran
seyedein@iust.ac.ir


M. R.
Aboutalebi
Department of Material & Metallurgy Engineering,
Iran University of Science and Technology
Department of Material & Metallurgy Engineerin
Iran
mrezab@pnu.ac.ir


H.
Sarpoolaky
Department of Material & Metallurgy Engineering,
Iran University of Science and Technology
Department of Material & Metallurgy Engineerin
Iran
hsarpoolaky@iust.ac.ir
Finite element simulation
Residual Stress
thermal barrier coating
Thermal Shock
[[1] Saeedi, B., Sabour, A., Ebadi, A., and Khoddami, A. M., “Influence of the Thermal Barrier Coatings Design on the Oxidation Behavior”, J. Mater. Sci. Technol., Vol.25, No.4, 2009, pp. 499507. ##[2] Di Girolamo, G., Blasi, C., Brentari, A., and Schioppa, M., “Microstructure and thermal properties of plasmasprayed ceramic thermal barrier coatings”, Studi & ricerche, Research papers, 2013. ##[3] Khor, K. A., “Thermal properties of plasmasprayed functionally graded thermal barrier coatings”, Thin Solid Films, Vol. 372, No.12, 2000, pp. 104113. ##[4] Wang, L., Wang, Y., Sun, X. G., He, J. Q., Pan, Z. Y., and Wang, C.H., “Microstructure and indentation mechanical properties of plasma sprayed nanobimodal and conventional ZrO28wt%Y2O3 thermal barrier coatings”, Vacuum, Vol. 86, No. 8, 2012, pp. 11741185. ##[5] Vaßen, R., Ophelia Jarligo, M., Steinke, T., Emil Mack, D., and Stöver, D., “Overview on advanced thermal barrier coatings”, Surface & Coatings Technology, Vol. 205, No.4, 2010, pp. 938942. ##[6] Baig, M. N.,.Khalid, F. A,.Khan, F. N, and Rehman, K., “Properties and residual stress distribution of plasma sprayed magnesia stabilized zirconia thermal barrier coatings”, Ceramics International, Vol. 40, No. 3, 2014, pp. 48534868. ##[7] Wang, L., Wang, Y., Sun, X. G, He, .J. Q., Pan, Z.Y., and Wang, C. H., “Thermal shock behavior of 8YSZ and doubleceramiclayer La2Zr2O7/8YSZ thermal barrier coatings fabricated by atmospheric plasma spraying”, Ceramics International, Vol. 38, No.5, 2012, pp. 35953606. . ##[8] Wang, L., Wang, Y., Zhang, W. Q., Sun, X. G., He, Z. Y. Pan, and Wang, C. H., “Finite element simulation of stress distribution and development in 8YSZ and doubleceramiclayer La2Zr2O7/8YSZ thermal barrier coatings during thermal shock”, Applied Surface Science, Vol. 258, No.8, 2012, pp. 3540 3551. ##[9] Matsumoto, M., Aoyama, K., Matsubara, H., Takayama, K., Banno, T., Kagiya, Y., and Sugita, Y., “Thermal conductivity and phase stability of plasma sprayed ZrO2–Y2O3–La2O3 coatings”, Surface & Coatings Technology, Vol. 194, No. 1, 2005, pp. 3135. ##[10] NAGA, S. M., “Ceramic matrix composite thermal barrier coatings for turbine parts”, In book: Advances in Ceramic Matrix Composites., Edition: 1, Chapter: 21, Publisher: Woodhead Publishing Limited, Editors: I.M. Low, pp.524533. ##[11] Khoddami, A. M., Sabour, A., Hadavi, S. M. M., “Microstructure formation in thermallysprayed duplex and functionally graded NiCrAlY/YttriaStabilized Zirconia coatings”, Surface & Coatings Technology, Vol. 201, No.12, 2007, pp. 6019–6024. ##[12] Mahamood, R. M., Akinlabi Member, E. T., IAENG, M. Shuklav, and Pityana, S, “Functionally Graded Material: An Overview”, Proceedings of the World Congress on Engineering, 2012(WCE 2012, July 4  6, 2012, London, U.K.). ##[13] Kieback, B., Neubrand, A., and Riedel, H., “Processing techniques for functionally graded materials”, Materials Science and Engineering A, Vol.362, No. 12, 2003, pp. 81–105. ##[14] Watremetz, B., BaiettoDubourg, M. C., and Lubrecht, A. A., “2D thermomechanical contact simulations in a functionally graded material: A multigridbased approach”, Tribology International, Vol. 40, 2007, pp. 754–762. ##[15] Jha, D. K., Kant, T., and Singh, R. K, “A critical review of recent research on functionally graded plates”, Composite Structures, Vol. 96, 2013, pp. 833849. ##[16] Lee W. Y, Stinton, D. P., “Concept of Functionally Graded Materials for Advanced Thermal Barrier Coating Applications”, Journal of the American Ceramic Society, Vol. 79, No. 12, 1996, pp. 30033012. ##[17] Helwany, S., “Applied Soil Mechanics with ABAQUS Applications”, John Wiley & Sons; 1 edition, 2007. ##]
Dynamic Analysis of AFM in Air and Liquid Environments Considering Linear and Nonlinear Interaction Forces by Timoshenko Beam Model
2
2
The atomic force microscopy of the cantilever beam frequency response behaviour in the liquid environment is different in comparison with air environment. In this paper, the dynamic analysis of AFM in the air and liquid environments is carried out in consideration of linear and nonlinear interaction forces and also the effect of geometrical parameters such as length, width, height; and inclined angle on the vibrating motion of the rectangular cantilever is investigated. A rectangular cantilever based on the Timoshenko theory is simulated in ADAMS software and more accurate results are obtained by considering the probe tip and the angular location of cantilever at simulation. At the end of the cantilever, a silicone probe is considered where the applied forces on it are approximated using two tangential and vertical springs. The vibrational simulation of cantilever at two states is carried out with regard to linear and nonlinear interaction forces. The amplitude and resonance frequency of the simulated cantilever based on Timoshenko theory are different from obtained results of EulerBernoulli theory due to the effect of shear deformation and rotary moment in Timoshenko theory. Therefore, the Timoshenko theory has better accuracy in comparison with Euler theory. Many chemical and biological processes occur instantly; therefore the use of cantilevers with small length for improving the imaging speed at the tapping mode and in the liquid environment is essential. Eventually short cantilever that is modeled based on the Timoshenko theory may produce more accurate results. This paper is aimed to demonstrate that the amplitude and resonance frequency of vibration in the liquid environment is different from amplitude and frequency of vibration in the air environment due to the damping coeficient and added mass of liquid.
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37
46


