Erhan İlhan KONUKSEVEN
(ODTÜ Müh. Fakültesi, Makine Müh. Bölümü, Ankara, Türkiye)
Proje Grubu: TÜBİTAK EEEAG ProjeSayfa Sayısı: 163Proje No: 114E274Proje Bitiş Tarihi: 01.10.2017Türkçe

0 0
Yüksek Hassasiyetli Hibrit Robotik Çapak Alma Sistemi Geliştirilmesi
Çapak giderme islemi için robotik yöntemler gelistirmeye yönelik projemizde ?çapak alma/ taslama sürecinin modellenmesi?, ?çapak gidermeye yönelik kontrol sistemleri gelistirilme-si?, ?çapak gidermeye yönelik yörünge planlaması? ve ?gelistirilen yöntemlerin robotik deney sistemleri üzerinde test edilmesi? ana baslıkları altındaki çalısmalar yer almıstır. Öte yandan; bir endüstriyel robota uç-eyleyici olarak takılan bir paralel robottan olusan özel bir yapılandır-ma için insan kolu ve eli analojisi yapıldıgında çapak alma konusunda uzman insanların kol ve el hareketlerinin bu çalısmada kullanılabilmesi için ?hareket primitifleri? ismi verilen yöntem üzerinde çalısılmıstır. ?Seri robotlarla taslama/çapak alma? sistemlerinde ?esneme? problemi bulunmaktadır. Bu se-beple, ?taslama/çapak alma sürecinin modellenmesi? konusunda taslama kuvvetlerinin model-lenmesine yogunlasılmıstır. Bu baglamda ?penetrasyon testleri? ismi verilen özgün bir çalısma yapılmıstır. Çalısmayı yaparken ?deney tasarımı? teknikleri kullanılmıstır. Penetrasyon deney-leri sonucunda önce ?Yapay Sinir Agları? sonra ise enerji yöntemlerine odaklanılmıstır. Tasla-ma ucunun esnemesinin kestirilmesi ve taslama esnasında gerekli düzeltmelerin yapılmasına yönelik bir çalısmada klasik mukavemet yaklasımıyla kontrol sistemlerinin kompanzasyon kabiliyeti birlestirilmistir. Optimal ve parçanın kenarlarına yönelik bir yörünge planlamasından farklı olarak bölgesel yörünge planlamasında yörüngede isleme esnasında yapılması gereken degisiklikler almaçlardan (algılayıcılardan) elde edilen veriler ısıgında bulunmalıdır. Bunun baslıca sebebi ise bah-sedilen esneme problemidir. Insanın tecrübe ve almaçları vasıtasıyla olusturdugu esnek yö-rüngelerin kaydedilmesi ve kullanılmasına yönelik matematiksel bir alt yapı olan ?hareket pri-mitifleri? incelenmis ve farklı çalısmalar yapılmıstır. Bu çalısmalarda sanal yaysönümleyici- kütle sistemi ile 1 ve 6 serbestlik dereceli Haptik cihaz kullanarak bilek hareketleri ve becerile-ri modellenmis ve gelistirilmistir. Özel olarak üretilmis numuneler üzerinde istenilen miktarda ve profil seklini koruyacak sekilde yapılacak bir taslama islemi çapak alma islemine en benzer bir prototip problemdir. Bu açıdan, PID ve ?Bulanık Mantık? tabanlı bir kontrolcü gelistirilmistir. Numune yüzeyleri yardımcı bir ölçme deney sistemi ile taranıp önceki ve sonraki yüzey profilleri kıyaslanmıs ve istatistiksel bir tabanda elde edilen basarılar incelenmistir. Öte yandan, yukarıda bahsedilen esneme prob-lemine yönelik ?taslama ucu esneme kompanzasyonu? ele alınmıs olup esnemeden kaynak-lanan açısal sapmalar elimine edilmistir.
  • Açıkgöz, K. (2015). Prediction of the cutting forces for robotic grinding processes with abrasive mounted bits. Middle East Technical University, Ankara, Turkey.
