scholarly journals Multi-Stage Prediction of Feed System Time Series

2021 ◽  
Author(s):  
Lihua Shen ◽  
Biling Wang ◽  
Hongjun Liu
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Tracking Error ◽  
Cnc Machining ◽  
Numerical Control ◽  
Machining Accuracy ◽  
Feed Speed ◽  
Feed System ◽  
Multi Stage ◽  
Robust Prediction

In order to reduce the tracking error of the computer numerical control (CNC) feed system and improve the CNC machining accuracy, a novel prediction model is proposed based on fuzzy C-means robust variational echo state network. Firstly, the feed speed time series is clustered, and then reconstructed for different categories. The multi-stage robust prediction models are established to realize the multi-state robust prediction of the CNC machining feed velocity to reduce the tracking error of the feed system. Finally, the reference and actual time series with different feed speed are used to verify the established models. The results show that the proposed method can reduce the tracking error and realize the effective prediction of the time series of the feed system.

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%).

Synchronous Adjustment of Milling Tool Path Based on the Relative Deviation

2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Xiao-Jin Wan ◽  
Cai-Hua Xiong ◽  
Lin Hua
Keyword(s):  
Tool Path ◽  
Cutting Tool ◽  
Cnc Machining ◽  
Numerical Control ◽  
Machining Process ◽  
Machining Accuracy ◽  
Source File ◽  
Modern Computer ◽  
Adjustment Strategy ◽  
Machining System

In machining process, machining accuracy of part mainly depends on the position and orientation of the cutting tool with respect to the workpiece which is influenced by errors of machine tools and cutter-workpiece-fixture system. A systematic modeling method is presented to integrate the two types of error sources into the deviation of the cutting tool relative to the workpiece which determines the accuracy of the machining system. For the purpose of minimizing the machining error, an adjustment strategy of tool path is proposed on the basis of the generation principle of the cutter location source file (CLSF) in modern computer aided manufacturing (CAM) system by means of the prediction deviation, namely, the deviation of the cutting tool relative to the workpiece in computer numerical control (CNC) machining operation. The resulting errors are introduced as adjustment values to adjust the nominal tool path points from cutter location source file from commercial CAM system prior to machining. Finally, this paper demonstrates the effectiveness of the prediction model and the adjustment technique by two study cases.

scholarly journals Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1233 ◽  
Author(s):  
Chen ◽  
Xie ◽  
Yuan ◽  
Huang ◽  
Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Tool Wear ◽  
Real Time ◽  
Original Data ◽  
Attention Mechanism ◽  
Cnc Machining ◽  
Numerical Control ◽  
Real Time Monitoring ◽  
Monitoring Method

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

Research on the Velocity Look-Ahead Algorithm in the NC System

2011 ◽  
Vol 311-313 ◽  
pp. 2379-2384
Author(s):  
Jian Qun Liu ◽  
Dong Xu ◽  
Ji Rong Wu ◽  
Yang Xu ◽  
Xiao Li ◽  
...  
Keyword(s):  
High Speed ◽  
Linear Acceleration ◽  
Numerical Control ◽  
Machining Accuracy ◽  
Feed Speed ◽  
Direct Transfer ◽  
Optimal Velocity ◽  
Nc System ◽  
Look Ahead ◽  
The Look

Aiming at improving the interpolation efficiency without lowering the machining accuracy, the velocity look-ahead algorithm in the numerical control (NC) system is introduced. Firstly, the machining path’s transferring vector angle model is established, and then the conditions of direct transfer for the adjacent paths are also discussed. Secondly, based on the linear acceleration/deceleration control, the velocity look-ahead algorithm is put forward which can find the approximate-optimal velocity adaptively according to the maximum number of the look-ahead blocks and the geometric properties of the machining path. Finally, the acceleration/deceleration control in the consecutive micro-path can be realized, therefore, the high-speed and smooth transfer of feed-speed among machining path blocks can be achieved.

scholarly journals The Application of Intelligent Manufacturing Technology in CNC Tools Design and Machining

2021 ◽  
Vol 2143 (1) ◽  
pp. 012045
Author(s):  
Zhaohui Su
Keyword(s):  
Cutting Tools ◽  
Development Trend ◽  
Intelligent Manufacturing ◽  
Manufacturing Technology ◽  
Cnc Machining ◽  
Numerical Control ◽  
Quality Level ◽  
Machining Accuracy ◽  
Industrial Manufacturing ◽  
Manufacturing Intelligence

Abstract CNC tool is a kind of cutting tools in industrial manufacturing. With the improvement of CNC machining accuracy and quality level, it puts forward more strict requirements for the performance of the cutting tool. Its manufacturing intelligence has become the inevitable choice for the development of the industry. In this paper, the key manufacturing technology of numerical control tools and the application of intelligence in numerical control tool manufacturing are described. the development trend of intelligent manufacturing of numerical control tools is analyzed.