P.
Maleki Moghadam Abyaneh
Department of Computer Engineering,
Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Computer Engineering,
Science
Iran
maleki.peroshat@gmail.com


M.H.
Korayem
Department of Mechanical and Aerospace Engineering,
Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Mechanical and Aerospace Engineering
Iran
hkorayem@iust.ac.ir


B.
Manafi
Department of Mechanical and Aerospace Engineering,
Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Mechanical and Aerospace Engineering
Iran
b.manafi@srbiau.ac.ir


M.
Damircheli
Department of Mechanical and Aerospace Engineering,
Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Mechanical and Aerospace Engineering
Iran
md_19762003@yahoo.com
AFM
Frequency Response
Interaction force
Liquid Environment
Timoshenko theory
[[1] Binnig, G., Quate, CF., and Gerber, C., “Atomic Force Microscope”, Physical Review Letters 56.9, 1986, pp. 930. ##[2] Timoshenko, SP., Goodier, JN., “Theory of Elasticity”, New York: McGraw Hill, 1951. ##[3] Meirovitch, L., Elements of vibration analysis. McGraw Hill, 1975. ##[4] Rao, JA., “Advanced Theory of Vibration”, New York: Wiley, 1992. ##[5] Turner, JA., Wiehn, JS., “Sensitivity of flexural and torsional vibration modes of atomic force microscope cantilevers to surface stiffness variations”, Nanotechnology 12.3, 2001, pp. 322. ##[6] Rabe, U., Janser, K., and Arnold, W., “Vibrations of free and surfacecoupled atomic force microscope cantilevers: theory and experiment”, Review of Scientific Instruments 67.9, 1996, pp. 32813293. ##[7] Chang, WJ., Chu, SS., “Analytical solution of flexural vibration responses on taped atomic force microscope cantilevers”, Physics Letters A309.1, 2003, pp. 133137. ##[8] Song, Y., Bhushan, B., “Simulation of dynamic modes of atomic force microscopy using a 3D finite element model”, Ultramicroscopy 106.8, 2006, pp. 847873. ##[9] Arafat, HN., Nayfeh, AH., and AbdelRahman, EM., “Modal interactions in contactmode atomic force microscopes”, Nonlinear Dynamics, Vol. 54, No. 12, 2008, pp. 151166. ##[10] Wang, HC., “Generalized hyper geometric function solutions on the transverse vibration of a class of nonuniform beams” Journal of Applied Mechanics, Vol. 34, 1967, pp. 702. ##[11] Auciello, NM., “Transverse vibrations of a linearly tapered cantilever beam with tip mass of rotary inertia and eccentricity”,. Journal of Sound and Vibration 194.1, 1996, pp. 2534. ##[12] Rank, C., Pastushenko, V., Kienberger, F., Stroh, CM., and Hinterdorfer, P., “Hydrodynamic damping of a magnetically oscillated cantilever close to a surface”, Ultramicroscopy 100.3, 2004, pp. 301308. ##[13] Vancur, C., Dufour, I., Heinrich, S.M., Josse, F., and Hierlemann, A., “Analysis of resonating microcantilevers operating in a viscous liquid environment”, Sensors and Actuators 141, 2008, pp. 43–51. ##[14] Korayem, MH., Ebrahimi, N., “Nonlinear dynamics of tappingmode atomic force microscopy in liquid”, Journal of Applied Physics 84301, 2011, pp. 109117. ##[15] Kim, Y., Kang, SK., Choi, I., Lee, J., and Yi, J., “Dependence of image distortion in a liquidcell atomic force microscope on fluidic properties”, Applied Physics Letters173121, Vol. 88, No. 17, 2006. ##[16] Kim, Y., Yi, J., “Enhancement of topographic images obtained in liquid media by atomic force microscopy”, Journal of Physical Chemistry B, Vol. 110, No. 41, 2006, pp. 20526–32. ##[17] Biswas, S., Hirtz, M., Lenhert, S., and Fuchs, H., “Measurement of DPNInk Viscosity using an AFM Cantilever”, In: Nanotechnology Conference and Expo, NSTINanotech, Vol. 2, 2010, pp. 2314. ##[18] Damircheli, M., Korayem, MH., “Dynamic analysis of the AFM by applying the Timoshenko beam theory in the tapping mode and considering the impact of the interaction forces in a liquid environment”, Canadian Journal of Physics, 2013. ##[19] Korayem, MH., Damircheli, M., “The effect of fluid properties and geometrical parameters of cantilever on the frequency response of atomic force microscopy”, Precision Engineering, 2013. ##]
Effects of Slip Boundaries on Mixed Convection of Al2O3water Nanofluid in Microcavity
2
2
Due to the importance of the slip effect on modeling of microchannel and microcavity, numerical investigations have been introduced in this work for studying the mixed convection of Al2O3water nanofluid in a square microcavity. Governing equations are discretized and solved using the Finite Volume Method and SIMPLER algorithm. The Knudsen number is selected between 0.001 and 0.1 to consider slip velocity and the temperature jump boundary conditions in slip flow regime. In this study we investigate the influence of the Knudsen number on the average Nusselt number and heat transfer rate of Al2O3water nanofluid. Results shows that the average Nusselt number is the function of Richardson number, Knudsen number and volume fraction of nanoparticles. Increasing the Richardson number, makes the forced convection less effective and leads in reduction of the Nusselt number. Hence, increasing the Knudsen number, leads to the temperature gradient reduction and reducing the average Nusselt number. As a result, the average Nusselt number could be enhanced up to 10.93% by using nanoparticles in the base fluid.
1