  • Ali, Y., & Zhang, L. (2004). A fuzzy model for predicting burns in surface grinding of steel. International Journal of Machine Tools and Manufacture, 44(5), 563-571.
  • Amamou, R., Fredj, N. B., & Fnaiech, F. (2008). Improved method for grinding force prediction based on neural network. The International Journal of Advanced Manufacturing Technology, 39(7-8), 656-668.
  • Asakawa, N., Toda, K., & Takeuchi, Y. (2002). Automation of chamfering by an industrial robot; for the case of hole on free-curved surface. Robotics and Computer-Integrated Manufacturing, 18(5), 379-385.
  • Aslan, D., & Budak, E. (2014). Semi-analytical force model for grinding operations. Procedia CIRP, 14, 7-12.
  • Azizi, A., & Mohamadyari, M. (2015). Modeling and analysis of grinding forces based on the single grit scratch. The International Journal of Advanced Manufacturing Technology, 78(5-8), 1223-1231.
  • Bizzi, E., Cheung, V. C. K., d'Avella, A., Saltiel, P., & Tresch, M. (2008). Combining modules for movement. Brain research reviews, 57(1), 125-133.
  • Brinksmeier, E., Aurich, J., Govekar, E., Heinzel, C., Hoffmeister, H.-W., Klocke, F., . . . Wittmann, M. (2006). Advances in Modeling and Simulation of Grinding Processes. CIRP Annals - Manufacturing Technology, 55(2), 667-696.
  • Budak, E. (2000). Improving Productivity and Part Quality in Milling of Titanium Based Impellers by Chatter Suppression and Force Control. Annals of the ClRP, 49(1), 31– 36.
  • Carslaw, H., & Jaeger, J. C. (1959). Conduction of Heat in Solids. Oxford Science Publications.
  • Chang, L. H., & Fu, L. C. (1997). Nonlinear adaptive control of a flexible manipulator for automated deburring. In Robotics and Automation, 1997. Proceedings, 1997 IEEE International Conference on (Vol. 4, pp. 2844-2849). IEEE.
  • Che, C., & Ni, J. (2000). Ball-target-based extrinsic calibration technique for high-accuracy 3- D metrology using off-the-shelf laser-stripe sensors. Precision Engineering, 24(3), 210– 219.
  • Chen, X., & Rowe, W. B. (1996). Analysis and simulation of the grinding process. Part II: Mechanics of grinding. International Journal of Machine Tools and Manufacture, 36(8), 883-896.
  • Chiu, N., & Malkin, S. (1993). Computer Simulation for Cylindrical Plunge Grinding. CIRP Annals - Manufacturing Technology, 42(1), 383-387.
  • Chotiprayanakul, P., Liu, D., Wang, D., & Dissanayake, G. (2007). A 3-dimensional force field method for robot collision avoidance in complex environments. In International Symposium on Automation and Robotics in Construction. Indian Institute of Technology Madras.
  • Dai, H., Yuen, K. M., & Elbestawi, M. A. (1993). Parametric modelling and control of the robotic grinding process. The International Journal of Advanced Manufacturing Technology, 8(3), 182-192.
  • De Lacalle, L. N. L., Lamikiz, A., Sanchez, J. A., & de Bustos, I. F. (2005). Simultaneous measurement of forces and machine tool position for diagnostic of machining tests. IEEE transactions on instrumentation and measurement, 54(6), 2329-2335.
  • De Lacalle, L. L., Lamikiz, A., Muñoa, J., Salgado, M. A., & Sánchez, J. A. (2006). Improving the high-speed finishing of forming tools for advanced high-strength steels (AHSS). The International Journal of Advanced Manufacturing Technology, 29(1-2), 49-63.
  • Degallier, S., & Ijspeert, A. (2010). Modeling discrete and rhythmic movements through motor primitives: a review. Biological cybernetics, 103(4), 319-338.
