With the increasing demand for efficiency and intelligence in grinding operations, the research of the automatic robotic processing method for complex surface workpieces becomes necessary. This paper proposes a new approach for robotic grinding on the free-form surface. Particularly, the authors design a novel force-controlled belt grinding tool, on which a contact model of grinding process is further developed. In order to improve the efficiency of grinding, a rapid robot trajectory planning method is proposed based on the workpiece’s computer-aided design model, and the process parameters are identified from simple experiments. Furthermore, a piecewise parameterized force control method based on impendence control for the tool is presented, and the purpose of this method is to compensate for the error caused by the rapid planning method and low consistency workblanks. Finally, the grinding experiments of the plate workpiece and complex shape workpiece are performed. The experiment results demonstrate that the proposed method is effective.
WangWLiuFLiuZ, et al.Prediction of depth of cut for robotic belt grinding. Int J Adv Manuf Technol2017; 91: 699–708.
2.
HuangHGongZMChenXQ, et al.SMART robotic system for 3D profile turbine vane airfoil repair. Int J Adv Manuf Technol2003; 21: 275–283.
3.
Pandremenos J and Doukas C. Machining with robots: a critical review. In: Proceedings of DET2011, Athens, Greece, 28–30 September 2011, pp.1–9..
4.
Xu X, Yang Y, Pan G, et al. A robotic belt grinding force model to characterize the grinding depth with force control technology. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol. 10984, LNAI, pp.287–298.
5.
RenXCabaravdicMZhangX, et al.A local process model for simulation of robotic belt grinding. Int J Mach Tools Manuf2007; 47: 962–970.
6.
GaoZLanXBianY. Structural dimension optimization of robotic belt grinding system for grinding workpieces with complex shaped surfaces based on dexterity grinding space. Chinese J Aeronaut2011; 24: 346–354.
7.
WuSKazerounianKGanZ, et al.A simulation platform for optimal selection of robotic belt grinding system parameters. Int J Adv Manuf Technol2013; 64: 447–458.
8.
VillagrossiECenatiCPedrocchiN, et al.Flexible robot-based cast iron deburring cell for small batch production using single-point laser sensor. Int J Adv Manuf Technol2017; 92: 1425–1438.
9.
ManouEVosniakosGCMatsasE, et al.Off-line programming of an industrial robot in a virtual reality environment. Int J Interact Des Manuf2019; 13: 507–519.
10.
PanZPoldenJLarkinN, et al.Recent progress on programming methods for industrial robots. Robot Comput-Integr Manuf2012; 28: 87–94.
11.
Domrös F, Rieger M and Kuhlenkötter B. Towards autonomous robot machining. In: Proceedings for the joint conference of ISR 2014 – 45th international symposium on robotics and robotik 2014 – 8th German conference on robotics, ISR/ROBOTIK2014, Munich, Germany, 2–3 June 2014, pp.448–453.
12.
XuXZhuDWangJ, et al.Calibration and accuracy analysis of robotic belt grinding system using the ruby probe and criteria sphere. Robot Comput-Integr Manuf2018; 51: 189–201.
13.
ZhaoPShiY. Posture adaptive control of the flexible grinding head for blisk manufacturing. Int J Adv Manuf Technol2014; 70: 1989–2001.
14.
Tanimoto H, Minami M, Yanou A, et al. Continuous shape-grinding experiment based on constraint-combined force / position hybrid control method. In: 2012 IEEE/SICE international symposium on system integration, SII 2012, vol. 7, 2012, pp.464–469.
Zhang X, Kuhlenkotter B and Kneupner K. A learning-based contact model in belt-grinding processes. In: International symposium on neural networks, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, vol. 3174, pp.761–766.
17.
RenXKuhlenkotterBMullerH. Simulation and verification of belt grinding with industrial robots. Int J Mach Tools Manuf2006; 46: 708–716.
18.
Lv H, Song Y and Jia P. Parameter optimization using PSO for ESN-based robotic belt grinding modeling. In: 2011 3rd international workshop on intelligent systems and applications, ISA 2011 – Proceedings, Wuhan, China, 28–29 May 2011, pp.11–14.
19.
YixuSHongjunYHongboL. Intelligent control for a robot belt grinding system. IEEE Trans Control Syst Technol2013; 21: 716–724.
20.
SongYLiangWYangY. A method for grinding removal control of a robot belt grinding system. J Intell Manuf2012; 23: 1903–1913.
21.
QiJZhangDLiS, et al.Modeling of the working accuracy for robotic belt grinding system for turbine blades. Adv Mech Eng2017; 9: 1–12.
22.
NgWXChanHKTeoWK, et al.Programming a robot for conformance grinding of complex shapes by capturing the tacit knowledge of a skilled operator. IEEE Trans Autom Sci Eng2017; 14: 1020–1030.
23.
Kuffner JJ and La Valle SM. RRT-connect: an efficient approach to single-query path planning. In: Proceedings - IEEE international conference on robotics and automation, vol. 2, 2000, pp.995–1001. New York: IEEE.
24.
PhamQC. A general, fast, and robust implementation of the time-optimal path parameterization algorithm. IEEE Trans Robot2014; 30: 1533–1540.
25.
TianFLvCLiZ, et al.Modeling and control of robotic automatic polishing for curved surfaces. CIRP J Manuf Sci Technol2016; 14: 55–64.
26.
TsaiMJHuangJFKaoWL. Robotic polishing of precision molds with uniform material removal control. Int J Mach Tools Manuf2009; 49: 885–895.
27.
NagataFHaseTHagaZ, et al.CAD/CAM-based position/force controller for a mold polishing robot. Mechatronics2007; 17: 207–216.
28.
DuanJGanYChenM, et al.Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot Autonom Syst2018; 102: 54–65.