47
54


A. R.
Rahmati
Department of Mechanical Engineering,
University of Kashan, Iran
Department of Mechanical Engineering,
University
Iran
ar_rahmati@kashanu.ac.ir


T.
Azizi
Department of Mechanical Engineering,
University of Kashan, Iran
Department of Mechanical Engineering,
University
Iran
azizi.taghi@yahoo.com


S. H.
Mousavi
Department of Mechanical Engineering,
University of Kashan, Iran
Department of Mechanical Engineering,
University
Iran
s.hmousavi@live.com


A.
Zarareh
Department of Mechanical Engineering,
University of Kashan, Iran
Department of Mechanical Engineering,
University
Iran
amin.zarareh@gmail.com
Knudsen Number
Mixed Convection
Microcavity
Nano fluid
Slip Flow
[[1] Beskok, A., Karniadakis, “Microflows Fundamentals and Simulation”, Springer, USA, 2001. ##[2] Muneer, A. Ismael, Ioan, Pop, and Ali, J., Chamkha, “Mixed convection in a liddriven square cavity with partial slip”, International Journal of Thermal Sciences, Vol. 82, 2014, pp. 4761. ##[3] Trisaksri, V., Wongwises, S., “Critical review of heat transfercharacteristics of nanofluids”, Renewable and Sustainable Energy Reviews, Vol. 11, No. 3, 2007, pp. 512523. ##[4] Ozerinc, S., Kakac, S. and YazIcIoglu, A.G., “Enhanced thermal conductivity of nanofluid: a stateoftheart review”, Microfluidics and Nanofluidics, Vol. 8, No. 2, 2010, pp. 145170. ##[5] Wang, X. Q, Mujumdar, A. S, “Heat transfer characteristics of nanofluids: a review”, International Journal of Thermal Sciences, Vol. 46, No. 1, 2007, pp. 1–19. ##[6] Wang, X. Q., Mujumdar, A. S., “A review on nanofluids— part I: theoretical and numerical investigations”, Brazilian Journal of Chemical Engineering, Vol. 25, No. 4, 2008, pp. 613 630. ##[7] Li Y, Zhou J, Tung S, Schneider E, Xi S, “A review on development of nanofluid preparation and characterization”, Powder Technology, Vol. 196, No. 2, 2009, pp. 89101. ##[8] Kakac, S.¸ Pramuanjaroenkij, A., “Review of convective heat transfer enhancement with nanofluids”, International Journal of Heat and Mass Transfer, Vol. 52, No. 1314, 2009, pp. 31873196. ##[9] Talebi, F., Mahmoudi, A. H., and Shahi, M., “Numerical study of mixed convection flows in a square liddriven cavity utilizing nanofluid”, International Communications in Heat and Mass Transfer, Vol. 37, 2010, pp. 7990. ##[10] Kandlikar, Satish G., “Heat transfer and fluid flow in minichannels and microchannels”, Elsevier, 2006. ##[11] Kuddusi, L., Cetegen, E., “Predication of temperature distribution and Nuseelt number in rectangular microchannelsat wall slip condition for all version of constant heat flux”, International Journal of Heat and Fluid Flow, Vol. 28, pp. 777786. ##[12] Renksizbulut, M., Niazmand, H., and Tercan, G., “Slipflow and heat transfer in rectangular microchannel with constant wall temperature”, International Journal of Thermal Sciences, Vol. 45, 2006, pp. 870881. ##[13] Mizzi, S., Emerson, D. R., Stefanov, S., Barbery, R.W., and Reese, J. M., “Microscale cavities in the slip and Transition flow regimes”, European Conference on Computational Fluid Dynamics, Netherlands, 2006. ##[14] Hettiarachchi, M., Golubovic, M., Worek, W. M., and Minkowycz, W. J., “Three dimensional laminar slipflow and heat transfer in a rectangular microchannel with constant wall temperature”, International Journal of Heat and Mass Transfer, Vol. 51, 2008, pp. 50885096. ##[15] Perumal, D. A., Krishna, V., Sarvesh, G., and Dass, A. K., “Numerical simulation of gaseous microflows by lattice boltzmann method”, International Journal of Recent Trends in Engineering, Vol. 1, No. 5, 2009. ##[16] Kuo, L. S., Chou, W. P., and Chen, P. H., “Effects of slip boundaries on thermal convection in 2D box using lattice Boltzmann method”, International Journal of Heat and Mass Transfer, Vol. 54, 2011, pp. 13401343. ##[17] Liu, X., Guoa, Zh., “Lattice Boltzmann study of gas flows in a long microchannel”, Computers and Mathematics with Applications, Vol. 65, 2013, pp. 186193. ##[18] Babaie, A., Saidi, M. H., and Sadeghi, A., “Heat transfer characteristics of mixed electroosmotic and pressure driven flow of powerlaw fluids in a slit microchannel”, International Journal of Thermal Sciences, Vol. 53, 2012, pp. 7179. ##[19] Shojaeian, M., Dibaji, S. A. R., “Threedimensional numerical