  • Demetriou, M., & Lavine, A. (2000). Thermal Aspects of Grinding: The Case of Upgrinding. Journal of Manufacturing Science and Engineering, 122, 605-611.
  • Deng, Z. H., Zhang, X. H., Liu, W., & Cao, H. (2009). A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding. The International Journal of Advanced Manufacturing Technology, 45(9-10), 859.
  • Denkena, B., Möhring, H. C., & Will, J. C. (2007). Tool deflection compensation with an adaptronic milling spindle. In Conference on Smart Machining Systems.
  • Domroes, F., Krewet, C., & Kuhlenkoetter, B. (2013). Application and Analysis of Force Control Strategies to Deburring and Grinding. Modern Mechanical Engineering, 3(June), 11– 18. https://doi.org/10.4236/mme.2013.32A002
  • Duelen, G., Munch, H., Surdilovic, D., & Timm, J. (1992). Automated force control schemes for robotic deburring: development and experimental evaluation. In Industrial Electronics, Control, Instrumentation, and Automation, 1992. Power Electronics and Motion Control, Proceedings of the 1992 International Conference on (pp. 912-917). IEEE.
  • Durgumahanti, U. P., Singh, V., & Rao, P. V. (2010). A New Model for Grinding Force Prediction and Analysis. International Journal of Machine Tools and Manufacture, 50(3), 231-240.
  • Elbestawi, M. A., Yuen, K. M., Srivastava, A. K., & Dai, H. (1991). Adaptive force control for robotic disk grinding. CIRP Annals-Manufacturing Technology, 40(1), 391-394.
  • Feng, J., Chen, P., & Ni, J. (2013). Prediction of grinding force in micro grinding of ceramic materials by cohesive zone-based finite element method. The International Journal of Advanced Manufacturing Technology, 68(5-8), 1039-1053.
  • Gillespie, L. K. (1999). Deburring and edge finishing handbook. Society of Manufacturing Engineers.
  • Golberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addion Wesley. Reading.
  • Guo, C., Wu, Y., Varghese, V., & Malkin, S. (1999). Temperatures and Energy Partition for Grinding with Vitrified CBN Wheels. CIRP Annals - Manufacturing Technology, 48(1), 247-250.
  • Guo, M., Li, B., Ding, Z., & Liang, S. Y. (2016). Empirical modeling of dynamic grinding force based on process analysis. International Journal of Advanced Manufacturing Technology, 1-11.
  • Habibi, M., Arezoo, B., & Vahebi Nojedeh, M. (2011). Tool deflection and geometrical error compensation by tool path modification. International Journal of Machine Tools and Manufacture, 51(6), 439–449. https://doi.org/10.1016/j.ijmachtools.2011.01.009
  • Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design, PWS Pub. Co., Boston, 3632.
  • Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394(6695), 780.
  • Hasson, C. J., Hogan, N., & Sternad, D. (2012). Human control of dynamically complex objects. In Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on (pp. 1235-1240). IEEE.
  • Hecker, R. L., Ramoneda, I. M., & Liang, S. Y. (2003). Analysis of Wheel Topography and Grit Force for Grinding Process Modeling. Journal of Manufacturing Processes, 5(1), 13- 23.
  • Hecker, R. L., Liang, S. Y., Wu, X. J., Xia, P., & Jin, D. G. W. (2007). Grinding force and power modeling based on chip thickness analysis. The International Journal of Advanced Manufacturing Technology, 33(5), 449-459.
  • Heinzel, C., Grimme, D., & Moisan, A. (2006). Modeling of Surface Generation in Contour Grinding of Optical Molds. CIRP Annals - Manufacturing Technology, 55(1), 581–584.
  • Her, M. G., & Kazerooni, H. (1991). Automated robotic deburring of parts using compliance control. TRANS. ASME J. DYN. SYST. MEAS. CONTROL., 113(1), 60-66.
  • Hogan, N. (1985). Impedance Control: An Approach to Manipulation, Parts I, II, III. ASME Journal of Dynamic Systems, Measurement, and Control, 107(1).
