Third-order contour-error estimation for arbitrary free-form paths in contour-following tasks

Song, De-Ning; Zhong, Yong; Ma, Jian-wei

doi:10.1016/j.precisioneng.2019.07.009

Cited by 18 publications

(11 citation statements)

References 18 publications

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“…According to the third-order contour error estimation method described in Ref. [24], the contour error of the free path can be expressed as…”

Section: Stability Analysismentioning

confidence: 99%

“…The integrated control strategy proposed in this paper only needs to use the position and velocity information of the interpolation point as well as the curvature and chord error information of the target trajectory. This information is available in multi-axis computer numerical control (CNC) platform, so that we can use the methods such as the high-precision three-axis contour error estimation algorithm [24] and the five-axis contour error estimation algorithm [25,26] to estimate the contour error of the multiaxis CNC platform and implement the contour error compensation of each axis. Through the proposed integrated control strategy, the error compensation can be distributed to each axis, so that the tracking and contour control accuracy of the multi-axis CNC platform can be significantly improved and the chord error requirements can be successfully met.…”

Section: Introductionmentioning

confidence: 99%

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Chord error constraint based integrated control strategy for contour error compensation

Zhang

Zou

2020

Front. Mech. Eng.

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As the traditional cross-coupling control method cannot meet the requirements for tracking accuracy and contour control accuracy in large curvature positions, an integrated control strategy of cross-coupling contour error compensation based on chord error constraint, which consists of a cross-coupling controller and an improved position error compensator, is proposed. To reduce the contour error, a PI-type cross-coupling controller is designed, with its stability being analyzed by using the contour error transfer function. Moreover, a feed rate regulator based on the chord error constraint is proposed, which performs speed planning with the maximum feed rate allowed by the large curvature position as the constraint condition, so as to meet the requirements of large curvature positions for the chord error. Besides, an improved position error compensation method is further presented by combining the feed rate regulator with the position error compensator, which improves the tracking accuracy via the advance compensation of tracking error. The biaxial experimental results of non-uniform rational Bsplines curves indicate that the proposed integrated control strategy can significantly improve the tracking and contour control accuracy in biaxial contour following tasks.

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“…According to the third-order contour error estimation method described in Ref. [24], the contour error of the free path can be expressed as…”

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chord error constraint based integrated control strategy for contour error compensation

Zhang

Zou

2020

Front. Mech. Eng.

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“…Mismatch of dynamic characteristics of each axis motor is the main cause of contour error [11]. is paper applies CCC to coordinate the motion of each axis.…”

Section: Contour Error Modelmentioning

confidence: 99%

Contour Tracking Control Based on Extended State Observer for Multiaxis Motion System

Wang

Zhou

Wang

2020

Mathematical Problems in Engineering

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In the contouring process, the trajectory generated by the computer numerical control (CNC) machine tool is a result of the multiaxis coordinated motion. The contour error has a direct impact on the accuracy of the machined product. To obtain higher contouring accuracy of the multiaxis motion control system, this paper presents a cross-coupled control approach based on the extended state observer sliding mode control. First, a single-axis sliding mode controller is designed, and an extended state observer is used to estimate system disturbances and improve the system robustness. Then, the cross-coupled control approach handles the coordinated motion of multiple axes to improve the contour control accuracy. Next, a simulation study is conducted on the three-axis motion platform. Its result shows that the control algorithm is effective in reducing tracking errors and contour errors.

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“…In Computer Numerical Control (CNC) machining, the contour error of the feeding system is one of the most fatal factors affecting the machining precision, and the lower the contour error is, the better machining precision it could be. The contour error results from both factors of the tracking error of the machine's feeding axes and the mismatch of multi-axis dynamic characteristics [1], especially at the location of the sharp corner [2][3] [4] of the tool path. To improve the machining accuracy, the contour error should be effectively predicted and compensated [5] [6].…”

Section: Introductionmentioning

confidence: 99%

“…The contour error results from both factors of the tracking error of the machine's feeding axes and the mismatch of multi-axis dynamic characteristics [1], especially at the location of the sharp corner [2][3] [4] of the tool path. To improve the machining accuracy, the contour error should be effectively predicted and compensated [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Contour error modeling and compensation of CNC machining based on deep learning and reinforcement learning

Jiang

Chen

Zhou

et al. 2021

Int J Adv Manuf Technol

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Contour error compensation of the Computer Numerical Control (CNC) machine tool is a vital technology that can improve machining accuracy and quality. To achieve this goal, the tracking error of a feeding axis, which is a dominant issue incurring the contour error, should be firstly modeled and then a proper compensation strategy should be determined. However, building the precise tracking error prediction model is a challenging task because of the nonlinear issues like backlash and friction involved in the feeding axis; besides, the optimal compensation parameter is also difficult to determine because it is sensitive to the machining tool path. In this paper, a set of novel approaches for contour error prediction and compensation is presented based on the technologies of deep learning and reinforcement learning. By utilizing the internal data of the CNC system, the tracking error of the feeding axis is modeled as a Nonlinear Auto-Regressive Long-Short Term Memory (NAR-LSTM) network, considering all the nonlinear issues of the feeding axis. Given the contour error as calculated based on the predicted tracking error of each feeding axis, a compensation strategy is presented with its parameters identified efficiently by a Time-Series Deep Q-Network (TS-DQN) as designed in our work. To validate the feasibility and advantage of the proposed approaches, extensive experiments are conducted, testifying that, our approaches can predict the tracking error and contour error with very good precision (better than about 99% and 90% respectively), and the contour error compensated based on the predicted results and our compensation strategy is significantly reduced (about 70%~85% reduction) with the machining quality improved drastically (machining error reduced about 50%).

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Third-order contour-error estimation for arbitrary free-form paths in contour-following tasks

Cited by 18 publications

References 18 publications

Chord error constraint based integrated control strategy for contour error compensation

Chord error constraint based integrated control strategy for contour error compensation

Contour Tracking Control Based on Extended State Observer for Multiaxis Motion System

Contour error modeling and compensation of CNC machining based on deep learning and reinforcement learning

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