simulation of the slip flow through triangular microchannels”, International Communications in Heat and Mass Transfer, Vol. 37, 2010, pp. 324329. ##[20] Shojaeian, M., Kosar, A., “Convective heat transfer and entropy generation analysis on Newtonian and nonNewtonian fluid flows between parallelplates under slip boundary conditions”, International Journal of Heat and Mass Transfer, Vol. 70, 2014, 664673. ##[21] Shetab Bushehri, M. R., Ramin, H., and Salimpour, M. R., “A new coupling method for slipflow and conjugate heat transfer in a parallel plate micro heat sink”, International Journal of Thermal Sciences, Vol. 89, 2015, pp. 174 184. ##[22] Alloui, Z., Vasseur, P., and Reggio, M., “Natural convection of nanofluids in a shallow cavity heated from below”, International Journal of Thermal Sciences, Vol. 50, 2010, pp. 19. ##[23] Pak, B. C., Cho, Y. I., “Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particle”, Exp. Heat Transfer, Vol. 11, 1999, pp. 151170. ##[24] Hwang, K. S., Lee, J. H., and Jang, S. P., “Buoyancydriven heat transfer of waterbased Al2O3 nanofluids in a rectangular cavity”, International Journal of Heat and Mass Transfer, Vol. 50, 2007, pp. 40034010. ##[25] Maxwell, J., “A Treatise on Electricity and Magnetism”, Second ed, Oxford University Press, Cambridge, UK, 1904. ##[26] Brinkman, H. C., “The viscosity of concentrated suspensions and solutions”. Journal of Chemical Physics, Vol. 20, 1952, pp. 571581. ##[27] Karniadakis, G., Beskok, A., Aluru, N., “Microflows and Nanoflows, Fundamentals and Simulation”, Springer, USA, 2005. ##[28] AbuNada, E., Chamkha, A. J., “Mixed convection flow in a lid driven square enclosure filled with a nanofluid”, European Journal of Mechanics B/Fluids, Vol. 29, 2010, pp. 472482. ##[29] Liu, Zh. Q., Jiang, S. R., Tamar, A., Yinnon, Mu, X. M., Kong, and Li, Y. J., “Effects of interfaces on dynamics in microfluidic devices slipboundaries’ impact on rotation characteristics of polar liquid film motors”, ar Xiv: 1404.5136v1 [condmat.soft]. ##[30] Aparajita, A., Satapathy, A. K., “Numeical analysis heat transfer characteristic of combined electroosmotic and pressuredriven fully developed flow of power law nanofluid in microchannels”, Proceedings of the 3rd European Conference on Microfluidics  Microfluidics 2012. ##[31] Aly, E. H., Ebaid, A., and Abd Elazem, N. Y., “Analytical and Numerical Investigations for the Flow and Heat Transfer of Nanofluids over a Stretching Sheet with Partial Slip Boundary Condition”, Applied Mathematics and Information Sciences, Vol. 8, No. 4, 2014, pp. 16391645. ##]
Prediction of Residual Stresses by Radial Basis Neural Network in HSLA65 Steel Weldments
2
2
This paper investigates the residual stress fields in the vicinity of weld bead in HSLA65 steel weldments using a neural network. This study consists of two cases: (i) the experimental analysis was carried out on the measurement of residual stresses by XRD technique. Many different specimens that were subjected to different conditions were studied. The values and distributions of residual stresses occurring in welding of HSLA65 plate under various conditions were determined. (ii) The mathematical modeling analysis has proposed the use of radial basis (RB) NN to determine the residual stresses based on the welding conditions. The input of RBNN are welding current, welding voltage, welding heat input, travel speed of welding, wire feed speed and distance from weld. The best fitting training data set was obtained with 18 neurons in the hidden layer, which made it possible to predict residual stresses with accuracy of at least as good as the experimental error, over the whole experimental range. After training, it was found that the regression values (R2) are 0.999664 and 0.999322 for newrbe and newrb functions respectively. Similarly, these values for testing data are 0.999425 and 0.998505, respectively. Based on the verification errors, it was shown that the radial basis function of neural network with newrbe function is superior in this particular case, and has the average error of 7.70% in predicting the residual stresses in HSLA65. This method is conceptually straightforward, and it is also applicable to other type of welding for practical purposes.
1