  • Hogan, N., & Sternad, D. (2012). Dynamic primitives of motor behavior. Biological cybernetics, 1-13.
  • Hogan, N., & Sternad, D. (2013). Dynamic primitives in the control of locomotion. Frontiers in computational neuroscience, 7.
  • Huang, F. C., Gillespie, R. B., & Kuo, A. D. (2007). Visual and haptic feedback contribute to tuning and online control during object manipulation. Journal of motor behavior, 39(3), 179-193.
  • Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical movement primitives: learning attractor models for motor behaviors. Neural computation, 25(2), 328-373.
  • Inasaki, I. (1996). Grinding Process Simulation Based on the Wheel Topography Measurement. CIRP Annals - Manufacturing Technology, 45(1), 347–350.
  • Israr, A., Kapson, H., Patoglu, V., & O'Malley, M. K. (2009). Effects of magnitude and phase cues on human motor adaptation. In Euro Haptics conference, 2009 and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics 2009. Third Joint (pp. 344-349). IEEE.
  • Jaeger, J. C. (1942). Moving Sources of Heat. Proc. R. Soc., 76(3), 203-224.
  • Jatta, F., Legnani, G., & Visioli, A. (2006a). Friction Compensation in Hybrid Force / Velocity Control of Industrial Manipulators. IEEE Transactions on Industrial Electronics, 53(2), 604–613.
  • Jatta, F., Legnani, G., Visioli, A., & Ziliani, G. (2006b). On the use of velocity feedback in hybrid force/velocity control of industrial manipulators. Control Engineering Practice, 14(9), 1045–1055. https://doi.org/10.1016/j.conengprac.2005.06.005
  • Jawanjal, T. R., & Bagde, S. T. (2013). An Advanced Chamfering System. International Journal of Emerging Technology and Advanced Engineering, 3, 598-601.
  • Jordan, M. I., & Rumelhart, D. E. (1992). Forward models: Supervised learning with a distal teacher. Cognitive science, 16(3), 307-354.
  • Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE transactions on Robotics and Automation, 12(4), 566-580.
  • Kazerooni, H., Bausch, J. J., & Kramer, B. M. (1986). An approach to automated deburring by robot manipulators. Journal of dynamic systems, measurement, and control, 108(4), 354-359.
  • Khansari-Zadeh, S. M., & Billard, A. (2011). Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Transactions on Robotics, 27(5), 943-957.
  • Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The international journal of robotics research, 5(1), 90-98.
  • Kim, C. T., & Lee, J. J. (2008). Training two-layered feedforward networks with variable projection method. IEEE Transactions on Neural Networks, 19(2), 371-375.
  • Kline, W. A., Devor, R. E., & Shareef, I. A. (1982). The Prediction of Surface Accuracy in End Milling. In Trans, of ASME (Vol. 104, pp. 272–278). https://doi.org/10.1115/1.3185830
  • Ko, S. L., & Park, S. W. (2006). Development of an effective measurement system for burr geometry. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(4), 507-512.
  • LaValle, S. M. (2006). Planning algorithms. Cambridge university press.
  • Law, K. M. Y., Geddam, A., & Ostafiev, V. A. (1999). A process-design approach to error compensation in the end milling of pockets. Journal of Materials Processing Technology, 89–90, 238–244. https://doi.org/10.1016/S0924-0136(99)00031-X
  • Leali, F., Pellicciari, M., Pini, F., Berselli, G., & Vergnano, A. (2013). An offline programming method for the robotic deburring of aerospace components. In Robotics in Smart Manufacturing (pp. 1-13). Springer, Berlin, Heidelberg.
  • Lee, S., Li, C., Kim, D., Kyung, J., & Han, C. (2009). The direct teaching and playback method for robotic deburring system using the adaptive-force-control. In 2009 IEEE International Symposium on Assembly and Manufacturing, ISAM 2009 (pp. 235–241).
