Modeling and adaptive compensation of tooth surface contour error for internal gearing power honing

2018 ◽  
Vol 233 (5) ◽  
pp. 1500-1514 ◽  
Author(s):  
Jiang Han ◽  
Yonggang Zhu ◽  
Lian Xia ◽  
Xiaoqing Tian ◽  
Bin Yuan
Keyword(s):  
Machine Tools ◽  
Tracking Error ◽  
Cross Coupling ◽  
Error Modeling ◽  
Numerical Control ◽  
Tooth Surface ◽  
Contour Error ◽  
Adaptive Compensation ◽  
Surface Contour ◽  
Gear Machine

The machining of high precision gears requires a strict and accurate co-movement relationship controlled by the electronic gearbox between the moving axes of the gear machine tools. This article proposes a tooth surface contour error modeling method and an adaptive electronic gearbox cross-coupling controller for internal gearing power honing. First, the electronic gearbox model is structured according to the generative machining principle of internal gearing power honing and the tooth surface contour error is established by means of homogeneous coordinate transformation and meshing principle. Then, the adaptive electronic gearbox cross-coupling controller is designed, which comprises the electronic gearbox cross-coupling controller and the fuzzy proportional–integral–derivative controllers whose universes of membership functions in fuzzy rules are optimized by particle swarm optimization to improve the adaptability and robustness to disturbance fluctuation and model uncertainty of the system. Finally, experiments are carried out on a self-developed gear numerical control system. The results have demonstrated that the estimated tooth surface contour error using the proposed method is very close to the actual one, and the proposed adaptive electronic gearbox cross-coupling controller can effectively reduce the tracking error and the tooth surface contour error when compared to the electronic gearbox cross-coupling controller and non–electronic gearbox cross-coupling controller (electronic gearbox controller without cross-coupling and adaptive compensation).

Improving Contour Accuracy of Machine Tools Using an Integral-Gain Scheduler

2005 ◽  
Vol 219 (7) ◽  
pp. 511-518 ◽  
Author(s):  
R C Ko ◽  
M C Good
Keyword(s):  
Machine Tools ◽  
High Performance ◽  
Pulse Width Modulation ◽  
Tracking Error ◽  
Contour Error ◽  
Current Loop ◽  
Pwm Inverter ◽  
Current Controller ◽  
Reversal Error ◽  
Grinding Machine

In high-precision machine tools, contour error at axis reversal can significantly reduce the quality of products. Resulting from non-linear friction behaviour, the reversal error is traditionally handled by the velocity controller, which highly relies on a high-performance current servo. However, the widely employed pulse width modulation (PWM) inverter in the power stage of the current servo operates with a severe non-linearity known as deadband. The deadband effect degrades the current-loop tracking performance and consequently hinders the velocity controller in responding to friction disturbances. The result is a significant and oscillatory tracking error, or contour error in a multiaxis system. Unlike other approaches where the deadband is compensated via measurement or estimation, a control system approach is proposed in this paper where the deadband is treated as a voltage perturbation in the current loop. The proposed scheme incorporates a feedforward signal from the current command and schedules the integral action in the current controller accordingly. The proposed scheme was implemented in digital servo drives of a commercial grinding machine. Experiments show that the proposed scheme is an effective and practical solution for this type of problem.

Cross-Coupling Controller for Five Degree-of-Freedom Coplanar Nanostage

2013 ◽  
Vol 284-287 ◽  
pp. 1788-1793
Author(s):  
Van Tsai Liu
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Cross Coupling ◽  
Degree Of Freedom ◽  
Creep Model ◽  
Contour Error ◽  
Viscoelastic Creep ◽  
Control Scheme ◽  
Optimization Parameters ◽  
Cross Coupling Control

The proposed approach is to design a tracking controller for five degree-of-freedom coplanar nanostage which can provide high precision applications. This study propose a viscoelastic creep model, it was modeled as a series connection of springs and dampers to describe the creep effect. Then, utilize a PI controller using Taguchi method to search the optimization parameters to suppress the tracking error. Finally, a cross-coupling control scheme is proposed to eliminate the contour error which is typical in dual-axes tracking control problem. The developed approaches are numerically and experimentally verified which demonstrate performance and applicability.

scholarly journals A Novel Position Domain Controller For Contour Tracking Performance Improvement

2021 ◽  
Author(s):  
Truong Dam
Keyword(s):  
Time Domain ◽  
Phase Error ◽  
Common Factor ◽  
Tracking Error ◽  
Numerical Control ◽  
Tracking Performance ◽  
Contour Error ◽  
Contour Tracking ◽  
Cnc Machine ◽  
Equivalent Time

A common problem with modern manufacturing processes that utilize high feed-rate machining is how to accurately track a given contour for the tool center point (TCP) of a system. Various methods have been developed to increase axial tracking performance and contouring performance of computerized numerical control (CNC) machines. These include: high gain feedback controllers, feedforward controllers, zero phase error tracking controllers (ZPETC), cross-coupled control (CCC), and iterative learning control to mention a few. The common factor amongst these methods is that they are all based in time domain. This thesis will propose a new control law based in position domain applied to contour tracking control of a CNC machine. The goal of this developed controller is to improve the overall tracking and contouring performance of a CNC system. The idea behind a position domain control involves transforming the dynamics of a system from time domain into position domain through a one-to-one mapping. In the position domain system control, the motion of one of the axis is used as an independent reference by sampling equidistantly to control the remaining axes according to the contouring requirements. The overall contour error in a position domain controller should be lower relative to an equivalent time domain controller since there will be a zero tracking error from the reference motion. The stability of the proposed position domain control is proven through the Lyapunov method. Simulations with linear and nonlinear TCP contours using the proposed position domain controller and an equivalent time domain controller indicate that the proposed position domain control can improve tracking and contouring performance. In addition, a position domain controller with cross-coupled control was also proposed to further improve contour performance.