55
64


M.
Heidari
Department of Mechanical Engineering,
Aligudarz Branch, Islamic Azad University, Aligudarz, Iran
Department of Mechanical Engineering,
Aligudarz
Iran
moh104337@yahoo.com
Artificial neural network
HSLA6
Residual Stress
Radial Basis Function
[[1] Montemarano, T. W., Sack, B. P., Gudas, J. P., Vassilaros, M. G., and Vanderveldt, H. H., “High Strength Low Alloy Steels in Naval Construction”, Journal of Ship Production, Vol. 2, No. 3, 1986, pp. 145–162. ##[2] Czyryca, E. J., Link, R. E., Wong, R. J., Aylor, D. A., Montemarano, T. W., and Gudas, J. P., “Development and Certification of HSLA100 Steel for Naval Ship Construction”, Naval Engineers Journal, Vol. 102, No. 3, 1990, pp. 63–82. ##[3] DeLoach, J. J., Null, C., Flore, S., and Konkol, P., “The Right Welding Wire Could Help the U.S. Navy Save Millions”, Welding Journal, Vol. 78, No.6, 1999, pp. 55–58. ##[4] Sampath, K., “An Understanding of HSLA65 Plate Steels”, Journal of Materials Engineering and Performance, Vol. 15, No. 1, 2006, pp. 3240. ##[5] Barnes, S. J., Bhatti, A. R., Steuwer, A., Johnson, R., Altenkirch, J., and Withers, P. J., “Friction Stir Welding in HSLA65 Steel: Part I. Influence of Weld Speed and Tool Material on Microstructural Development”, Metallurgical and Materials Transactions A., Vol. 43, No.7, 2012, pp. 23422355. ##[6] Steuwer, A., Barnes, S. J., Altenkirch, J., Johnson, R., and Withers P. J., “Friction Stir Welding of HSLA65 Steel: Part II. The Influence of Weld Speed and Tool Material on the Residual Stress Distribution and Tool Wear”, Metallurgical and Materials Transactions A, Vol. 43, No.7, 2012, pp. 23562365. ##[7] Wei, L., Nelson, T. W., “Influence of Heat Input on Post Weld Microstructure and Mechanical Properties of Friction Stir Welded HSLA65 Steel”, Materials Science and Engineering: A. Vol. 556, 2012, pp. 51–59. ##[8] Nasser, S. N., Guo, W. G., “Thermo Mechanical Response of HSLA65 Steel Plates: Experiments and Modeling”, Mechanics of Materials. Vol. 37, No. 2, 2005, pp. 379–405. ##[9] Czyryca, E. J., Link, R. E., and Wong, R. J., “Evaluation of HSLA100 Steel for Surface Combatant Structural Certification”, DTRC/SME89/15, Bethesda, Maryland, 1989, pp. 1. ##[10] Spanos, G., Fonda, R. W., Vandermeer, R. A., and Matuszski, A., “Microstrucral Change in HSLA100 Steel Thermally Cycled Simulate the Heat–Affected Zone During Welding”, Metallurgical and Materials Transactions, Vol. 26, No.12, 1995, pp. 32773293. ##[11] Blackburn, J. M. “An Overview of Some Current Research on the Welding Residual Stresses and Distortion in the U.S. Navy”, 1996, IIW Doc. X135996. ##[12] Ahmadzadeh, M., Hoseinifard, A., Saranjam, B., and Salimi, H. R., “Prediction of Residual Stresses in Gas arc Welding by Back Propagation Neural Network”, NDT & E International, Vol. 52, 2012, pp. 136–143. ##[13] Zhang, J., Dong, P., and Brust, F., “Residual Stress Analysis and Fracture Assessment of Weld Joints in Moment Frames ASME. PVPFracture”, Fatigue and Weld Residual Stress, Vol. 393, 1999, pp. 201207. ##[14] Preston, R., Smith, S., Shercliff, H.,and Withers, P., “An Investigation in to the Residual Stresses in Aanaluminum 2024 Test Weld”, ASME. PVP—Fracture, Fatigue and Weld Residual Stress. Vol. 393, 1999, pp. 2657. ##[15] Dong, P., Hong, J., Bynum, J., and Rogers, P., “Analysis of residual stresses in Al–Li alloy repair welds”, ASME, PVP—Approximate Methods Des Anal Press Vessels Pip Compon, Vol. 347, 1997, pp. 61–75. ##[16] Karlsson, R. I., Josefson, B. L., “ThreeDimensional Finite Element Analysis of Temperatures and Stresses in a SinglePass ButtWelded Pipe”, ASME J Pressure Vessel Technol, Vol. 112, 1990, pp. 7684. ##[17] Goldak, J., Chakravarti, A., and Bibby, M. A. “New Finite Element Model for Welding Heat Sources”, Metall Trans B, Vol. 15B, 1984, pp. 299305. ##[18] Goldak, J., “Distortion and Residual