scholarly journals Impact of Sycamore Maple Thermal Treatment on a Granulometric Composition of Chips Obtained due to Processing on a CNC Machining Mentre

2019 ◽  
Vol 11 (3) ◽  
pp. 718 ◽  
Author(s):  
Richard Kminiak ◽  
Ladislav Dzurenda
Keyword(s):  
Thermal Treatment ◽  
Granulometric Composition ◽  
Negative Impact ◽  
Cnc Machining ◽  
Numerical Control ◽  
Three Dimensions ◽  
Dust Particles ◽  
Feed Speed ◽  
Mode Iii ◽  
Granulometric Analysis

This article deals with a granulometric composition of chips from the milling process of native and thermal treatment maple cuttings on a 5-axial Computer numerical control (CNC) machining center SCM Tech Z5. The aim of this article was to determine the changes in the granulometric composition of chips due to the thermal treatment of wood and to assess the potential risk of the creation of harmful dust fractions. Cuttings were milled with a shank cutter with exchangeable razor blades at feed speed vf = 1 ÷ 5 m·min−1 and material removal e = 3 mm. The thermal treatment in order to modify the color of the maple wood was done with water vapour at temperatures of tI = 112.5 ± 2.5 °C for a period of τ = 5.5 h (Mode I), tII = 127.5 ± 2.5 °C for a period of τ = 6.5 h (Mode II), and tIII = 137.5 ± 2.5 °C for a period of τ = 7.5 h (Mode III). The granulometric composition of the chips was detected by sifting. A granulometric analysis of the chips provided that more than 2/3 of the produced chips are a coarse fraction consisting of flat chips with dimensions over 1 mm. Dust fractions smaller than 500 μm form isometric grains, i.e., chips having approximately the same size in all three dimensions. Inhalable dust particles, smaller than 125 μm, do not exceed a 2.5% share. The granulometric analysis of chips shows that the thermal treatment of maple wood does not create respirable fractions, and therefore, the thermal treatment of the wood does not have a negative impact on the living and working environments.

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.

The Thermal Characteristics of the Ball Screw Feed System on a Gantry Machine Tool

2014 ◽  
Vol 490-491 ◽  
pp. 1008-1012
Author(s):  
Rui Jun Liang ◽  
Wen Hua Ye ◽  
Qun Qiang Chen ◽  
Xin Jie Zhao
Keyword(s):  
Temperature Field ◽  
Machine Tool ◽  
Thermal Equilibrium ◽  
Thermal Deformation ◽  
Thermal Structure ◽  
Ball Screw ◽  
Machining Accuracy ◽  
Feed Speed ◽  
Feed System ◽  
Gantry Machine Tool

With the increasing of machine tool feed speed, a large quantity of friction heat is generated on the ball screw system and will cause the temperature rising and thermal deformation along the ball screw that reduces the machining accuracy. The heat accumulated and dissipated are calculated to load to the established model of the Y feed system on a gantry machine tool. The stable temperature field at thermal equilibrium and the unstable temperature field before thermal equilibrium or with the variation of thermal load are gotten. From thermal structure analysis, the thermal deformation is derived. The FEM model is verified by the experiments carried out under the same condition with the simulation.

Adaptive spiral tool path generation for computer numerical control machining using point cloud

2021 ◽  
pp. 095440622199007
Author(s):  
Mandeep Dhanda ◽  
Aman Kukreja ◽  
SS Pande
Keyword(s):  
Tool Path ◽  
Cnc Machining ◽  
Numerical Control ◽  
Numerical Control Machining ◽  
Form Errors ◽  
Spiral Tool Path ◽  
Novel Method ◽  
Mesh Grid ◽  
Cloud Of Points ◽  
Cam Software

This paper reports a novel method to generate adaptive spiral tool path for the CNC machining of complex sculptured surface represented in the form of cloud of points without the need for surface fitting. The algorithm initially uses uniform 2 D circular mesh-grid to compute the cutter location (CL) points by applying the tool inverse offset method (IOM). These CL points are refined adaptively till the surface form errors converge below the prescribed tolerance limits in both circumferential and radial directions. They are further refined to eliminate the redundancy in machining and generate optimum region wise tool path to minimize the tool lifts. The NC part programs generated by our algorithm were widely tested for different case studies using the commercial CNC simulator as well as by the actual machining trial. Finally, a comparative study was done between our developed system and the commercial CAM software. The results showed that our system is more efficient and robust in terms of the obtained surface quality, productivity, and memory requirement.

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