  • Lee, H., Krebs, H. I., & Hogan, N. (2012). Linear time-varying identification of ankle mechanical impedance during human walking. In 5th Annual Dynamic Systems and Control Conference. Fort Lauderdale, FL: ASME.
  • Lee, H., Krebs, H. I., & Hogan, N. (2014). Multivariable dynamic ankle mechanical imped-ance with active muscles. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(5), 971-981.
  • Legnani, G., Tiboni, M. (2014). Optimal design and application of a low-cost wire-sensor system for the kinematic calibration of industrial manipulators, In Mechanism and Machine Theory, Volume 73, 2014, Pages 25-48
  • Leonesio, M., Parenti, P., Cassinari, A., Bianchi, G., & Monno, M. (2012). A Time-Domain Surface Grinding Model for Dynamic Simulation. Procedia CIRP: 3rd CIRP Conference on Process Machine Interactions, 4, 166-171.
  • Li, D., Xu, M., Wei, C., Hu, D., & Xu, L. (2012). A dynamic threshold-based fuzzy adaptive control algorithm for hard sphere grinding. The International Journal of Advanced Manufacturing Technology, 60(9), 923-932.
  • Liao, L., Xi, F. J., & Liu, K. (2008). Modeling and control of automated polishing/deburring process using a dual-purpose compliant tool head. International Journal of Machine Tools and Manufacture, 48(12), 1454-1463.
  • Liu, S., & Asada, H. (1991). Adaptive control of deburring robots based on human skill models. In Decision and Control, 1991. Proceedings of the 30th IEEE Conference on (pp. 348-353). IEEE.
  • Liu, C. H., Chen, a., Wang, Y.-T., & Chen, C.-C. a. (2004). Modelling and simulation of an automatic grinding system using a hand grinder. The International Journal of Advanced Manufacturing Technology, 23, 874–881.
  • Liu, C. C. H., Chen, A., Chen, C.-C. a. C., & Wang, Y. Y.-T. (2005). Grinding force control in an automatic surface finishin g system. Journal of Materials Processing Technology, 170(1–2), 367–373. Luo, J. (Ed.). (2012). Soft computing in information communication technology (Vol. 2). Springer Science & Business Media.
  • Malkin, S., & Cook, N. H. (1971). The wear of grinding wheels: part 1—attritious wear. Journal of Engineering for Industry, 93(4), 1120-1128.
  • Malkin, S. (2002). Grinding Technology. Society of Manufacturing Engineers.
  • Mendes, N., Neto, P., Pires, J. N., & Loureiro, A. (2013). An optimal fuzzy-PI force/motion controller to increase industrial robot autonomy. The International Journal of Advanced Manufacturing Technology, 68(1-4), 435-441.
  • Nagengast, A. J., Braun, D. A., & Wolpert, D. M. (2010). Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty. PLoS computational biology, 6(7), e1000857.
  • Navid, M. L., & Konukseven, E. ilhan. (2017). Hybrid model based on energy and experimental methods for parallel hexapod-robotic light abrasive grinding operations. The International Journal of Advanced Manufacturing Technology.
  • Pagilla, P. R., & Yu, B. (2001). Adaptive control of robotic surface finishing processes. In Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148) (Vol. 1, pp. 630–635).
  • Park, J., Kim, S. H., & Kim, S. (2008). Active compliant motion control for grinding robot. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 17, pp. 4285–4289).
  • Part, S. I. (1985). Impedance control: An approach to manipulation. Journal of dynamic systems, measurement, and control, 107, 17.
  • Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on (pp. 763-768). IEEE.
  • Pedrocchi, N., Visioli, A., Ziliani, G., & Legnani, G. (2008). On the elasticity in the dynamic decoupling of hybrid force/velocity control in the contour tracking task. In 2008 IEEE/RSJ Intern. Conf. on Intell. Robots and Sys., IROS (pp. 955–960).