Stress in Welds: the Next Generation”, 8th Trends in Welding Research, 2009, pp. 45–52. ##[19] Junek, L., Slovacek, M., Magula, V., and Ochodek, V., “Residual Stress Simulation Incorporating Weld HAZ Microstructure”, ASME PVP—Fracture, Fatigue and Weld Residual Stress. Vol. 393, 1999, pp. 17992. ##[20] Xu, S., Wang, W., “Numerical Investigation on Weld Residual Stresses in Tube to Tube Sheet Joint of a Heat Exchanger”, International Journal of Pressure Vessels and Piping, Vol. 101, 2013, pp. 3744. ##[21] Lee, C. H., Chang, K. H., “Prediction of Residual Stresses in High Strength Carbon Steel Pipe Weld Considering SolidState Phase Transformation Effects”, Computers & Structures, Vol. 89, 2011, pp. 256265. ##[22] Pearce, S. V., Linton, V. M., and Oliver, E. C., “Residual Stress in a Thick Section High Strength TButt Weld. Materials Science and Engineering: A”, Vol. 480, 2008, pp. 411418. ##[23] Brown, T. B., Dauda, T. A., Truman, C. E., Smith, D. J., Memhard, D., and Pfeiffer, W., “Predictions and Measurements of Residual Stress in Repair Welds in Plates”, International Journal of Pressure Vessels and Piping, Vol. 83, 2006, pp. 809818. ##[24] Bae, I. H., Lim, D. H., Gyun, M. N, and Kim, J. W., “Prediction of Residual Stress in the Welding Zone of Dissimilar Metals Using DataBased Models and Uncertainty Analysis”, Nuclear Engineering and Design, Vol. 240, 2010, pp. 2555–2564. ##[25] Yajiang, L., Juan, W., Maoai, C., and Xiaoqin, S., “Finite Element Analysis of Residual Stress in the Welded Zone of a High Strength Steel”, Bull. Mater. Sci., Vol. 27, No. 2, 2004, pp. 127–132. ##[26] Withers, P., Turski, M., Edwards, L., Bouchard, P., and Buttle, D., “Recent Advances in Residual Stress Measurement”, International Journal of Pressure Vessels and Piping, Vol. 85, No. 3, 2008, pp. 118127. ##[27] ElKassas, E. M. A., Mackie, R. I., and ElSheikh, A. I., “Using Neural Networks in ColdFormed Steel Design”, Computers & Structures, Vol. 79, 2001, pp. 16871696. ##[28] Freeman, J. A., “Simulating Neural Networks”, Addison–Wesley Publishing Company, Inc., New York, 1994. ##[29] Wasserman, P. D., “Neural Computing: Theory and Practice”, Van Nostrand Reinhold, New York, 1989. ##[30] Heidari, M., Homaei, H., “Design of a PID Controller for Suspension System by Back Propagation Neural Network”, Journal of Engineering, Vol. 2013, 2013, pp. 19. ##[31] Jacobs R. A., “Increased Rates of Convergence Through Learning Rate Adaptation”, Neural Network, Vol. 1, 1988, pp. 295307. ##[32] Demuth, H., Beale, M., “Matlab Neural Networks Toolbox”, User’s Guide. Copyright 19922001, The Math Works, Inc., See also URL http://www.mathworks.com. ##[33] Zhang, H., Wei, W., and Mingchen, Y., “Boundedness and Convergence of Batch BackPropagation Algorithm with Penalty for Feedforward Neural Networks”, Neurocomputing, Vol. 89, 2012, pp. 141146. ##]
Designing an Artificial Neural Network Based Model for Online Prediction of Tool Life in Turning
2
2
Artificial neural network is one of the most robust and reliable methods in online prediction of nonlinear incidents in machining. Tool flank wear as a tool life criterion is an important task which is needed to be predicted during machining processes to establish an online tool life estimation system.In this study, an artificial neural network model was developed to predict the tool wear and tool life in turning process. Cutting parameters and cutting forces were used as input and tool flank wear rates were regarded as target data for creating the online prediction system. SIMULINK and neural network tool boxes in MATLAB software were used for establishing a reliable online monitoring model. For generalizing the model, full factorial method was used to design the experiments. Predicted results were compared with the test results and a full confirmation of the model was reached.
1