MPCC-Based Set Point Optimisation for Machine Tools

2019 ◽  
Vol 13 (3) ◽  
pp. 407-418
Author(s):  
Titus Haas ◽  
Sascha Weikert ◽  
Konrad Wegener ◽  
◽  
Keyword(s):  
Machine Tools ◽  
State Of The Art ◽  
Linear Constraints ◽  
Numerical Control ◽  
Contour Error ◽  
Set Point ◽  
Dynamic Constraints ◽  
Control Code ◽  
Time Optimal ◽  
Optimal Set

Numerical control code is typically used for manufacturing a workpiece using machine tools. Most state-of-the-art approaches decouple the set point optimisation into two steps: the geometry and the feed rate optimisation that does not necessarily result in time-optimal set points for the desired geometry. Given the originally programmed geometry through the numerical control code, dynamic constraints of the machine tool, and maximum permissible contour error for the optimisation, a model predictive contouring control based set point optimisation approach is developed to generate time-optimal set points for machine tools globally. A suitable error definition and its linearisation are used whereby the optimisation problem can be represented by a quadratic programming problem with linear constraints. Compared to most state-of-the-art methods, a direct approach is presented and no previous geometry optimisation step is required. Depending on the demands of accuracy, different maximum contour error constraints and penalisation as well as various maximum permissible axis velocities and accelerations are presented and tested on a test bench. The method is shown to be adaptable to different demands on the set points, and the contour errors can be affected by either the constraints or penalising factors.

Tracking Error Transform in Parallel Kinematic Machining

2010 ◽  
Vol 450 ◽  
pp. 585-588
Author(s):  
Ghasem Amirian ◽  
Christian Schenck ◽  
Bernd Kuhfuss
Keyword(s):  
Machine Tools ◽  
High Speed ◽  
Tracking Error ◽  
Contour Error ◽  
Machining Accuracy ◽  
Tracking Errors ◽  
High Productivity ◽  
Machine Errors ◽  
Parallel Kinematic ◽  
Servo Tracking

Parallel kinematic machine tools (PKM) are developed to increase dynamic parameters for high speed and high accuracy machining to gain short lead times and high productivity. One of the most important components of machine tools is the numerical controller (NC). Most NCs are organized in a cascaded structure, comprising the position, velocity and current loops. Commonly applied servo controllers generate tracking errors in each axis. These tracking errors are a significant factor that affects machining accuracy, beside geometric machine errors, vibrations, temperature changes and tool errors. In this paper the contour error originated from servo tracking controller in Cartesian kinematic machine tools (CKM) with perpendicular arranged machine axes and PKM is presented. The effects of the forward transformation of the tracking errors in PKM are addressed with experiment and simulation results. The servo tracking effect on trace accuracy is discussed by a tripod mechanism and radial deviations are measured with double ball bar method (DBB).

Research on Geometric Error Compensating Technique of CNC P3G Grinding Machine

2012 ◽  
Vol 462 ◽  
pp. 287-294 ◽  
Author(s):  
Yi Jian ◽  
Qian Qian Li ◽  
Hong Cheng ◽  
Bin Wu Lai ◽  
Jian Fei Zhang
Keyword(s):  
Machine Tools ◽  
Geometric Error ◽  
Error Model ◽  
Error Modeling ◽  
Numerical Control ◽  
Geometric Errors ◽  
Real Time Control ◽  
Time Control ◽  
Applied Software ◽  
Grinding Machine

Kinematic accuracy is a key reason which influence workpiece's geometric error precision on traditional working process of precisely CNC（Computerized Numerical Control）P3G（polygon profile with 3 lobes） grinding machine. A systematic geometric error model has been presented for CNC P3G grinding machine， proposed multi-body system theory integrate with the structure of CNC P3G grinding machine tools, researched on the machine's space geometric errors. By means of separate geometric errors from the machine tools, build geometric mathematical error model. Then, identify 21 error parameters through method of 9 lines, analysis and calculate the total space geometric errors of the workpiece and wheel. Finally, formed a parameter-list and applied software error compensational technique ， achieved real-time control to the motions of workpiece and wheel. Experimental results shown that the geometrical error modeling technique is accurate and efficient, and the precision of CNC P3G grinding machine is highly raised 70%.

scholarly journals Thermal error modeling based on BiLSTM deep learning for CNC machine tool

2021 ◽  
Author(s):  
Pu-Ling Liu ◽  
Zheng-Chun Du ◽  
Hui-Min Li ◽  
Ming Deng ◽  
Xiao-Bing Feng ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Numerical Control ◽  
Machining Accuracy ◽  
Depth Variation ◽  
Thermal Error Modeling ◽  
Proposed Model

AbstractThe machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedforward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 µm to less than 2 µm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

scholarly journals Contour Error Modeling and Compensation of CNC Machining Based on Deep Learning and Reinforcement Learning

2021 ◽  
Author(s):  
Yakun Jiang ◽  
Jihong Chen ◽  
Huicheng Zhou ◽  
Jianzhong Yang ◽  
Pengcheng Hu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Tracking Error ◽  
Cnc Machining ◽  
Numerical Control ◽  
Contour Error ◽  
Error Prediction ◽  
Machining Accuracy ◽  
Good Precision ◽  
Compensation Strategy

Abstract 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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