Industrial robots provide a flexible and cost-effective solution for machining large workpieces, but their application is often limited by inherent structural properties. Serial articulated robots, in particular, exhibit relatively low, configuration-dependent stiffness, making them susceptible to vibrations during milling that impair the final surface topography. To address this, this paper presents a predictive model for surface topography that integrates the robot’s posture-dependent dynamics with tool vibration. The proposed method begins by establishing an ideal kinematic model of the cutting edge trajectory. Next, the posture-dependent frequency response function is predicted using an inverse distance-weighted algorithm, and the dynamic parameters of the dominant vibration mode are identified. The resulting tool vibration displacements are then calculated by solving the system’s dynamic model and are integrated into the cutting edge’s swept surface. Milling experiments were conducted to validate the model, demonstrating strong agreement between the predicted and measured surface topography.
AlmeidaSMoJBilC, et al. Accurate vibration-free robotic milling electric discharge machining. Int J Adv Manuf Technol2022; 122: 343–363.
2.
GrossiNScippaASalleseL, et al. On the generation of chatter marks in peripheral milling: a spectral interpretation. Int J Mach Tools Manuf2018; 133: 31–46.
3.
WojciechowskiSTwardowskiPPelicM, et al. Precision surface characterization for finish cylindrical milling with dynamic tool displacements model. Precis Eng2016; 46: 158–165.
4.
YanBZhuLLiuC.Prediction model of peripheral milling surface geometry considering cutting force and vibration. Int J Adv Manuf Technol2020; 110: 1429–1443.
5.
ZhuoYHanZAnD, et al. Surface topography prediction in peripheral milling of thin-walled parts considering cutting vibration and material removal effect. Int J Mech Sci2021; 211: 106797.
6.
GuoQWangWJiangY, et al. 3D surface topography prediction in the five-axis milling of plexiglas and metal using cutters with non-uniform helix and pitch angles combining runout. J Mater Process Technol2023; 314: 117885.
7.
DongJChangZHeJ, et al. A novel simulation model of three-dimensional surface topography for five-axis CNC milling using bull-nose tool. Int J Adv Manuf Technol2023; 128: 5041–5060.
8.
LazkanoXAristimuñoPXAizpuruO, et al. Roughness maps to determine the optimum process window parameters in face milling. Int J Mech Sci2022; 221: 107191.
9.
LyuWLiuZCaiY, et al. A divide and conquer approach for machined surface topography reconstruction in peripheral milling Inconel 718. Surf Topogr Metrol Prop2023; 11: 015002.
10.
SlamaniMGauthierSChatelainJF.A study of the combined effects of machining parameters on cutting force components during high speed robotic trimming of CFRPs. Measurement2015; 59: 268–283.
11.
Unnikrishna PillaiJSanghrajkaIShunmugavelM, et al. Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi–Grey relational approach. Measurement2018; 124: 291–298.
12.
XuPGaoYYaoX, et al. Influence of process parameters and robot postures on surface quality in robotic machining. Int J Adv Manuf Technol2023; 124: 2545–2561.
13.
MousaviSGagnolVBouzgarrouBC, et al. Stability optimization in robotic milling through the control of functional redundancies. Robot Comput Integr Manuf2018; 50: 181–192.
14.
CvitanicTNguyenVMelkoteSN.Pose optimization in robotic machining using static and dynamic stiffness models. Robot Comput Integr Manuf2020; 66: 101992.
15.
XueYSunZLiuS, et al. Stiffness oriented placement optimization of machining robots for large component flexible manufacturing system. Machines2022; 10: 389.
16.
XiongXLiYQinH. Structural dynamic performance evaluation of industrial robots based on vibration tests. In: 2018 4th Information technology and mechatronics engineering conference (ITOEC), Chongqing, China, 2018, pp.302–306. IEEE.
17.
WangLLiuYYuY, et al. Optimization of redundant degree of freedom in robotic milling considering chatter stability. Int J Adv Manuf Technology2022; 121: 8379–8394.
18.
HouTLeiYDingY.Pose optimization inrobotic milling based on surface location error. J Manuf Sci Eng2023; 145: 084501.
19.
LiaoZYQinZZXieHL, et al. Constant load toolpath planning and stiffness matching optimization in robotic surface milling. Int J Adv Manuf Technol2024; 130: 353–368.
20.
XuXYeSYangZ, et al. Analysis and prediction of surface roughness for robotic belt grinding of complex blade considering coexistence of elastic deformation and varying curvature. Sci China Technol Sci2021; 64: 957–970.
21.
SunYShiZGuoQ, et al. A novel method to predict surface topography in robotic milling of directional plexiglas considering cutter dynamical displacement. J Mater Process Technol2022; 304: 117545.
22.
QinXLiYFengG, et al. A novel surface topography prediction method for hybrid robot milling considering the dynamic displacement of end effector. Int J Adv Manuf Technol2024; 130: 3495–3508.
23.
ArtetxeEOlveraDVDe LacalleLNL, et al. Solid subtraction model for the surface topography prediction in flank milling of thin-walled integral blade rotors (IBRs). Int J Adv Manuf Technol2017; 90: 741–752.
24.
ArizmendiMJiménezACumbicusWE, et al. Modelling of elliptical dimples generated by five-axis milling for surface texturing. Int J Mach Tools Manuf2019; 137: 79–95.
25.
OzoegwuCEberhardP.Closed-form models for the cutting forces of general-helix cylindrical milling tools. Proc Inst Mech Eng B J Eng Manuf2023; 237: 2286–2298.
26.
AenlleMJuulMBrinckerR.Modal mass and length of mode shapes in structural dynamics. Shock Vib2020; 2020: 1–16.
27.
BrandtABerardengoMManzoniS, et al. Scaling of mode shapes from operational modal analysis using harmonic forces. J Sound Vib2017; 407: 128–143.
28.
LiuJNiuYZhaoY, et al. Prediction of surface topography in robotic ball-end milling considering tool vibration. Actuators2024; 13: 72.