65
71


A.
Salimiasl
Department of Mechanical Engineering,
Payame Noor University, Iran
Department of Mechanical Engineering,
Payame
Iran
aydin952@gmail.com


A.
Özdemir
Department of Manufacturing Engineering,
Faculty of Technology, University of Gazi, Ankara, Turkey
Department of Manufacturing Engineering,
Faculty
Turkey
ahmetoz@gazi.edu.tr


I.
Safarian
Department of Mechanical Engineering,
Payame Noor University, I.R. of Iran
Department of Mechanical Engineering,
Payame
Iran
ibr_safa@yahoo.com
artificial neural networks
Cutting Forces
Prediction
Tool Life
[[1] Isabelle Guyon, A. E., “An introduction to variable and feature selection”, J. Mach. Learn. Res., Vol. 3, 2003, pp. 11571182. ##[2] Cho, D.W., Lee, S. J., and Chu, C. N., “The state of machining process monitoring research in Korea”, International Journal of Machine Tools and Manufacture, Vol. 39, No. 11, 1999, pp. 16971715. ##[3] Liang, S. Y., Hecker, R. L., and Landers, R. G., “Machining process monitoring and control: The stateoftheart”, Journal of Manufacturing Science and EngineeringTransactions of the Asme, Vol. 126, No. 2, May 2004, pp. 297310. ##[4] Bahr, B., Motavalli, S., and Arfi, T., “Sensor fusion for monitoring machine tool conditions”, International Journal of Computer Integrated Manufacturing, Vol. 10, No. 5, 1997/01/01 1997, pp. 314323. ##[5] Ertekin, Y. M., Kwon, Y., and Tseng, T.L., “Identification of common sensory features for the control of CNC milling operations under varying cutting conditions”, International Journal of Machine Tools and Manufacture, Vol. 43, No. 9, 2003, pp. 897904. ##[6] Zhang, J. Z. Chen, J. C., “The development of an inprocess surface roughness adaptive control system in end milling operations”, International Journal of Advanced Manufacturing Technology, Vol. 31, No. 910, 2007, pp. 877887. ##[7] Benardos, P. G. Vosniakos, G. C., “Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments”, Robotics and ComputerIntegrated Manufacturing, Vol. 18, No. 56, 2002, pp. 343354. ##[8] Niu, Y., Wong, Y., and Hong, G., “An intelligent sensor system approach for reliable tool flank wear recognition”, The International Journal of Advanced Manufacturing Technology, Vol. 14, No. 2, 1998, pp. 7784. ##[9] Jantunen, E., “A summary of methods applied to tool condition monitoring in drilling”, International Journal of Machine Tools and Manufacture, Vol. 42, No. 9, 2002, pp. 9971010. ##[10] Teti, R., Jemielniak, K., O'Donnell, G., and Dornfeld, D., “Advanced monitoring of machining operations”, Cirp AnnalsManufacturing Technology, Vol. 59, No. 2, 2010, pp. 717739. ##[11] U. Zuperl, F. C., J. Balic. “Intelligent cutting tool condition monitoring in milling”, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 49, No. 2, 2011, pp. 477486. ##[12] Cakir, M. C. Isik, Y., “Detecting tool breakage in turning aisi 1050 steel using coated and uncoated cutting tools”, Journal of Materials Processing Technology, Vol. 159, No. 2, 2005, pp. 191198. ##[13] Scheffer, C., Kratz, H., Heyns, P. S., and Klocke, F., “Development of a tool wearmonitoring system for hard turning”, International Journal of Machine Tools and Manufacture, Vol. 43, No. 10, 2003, pp. 973985. ##[14] Gajate, A., Haber, R., del Toro, R., Vega, P., and Bustillo, A., “Tool wear monitoring using neurofuzzy techniques: a comparative study in a turning process”, Journal of Intelligent Manufacturing, Vol. 23, No. 3, 2012/06/01 2012, pp. 869882. ##[15] Sharma, V., Sharma, S. K., and Sharma, A., “Cutting tool wear estimation for turning”, Journal of Intelligent Manufacturing, Vol. 19, No. 1, 2008/02/01 2008, pp. 99108. ##[16] Haykin., S., Neural Networks: A Comprehensive Foundation, 1999, pp. 156254. ##[17] RH, N., “Kolmogrov’s mapping neural network existence theorem”, in Second IEEE International Conference on Neural Networks, San Diego, June 2124, 1987, pp. 1114. ##[18] Achanta AS, K. I., Rhodes CT, Artificial neural networks: implications for pharmaceutical sciences, 1995. ##[19] Baughman DR, L. Y., Neural Networks in Bioprocessing and Chemical Engineering,New York, 1995. ##[20] RJ, E., Introduction to backpropagation neural network computation, 1993, pp. 165170. ##[21] RH, N., “Kolmogrov’s mapping neural network existence theorem”, in Second IEEE International Conference on Neural Networks, San Diego, June 2124,1987.##]
A New Rolling Pressure Model for An Actual Reversing Cold Rolling Strip Mill
2
2
The forging model for cold rolling is one of the rolling models which is used in rolling calculations. In this model, the final rolling pressure is an average value and it is not as accurate for actual and industrial cases. Also, by using forging model, friction hill curves are plotted due to the central point of rolling bite length while frictional stresses intersect at the neutral point of rolling bite. In this study, a new model based on the forging model is presented to determine the rolling pressure during cold rolling process for using in a reversing tandem mill, where this is called “Improved forging model”. In the proposed model, the intersection of the frictional forces is the neutral point. Finally, the computing results from this new model coincide well with the precedent investigations.
1