  • Pervez, A., Ali, A., Ryu, J. H., & Lee, D. (2017). Novel learning from demonstration approach for repetitive teleoperation tasks. In World Haptics Conference (WHC), 2017 IEEE (pp. 60-65). IEEE.
  • Posada, J. R. D., Kumar, S., Kuss, A., Schneider, U., Drust, M., Dietz, T., & Verl, A. (2016). Automatic Programming and Control for Robotic Deburring. In ISR 2016: 47st International Symposium on Robotics; Proceedings of (pp. 1-8). VDE.
  • Princely, F. L., & Selvaraj, T. (2014). Vision assisted robotic deburring of edge burrs in cast parts. Procedia Engineering, 97, 1906-1914.
  • Raibert, M. H., & Craig, J. J. (1981). Hybrid position/force control of manipulators. Journal of Dynamic Systems, Measurement, and Control, 102(127), 126-133.
  • Rowe, W. B., Yan, L., Inasaki, I., & Malkin, S. (1994). Applications of Artificial Intelligence in Grinding. CIRP Annals - Manufacturing Technology, 43(2), 521-531.
  • Ryu, S. H., Lee, H. S., & Chu, C. N. (2003). The form error prediction in side wall machining considering tool deflection. International Journal of Machine Tools & Manufacture, 43, 1405–1411.
  • Santello, M., Baud-Bovy, G., & Jörntell, H. (2013). Neural bases of hand synergies. Frontiers in computational neuroscience, 7.
  • Schaal, S., Sternad, D., Osu, R., & Kawato, M. (2004). Rhythmic arm movement is not discrete. Nature neuroscience, 7(10), 1136.
  • Schueler, G. M., Engmann, J., Marx, T., Haberland, R., & Aurich, J. C. (2010). Burr for-mation and surface characteristics in micro-end milling of titanium alloys. In Burrs-analysis, control and removal (pp. 129-138). Springer, Berlin, Heidelberg.
  • Schutter, J. De. (1987). A study of active compliant motion control methods for rigit manipulators based on a generic scheme. In Robotics and Automation. Proceedings (Vol. 4, pp. 1060–1065).
  • Shugui, L. I. U., Shaliang, T., Chao, C., Liu, S., Tang, S., & Chen, C. (2009). Calibration of Optical Probe for CMM. 2009 Symposium on Photonics and Optoelectronics, (2), 1–4.
  • Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering, 2013.
  • Smith, K. B., & Zheng, Y. F. (2000). Point Laser Triangulation Probe Calibration for Coordinate Metrology. Journal of Manufacturing Science and Engineering, 122(10), 582–593.
  • Song, H. C., Kim, B. S., & Song, J. B. (2012). Tool path generation based on matching between teaching points and CAD model for robotic deburring. In Advanced Intelligent Mechatronics (AIM), 2012 IEEE/ASME International Conference on (pp. 890-895). IEEE.
  • Song, H. C., & Song, J. B. (2013). Precision robotic deburring based on force control for arbitrarily shaped workpiece using CAD model matching. International Journal of Precision Engineering and Manufacturing, 14(1), 85-91.
  • Soori, M., Arezoo, B., & Habibi, M. (2014). Virtual machining considering dimensional, geometrical and tool deflection errors in three-axis CNC milling machines. Journal of Manufacturing Systems, 33(4), 498–507.
  • Sugita, S., Itaya, T., & Takeuchi, Y. (2004). Development of robot teaching support devices to automate deburring and finishing works in casting. The International Journal of Advanced Manufacturing Technology, 23(3-4), 183-189.
  • Suh, S.-H., Cho, J.-H., & Hascoet, J.-Y. (1996). Incorporation of Tool Deflection in Tool Path Computation: Simulation and Analysis. Journal of Manufacturing Systems, 15(3), 190– 199.
  • Tahvilian, A. M., Hazel, B., Rafieian, F., Liu, Z., & Champliaud, H. (2016). Force model for impact cutting grinding with a flexible robotic tool holder. The International Journal of Advanced Manufacturing Technology, 85(1-4), 133-147.
