Neural Network Contour Error Predictor in CNC Control Systems

Paper presented as poster presentation at MMAR 2016 conference (Międzyzdroje,Poland, 29 Aug.-1 Sept. 2016)This article presents a method for predicting contour error using artificial neural networks. Contour error is defined as the minimum distance between actual position and reference toolpath and is commonly used to measure machining precision of Computerized Numerically Controlled (CNC) machine tools. Offline trained Nonlinear Autoregressive networks with exogenous inputs (NARX) are used to predict following error in each axis. These values and information about toolpath geometry obtained from the interpolator are then used to compute the contour error. The method used for effective off-line training of the dynamic recurrent NARX neural networks is presented. Tests are performed that verify the contour error prediction accuracy using a biaxial CNC machine in a real-time CNC control system. The presented neural network based contour error predictor was used in a predictive feedrate optimization algorithm with constrained contour error

Contour Error Map Algorithm

The contour error map (CEM) algorithm and the software that implements the algorithm are means of quantifying correlations between sets of time-varying data that are binarized and registered on spatial grids. The present version of the software is intended for use in evaluating numerical weather forecasts against observational sea-breeze data. In cases in which observational data come from off-grid stations, it is necessary to preprocess the observational data to transform them into gridded data. First, the wind direction is gridded and binarized so that D(i,j;n) is the input to CEM based on forecast data and d(i,j;n) is the input to CEM based on gridded observational data. Here, i and j are spatial indices representing 1.25-km intervals along the west-to-east and south-to-north directions, respectively; and n is a time index representing 5-minute intervals. A binary value of D or d = 0 corresponds to an offshore wind, whereas a value of D or d = 1 corresponds to an onshore wind. CEM includes two notable subalgorithms: One identifies and verifies sea-breeze boundaries; the other, which can be invoked optionally, performs an image-erosion function for the purpose of attempting to eliminate river-breeze contributions in the wind fields

Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories

Neural network contour error prediction of a bi-axial linear motor positioning system

In the article a method of predicting contour error using artificial neural network for a bi-axial positioning system is presented. The machine consists of two linear stages with permanent magnet linear motors controlled by servo drives. The drives are controlled from a PC with real-time operating system via EtherCAT fieldbus. A randomly generated Non-Uniform Rational B-Spline (NURBS) trajectory is used to train offline a NARX-type artificial neural network for each axis. These networks allow prediction of following errors and contour errors of the motion trajectory. Experimental results are presented that validate the viability of the neural network based contour error prediction. The presented contour error predictor will be used in predictive control and velocity optimization algorithms of linear motor based CNC machines

Hierarchical Optimal Force-Position-Contour Control of Machining Processes. Part I. Controller Methodology

There has been a tremendous amount of research in machine tool servomechanism control, contour control, and machining force control; however, to date these technologies have not been tightly integrated. This paper develops a hierarchical optimal control methodology for the simultaneous regulation of servomechanism positions, contour error, and machining forces. The contour error and machining force process reside in the top level of the hierarchy where the goals are to 1) drive the contour error to zero to maximize quality and 2) maintain a constant cutting force to maximize productivity. These goals are systematically propagated to the bottom level, via aggregation relationships between the top and bottom-level states, and combined with the bottom-level goals of tracking reference servomechanism positions. A single controller is designed at the bottom level, where the physical control signals reside, that simultaneously meets both the top and bottom-level goals. The hierarchical optimal control methodology is extended to account for variations in force process model parameters and process parameters

Contour error compensation based on feed rate adjustment

To improve the performance of computer numerical control (CNC) machining, especially for large-curvature trajectories, this paper presents a contour error compensation algorithm based on reference trajectory modification. In order to estimate the contour error accurately and efficiently, a contour error estimation model is established. The reference trajectory is modified on the basis of the estimated contour error and partitioned into different segments, which adopt different feed rates according to a corner detection algorithm. The effectiveness of this contour error compensation algorithm is verified by experiments on a CNC machine tool

Hierarchical Optimal Force-Position-Contour Control of Machining Processes. Part II. Illustrative Example

There has been a tremendous amount of research in machine tool servomechanism control, contour control, and machining force control; however, to date these technologies have not been tightly integrated. This paper develops a hierarchical optimal control methodology for the simultaneous regulation of servomechanism positions, contour error, and machining forces. The contour error and machining force process reside in the top level of the hierarchy where the goals are to 1) drive the contour error to zero to maximize quality and 2) maintain a constant cutting force to maximize productivity. These goals are systematically propagated to the bottom level, via aggregation relationships between the top and bottom-level states, and combined with the bottom-level goals of tracking reference servomechanism positions. A single controller is designed at the bottom level, where the physical control signals reside, that simultaneously meets both the top and bottom-level goals. The hierarchical optimal control methodology is extended to account for variations in force process model parameters and process parameters. Simulations are conducted for four machining operations that validate the developed methodology. The results illustrate that the controller can simultaneously achieve both the top and bottom-level goals

Analisa Metode Cross-Coupling Generalized Predictive Control untuk Mengurangi Countour Error pada Mesin CNC

Computerized Numerical Control (CNC) semakin banyak diminati di bidang industri manufaktur [1]. Pada umumnya, CNC dibutuhkan untuk dapat menghasilkan pergerakan yang sinkron diantara tiap sumbu geraknya untuk mengikuti suatu lintasan yang telah ditentukan. Tantangan utamanya adalah bagaimana cara untuk mengeliminasi contour error daripada menghilangkan error pada masing-masing sumbu [2]. Penelitian ini mengajukan desain cross-coupling dari Generalized Predictive Control untuk mesin CNC dengan sistem servo dua sumbu gerak. Untuk mendapatkan sinkronisasi dan respon tracking yang baik, metode Cross-Coupled Control, yang cukup banyak digunakan untuk mengurangi contour error[5], dikombinasikan dengan metode Generalized Predictive Control. Metode Cross-Coupling Generalized Predictive Control (CC-GPC) akan dibandingkan dengan metode modified Cross-Coupled Control (M-CCC) [3] untuk menunjukkan metode kontrol mana yang menghasilkan perfoma paling memuaskan untuk mengurangi contour error. Simulasi menunjukkan keuntungan dari metode CC-GPC adalah dapat meningkatkan respon dari sistem motor servo AC. Rise time (t_r) menurun menjadi 1.6 detik dan error steady state (e_ss) berkurang menjadi 0.49. Keuntungan lain adalah koreksi dari tiap sumbu terjadi secara bersamaan, sehingga sehingga sistem memiliki kemampuan yang baik untuk menahan gangguan. Dari pengujian gangguan, hasil menunjukkan Root Mean Square Error (RMSE) dari metode CC-GPC adalah 0.3806. Cukup baik dibanding RMSE metode M-CCC yang sebesar 1.0478

Prediction and compensation of contour error of CNC systems based on LSTM neural-network

This paper proposes a contour error estimation and compensation method for computer numerical control (CNC) systems based on the long short-term memory neural network (LSTM-NN). This is achieved by performing modeling of each axis to predict the tracking error, calculating the actual trajectory, estimating the contour error, and modifying the reference trajectory. First, linear feature selection based on a simplified single-axis model and nonlinear feature selection based on a circular test are performed to achieve tracking error prediction. Then, a spline-approximation-based contour error estimation method is proposed to estimate the contour error between the reference trajectory and the predicted trajectory. Finally, contour error compensation is performed on the reference trajectory before it is run on CNC systems. The proposed method is validated through experiments on a three-axis CNC system