73
80


M.
Heydari Vini
Department of Mechanical Engineering, Mobarakeh branch, Islamic Azad University, Mobarakeh, Isfahan, Iran
Department of Mechanical Engineering, Mobarakeh
Iran
m.heydarivini@gmail.com
Friction Hill Curve
Pressure Model
Two Stand Reversing Cold Mill
[[1] Montmitonnet, P., E. Massoni, M. Vacance, G. Sola, P. Gratacos, “Modelling for geometrical control in cold and hot rolling”, Iron making Steelmaking 20, 1993, pp. 254260. ##[2] Fleck, N. A., Johnson, K. L., Mear, M. E., and Zhang, L. C., “Cold rolling of foil”, Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 206, 1992, pp. 119131. ##[3] Zhang, L. C., “On the mechanism of cold rolling thin foil”, J. Mach. Tools Manuf. 35, 1995, pp. 363372. ##[4] Sutcliffe, M. P. F., Rayner, P. J., “Experimental measurements of load and strip profile in thin strip rolling”, Int. J. Mech. Sci. 40, 1998, pp. 887899. ##[5] Matsumoto, H., “Elasticplastic theory of cold and temper rolling”, Proceedings of the Eighth International Conference on Technol. Plast., Verona, Italy, October 9–13, 2005, pp. 521522. ##[6] Jiang, Z. Y., Tieu, A. K., Zhang, X. M., Lu, C., and Sun, W. H., “Finite element simulation of cold rolling of thin strip”, J. Mater. Proc. Technol. Vol. 140, 2003, pp. 542547. ##[7] Jiang, Z. Y., Tieu, A. K., “Elasticplastic finite element method simulation of thin strip with tension in cold rolling”, J. Mater. Proc. Technol. Vol. 130131, 2002, pp. 511–515. ##[8] Von Karman, T., “On the theory of rolling”, Z. Angew. Math. Mech, Vol. 5, 1925, pp. 139141. ##[9] Orowan, E., “The calculation of roll pressure in hot and cold flat rolling”, Proc. Inst. Mech. Eng. 150, 1943, pp. 140–167, J. Eng. Manuf. 206, 1992, pp. 119131. ##[10] Bland, D. R., Ford, H., “The calculation of rolls force and torque in cold strip rolling with tensions”, Proc. Inst. Mech. Eng., Vol. 159, 1948, pp. 144153. ##[11] Bland, D. R., Sims, R. B., “A note on the theory of rolling with tensions”, Proc. Inst. Mech. Eng., Vol. 167, 1953, pp. 371–374 ##[12] Alexander, J. M., “On the theory of rolling”, Proc. R. Soc. Lond. A 326, 1972, pp. 535–563. ##[13] Hitchcock, J. H., “Roll Neck Bearings”, Report of ASME Research Committee, 1935. ##[14] Fleck, N. A., Johnson, K. L., Mear, M. E., Jhang, L. C., “Cold rolling of foil”, Proc. Inst. Mech. Eng. 206, 1992, pp. 119–131. ##[15] Dixit, U. S., Dixit, P. M., “A finite element analysis of flat rolling and application of fuzzy set theory”, Int. J. Mach. Tools Manuf. 36, 1996, pp. 947–952. ##[16] Jingyu Shi, D. L. S., McElwain, Langlands, T. A. M., “A comparison of methods to estimate the roll torque in thin strip rolling”, Int. J. Mech. Sci. 43, 2001, pp. 611–630. ##[17] Schey, J. A., “Tribology in Metalworking Friction, Lubrication and Wear”, American Society of Metals, Metals Park, OH, 1983, pp. 27–130. ##[18] Tan, X., “Friction of plasticity: application of the dynamic friction model”, Proceedings of the Institution of Mechanical Engineers Part J. J. Eng. Tribol. 221, 2007, pp. 115–131. ##[19] Li, E. B. “Application of digital image correlation technique to dynamic measurement of the velocity field in the deformation zone in cold rolling”, optics and laser in engineering 39, 2003, pp. 479488. ##[20] Heydari Vini, M., Ebrahimi, H., Ziaeimoghadam, H. R., and Jafarian, M., “A new approach to determine the friction hill curves ,rolling load and analysis of permanent failure of work rolls in an actual cold rolling”, International Conference on Materials Heat Treatment (ICMH 2012), Islamic Azad University, Majlesi Branch, May 3031, 2012, Isfahan, Iran. ##[21] Moshksari, M., “Fundamentals of rolling”, 2nded., Shiraz University, 2005, Chaps. 5, 6, 8. ##[22] Kumar, A., Samarasekera, I. V., and Hawbolt, E. B., “Rollbite deformation during the hot rolling of steel strip”, Journal of Materials Processing Technology, Vol. 30, 1992, pp. 91–114. ##[23] LARKE, E. C., “The rolling of strip, sheets and plate”, science paperback edition, London, 1967, pp. 71126. ##]
Numerical Simulation of FluidStructure Interaction and its Application in Impact of LowVelocity Projectiles with Water Surface
2
2
In this article, finite element method and ALE formulation were used to numerically simulate impact of lowvelocity specific projectiles with water surface. For the simulation, LsDyna finite element code was used. Material models which were used to express behavior of air and water included Null material model. For the projectile, plastickinematics material model was applied. MieGruneisen equation of state was also attributed to air and water. First, the results were validated by analyzing the impact of metallic cylinder with water surface and then impact of a mine as a lowvelocity projectile was simulated. Among major outputs were force and pressure applied to the projectile, velocity and acceleration variations upon entering water, stressstrain variations and variations of water surface in various steps of analysis. The results showed that impact of structure with fluid can be modeled using finite element model with high accuracy in terms of quality and quantity.
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N.
Khazraiyan
Department of Mechanical Engineering,
Islamic Azad University, Islamshahr Branch, Tehran, Iran
Department of Mechanical Engineering,
Islamic
Iran
n_khazra@dr.com


N.
Dashtian Gerami
Department of Mechanical Engineering,
University of Tarbiat Modares, Tehran, Iran
Department of Mechanical Engineering,
University
Iran
n.dashtian@modares.ac.ir


M.
Damircheli
Department of Mechanical Engineering,
Islamic Azad University, ShahreQods Branch, Tehran, Iran
Department of Mechanical Engineering,
Islamic
Iran
md_19762003@yahoo.com
ALE Formulation
FluidStructure Interaction
Finite Element Method
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