  • Tang, J., Du, J., & Chen, Y. (2009). Modeling and experimental study of grinding forces in surface grinding. Journal of materials processing technology, 209(6), 2847-2854.
  • Tao, Y., Zheng, J., Lin, Y., Wang, T., Xiong, H., He, G., & Xu, D. (2015). Fuzzy PID control method of deburring industrial robots. Journal of Intelligent & Fuzzy Systems, 29(6), 2447-2455.
  • Thomessen, T., & Lien, T. K. (2000). Robot Control System for Safe and Rapid Programming of Grinding Applications. Industrial Robot: An International Journal, 27(6), 437–444.
  • Thomessen, T., Lien, T. K., & Sannæs, P. K. (2001). Robot control system for grinding of large hydropower turbines. Industrial Robot: An International Journal, 28(4), 328-334.
  • Tiana, L., Fu, Y., Xu, J., Li, H., & Ding, W. (2015). The influence of speed on material removal mechanism in high speed grinding with single grit. International Journal of Machine Tools and Manufacture, 89, 192–201.
  • Tönshoff, H. K., Peters, J., Inasaki, T., & Paul, T. (1992). Modelling and Simulation of Grinding. Annals of the CIRP, 41(2), 677-688.
  • Valente, C., & Oliveira, J. (2004). A new approach for tool path control in robotic deburring operations. In ABCM Symposium series in Mechatronics (Vol. 1, pp. 124-133).
  • Vertut, J. (Ed.). (2013). Teleoperation and robotics: applications and technology (Vol. 3). Springer Science & Business Media.
  • Wang, D., Ge, P., Bi, W., & Jiang, J. (2014). Grain trajectory and grain-workpiece contact analyses for modeling of grinding force and energy partition. The International Journal of Advanced Manufacturing Technology, 70(9-12), 2111-2123.
  • Winkler, A., & Suchý, J. (2012). Position feedback in force control of industrial manipulators - An experimental comparison with basic algorithms. In 2012 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2012 - Proceedings (pp. 31–36).
  • Wolpert, D. M., Miall, R. C., & Kawato, M. (1998). Internal models in the cerebellum. Trends in cognitive sciences, 2(9), 338-347.
  • Wolpert, D. M., & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature neuroscience, 3(11s), 1212.
  • Yang, M. Y., & Choi, J. G. (1998). A tool deflection compensation system for end milling accuracy improvement. Journal of Manufactuing Science and Engineering, 120(2), 222–229.
  • Yang, Z., Gao, Y., Zhang, D., & Huang, T. (2003). A Self-tuning Based Fuzzy-PID Approach for Grinding Process Control. Key Engineering Materials. Trans Tech Publications, 238–239, 375–382.
  • Zeng, G., & A., H. (1997). An overview of robot force control. Robotica, 15(5), 473–482.
  • Zexiao, X., Qiumei, Z., & Guoxiong, Z. (2004). Modeling and calibration of a structured-lightsensor- based five-axis scanning system. Measurement, 36(2), 185–194.
  • Zhang, B., Wang, J., Yang, F., & Zhu, Z. (1999). The effect of machine stiffness on grinding of silicon nitride. International Journal of Machine Tools and Manufacture, 39(8), 1263- 1283.
  • Zhang, H., Chen, H., Xi, N., Zhang, G., & He, J. (2006). On-line path generation for robotic deburring of cast aluminum wheels. In Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on (pp. 2400-2405). IEEE.
  • Zhao, H., Powell, M. J., & Ames, A. D. (2014). Human‐inspired motion primitives and transitions for bipedal robotic locomotion in diverse terrain. Optimal Control Applications and Methods, 35(6), 730-755.
  • Ziliani, G., Visioli, A., & Legnani, G. (2007). A mechatronic approach for robotic deburring. Mechatronics, 17(8), 431-441.

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © 2019 Tüm Hakları Saklıdır.