Research on machining error prediction and compensation technology for a stone-carving robotic manipulator

Yin, Fangchen; Ji, Qingzhi; Wang, Cai-zhi

doi:10.1007/s00170-021-07230-z

Cited by 19 publications

(10 citation statements)

References 19 publications

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“…With the developing of manufacturing industry, there is a high demand on the precision of CNC machining. It is clear that stability and accuracy [1,2] are the two key indicators of the performance of CNC machine. Therefore, it is essential to improve the CNC machining precision, and error compensation that is economic and effective way to for CNC machining accuracy improvement has been wildly applied.…”

Section: Introductionmentioning

confidence: 99%

A data-driven high-precision modeling method of machine tool spatial error under the influence of Abbe error

Zhang

jiang²,

Chen³

et al. 2023

Preprint

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Due to the complex mechanism of the influence of Abbe error on spatial accuracy, the Abbe error accumulated in the traditional spatial accuracy model is hard to be identified and cannot be eliminated, which affects the modeling accuracy and restricts the effect of accuracy improvement. This paper presents a data-driven spatial accuracy modeling method for machine tool under the influence of Abbe error, using a three-axis coupling measurement optical path to directly measure the comprehensive spatial accuracy data of machine tool containing Abbe error. In addition, in order to effectively identify the Abbe error in the comprehensive spatial accuracy, the Abbe error quantization function is established to eliminate the Abbe error in the spatial accuracy data of machine tool by analyzing its formation mechanism in the measurement process. Further, aiming at the problem of small data samples after eliminating Abbe error, the data samples are extended based on the degradation mechanism of machine tool spatial accuracy at different coordinate positions, and a high-precision spatial error model for machine tool is given. Finally, the experiment is conducted on a three-axis CNC machine tool with the model accuracy of over 95%, and the example application verification shows that the developed model scheme is feasible and effective.

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Section: Introductionmentioning

confidence: 99%

A data-driven high-precision modeling method of machine tool spatial error under the influence of Abbe error

Zhang

jiang²,

Chen³

et al. 2023

Preprint

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“…Liu et al [13] used the simplex method of linear programming to reduce tool fluctuation range and optimize tool trajectory. Chen et al [14] developed a global stiffness model and predicted the machining error and milling depth when machining a marble workpiece. They used an interval correction strategy to construct a discrete control system for error compensation.…”

Section: Introductionmentioning

confidence: 99%

Machining error compensation based on a compliance error model reconstructed surface method

Chen

Yu²,

Tang³

et al. 2023

Preprint

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To reduce the errors caused by the overall compliance of the system and the machine tool wear during the machining process for curved workpieces, and to improve the machining accuracies of curved workpieces, a machining error compensation method using a compliance error model to reconstruct the surface was adopted. During a process involving multiple cuts, on-machine measurement (OMM) was achieved by replacing the cutting tool with a touch probe. After the last semi-finishing cut, the error values were measured and compensated for using a compliance error model that predicted the change in the overall compliance of the system and estimated the error at the finishing cut depth based on the measured machining error at the previous cut depth. Finally, the machining errors before and after compensation of formula and free-form surface workpieces were verified experimentally. For the formula surface workpiece, the maximum value of the machining error was reduced from 0.057 mm to 0.0163 mm, and the average absolute value of the machining error was reduced from 0.047 mm to 0.0032 mm after compensation when using the method based on a compliance error model reconstructed surface. For the free-form surface workpiece, the maximum value of the machining error was reduced from 0.0586 mm to 0.0166 mm, and the average absolute value of the machining error was reduced from 0.0483 mm to 0.0057 mm after compensation using the compliance error model reconstructed surface method. It was verified that this method could effectively reduce machining errors and improve the machining accuracy of machine tools. This method can be used to predict and compensate for machining errors at different cut depths, which is of great research significance for machining various types of curved workpieces.

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“…Yin et al studied the machining error prediction and compensation technology for a stone-carving robotic manipulator. The feasibility and effectiveness of the proposed compensation technology were verified by an experiment using the KUKA-240-2900 SCRM system [ 16 ]. Chen et al introduced a milling force model for robotic milling of cortical bone, and analyzed the influence of bone material anisotropy on the milling force.…”

Section: Introductionmentioning

confidence: 99%

Contribution Ratio Assessment of Process Parameters on Robotic Milling Performance

Dai

Yue

et al. 2022

Materials

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Robotic milling has broad application prospects in many processing fields. However, the milling performance of a robot in a certain posture, such as in face milling or grooving tasks, is extremely sensitive to process parameters due to the influence of the serial structure of the robot system. Improper process parameters are prone to produce machining defects such as low surface quality. These deficiencies substantially decrease the further application development of robotic milling. Therefore, this paper selected a certain posture and carried out the robotic flat-end milling experiments on a 7075-T651 high-strength aeronautical aluminum alloy under dry conditions. Milling load, surface quality and vibration were selected to assess the influence of process parameters like milling depth, spindle speed and feed rate on the milling performance. Most notably, the contribution ratio based on the analysis of variance (ANOVA) was introduced to statistically investigate the relation between parameters and milling performance. The obtained results show that milling depth is highly significant in milling load, which had a contribution ratio of 69.25%. Milling depth is also highly significant in vibration, which had a contribution ratio of 51.41% in the X direction, 41.42% in the Y direction and 75.97% in the Z direction. Moreover, the spindle speed is highly significant in surface roughness, which had a contribution ratio of 48.02%. This present study aims to quantitatively evaluate the influence of key process parameters on robotic milling performance, which helps to select reasonable milling parameters and improve the milling performance of the robot system. It is beneficial to give full play to the advantages of robots and present more possibilities of robot applications in machining and manufacturing.

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Research on machining error prediction and compensation technology for a stone-carving robotic manipulator

Cited by 19 publications

References 19 publications

A data-driven high-precision modeling method of machine tool spatial error under the influence of Abbe error

A data-driven high-precision modeling method of machine tool spatial error under the influence of Abbe error

Machining error compensation based on a compliance error model reconstructed surface method

Contribution Ratio Assessment of Process Parameters on Robotic Milling Performance

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