An Error Compensation Method for Rectangular Window Based on NURBS Reconstruction Theory

2018 ◽  
Vol 917 ◽  
pp. 284-288
Author(s):  
Dong Xia Li ◽  
Ai Min Wang ◽  
Peng Hao Ren
Keyword(s):  
Software Development ◽  
Error Compensation ◽  
Tool Path ◽  
Measured Data ◽  
Compensation Method ◽  
Adaptive Compensation ◽  
Machining Error ◽  
Rectangular Window ◽  
B Splines ◽  
Error Calculation

Aiming at the error compensation problem for rectangular window, this paper presents a method of compensation for rectangular window based on NURBS (Non-Uniform Rational B-Splines) reconstruction. In the method, the machining surface is digitally obtained by means of on-machine measurement. The measured data are divided into four regions and different error compensation schemes are used for different regions. The adaptive compensation of the machining error calculated based on NURBS reconstruction theory is achieved by modifying the coordinates of the tool point in the cutter location file. The automation of error calculation and compensation is implemented by software development based on Visual Studio 2012. At the end of the paper, a compensating tool path is emulated in VERICUT. The results show our method is feasible.

On-line first-order machining error compensation for thin-walled parts considering time-varying cutting condition

2021 ◽  
pp. 1-16
Author(s):  
Xiong Zhao ◽  
Lianyu Zheng ◽  
Yuehong Zhang
Keyword(s):  
Error Compensation ◽  
Compensation Method ◽  
Cutting Condition ◽  
Time Varying ◽  
Machining Error ◽  
Process Step ◽  
Compensation Model ◽  
First Order ◽  
On Line ◽  
Thin Walled

Abstract Mirror error compensation is usually employed to improve the machining precision of thin-walled parts. However, this zero-order method may result in inadequate error compensation, due to the time-varying cutting condition of thin-walled parts. To cope with this problem, an on-line first-order error compensation method is proposed for thin-walled parts. With this context, firstly, the time-varying cutting condition of thin-walled parts is defined with its in-process geometric and physical characteristics. Based on it, a first-order machining error compensation model is constructed. Then, during the process planning, the theory geometric and physical characteristic of thin-walled parts are respectively obtained with CAM software and structure dynamic modification method. After process performing, the real geometric characteristic of thin-walled parts is measured, and it is used to calculate the dimension error of thin-walled parts. Next, the error compensated value is evaluated based on the compensation model, from which, an error compensation plane is constructed to modify the tool center points for next process step. Finally, the machining error is compensated by performing the next process step. A milling test of thin-walled part is employed to verify the proposed method, and the experiment results shown that the proposed method can significantly improve the error compensation effect for low-stiffness structure, and thickness precision of thin-walled parts is improved by 71.4 % compared with the mirror error compensation method after machining.

scholarly journals A Novel Error Compensation Method of Five-axis Flank Milling of Ruled Surface by Modifying Tool Path

2021 ◽  
Author(s):  
Gaiyun He ◽  
Chenglin Yao ◽  
Yicun Sang ◽  
Yichen Yan
Keyword(s):  
Error Compensation ◽  
Tool Path ◽  
Error Distribution ◽  
Ruled Surface ◽  
Compensation Method ◽  
Distribution Model ◽  
Flank Milling ◽  
Average Error ◽  
Machining Accuracy ◽  
Five Axis

Abstract Five-axis flank milling is widely used in the aerospace and automotive industry. However, diverse sources of errors prevent the improvement of machining accuracy. This paper proposes a novel error compensation method for five-axis flank milling of ruled surface by modifying the original tool path according to the error distribution model. The method contains three steps: First, the errors at the middle of the straight generatrix on the machined surface are calculated according to error distribution, and the corresponding normal vectors are obtained by geometric calculation. Second, multi-peaks Gaussian fitting method is utilized to make connections between parameters in the original tool path and error distribution. Finally, the new tool path is generated by adjusting original tool path. Machining experiments are performed to test the effectiveness of the proposed error compensation method. The error distribution after compensation shows that the average error decreases 74%, and the maximum error (contains overcutting and undercutting) decreases 26%. Results show that the proposed error compensation method is effective to improve the accuracy for five-axis flank milling.

Integrated machining error compensation method using OMM data and modified PNN algorithm

2006 ◽  
Vol 46 (12-13) ◽  
pp. 1417-1427 ◽  
Author(s):  
Myeong-Woo Cho ◽  
Gun-Hee Kim ◽  
Tae-Il Seo ◽  
Yeon-Chan Hong ◽  
Harry H. Cheng
Keyword(s):  
Error Compensation ◽  
Compensation Method ◽  
Machining Error ◽  
Machining Error Compensation

scholarly journals Accurate Adaptive Compensation Method for Mechanical Structure Error of the Blade Measuring System

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Wei Shao ◽  
Peng Peng ◽  
Awei Zhou ◽  
Quanquan Zhu ◽  
Di Zhao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Laser Interferometer ◽  
Compensation Method ◽  
Measuring System ◽  
Adaptive Compensation ◽  
Mechanical Structure ◽  
Compensation Methods ◽  
Theoretical Significance

In view of the high precision requirement for mechanical structure of aeronautical blade measuring system, this paper proposes a laser interferometer to measure the error of the spatial nodes of the measuring system based on a comprehensive analysis of domestic and foreign error compensation methods for the measuring system. The optimized algorithm backpropagation (BP) neural network (OA-BPNN) compensation method is utilized to adaptively compensate for the systematic error of the mechanical system. Compared with the traditional polynomial fitting and genetic algorithm BP neural network (GA-BPNN) algorithm, the results show that the OA-BPNN algorithm is characterized by the best adaptability, precision, and efficiency for the adaptive error compensation. The spatial errors in the XYZ directions are reduced from 10.9, 60.1, and 84.2 μm to 1.3, 4.0, and 2.4 μm, respectively. The method is of great theoretical significance and practical value.

Self-Adaptive Compensation Method on the Tool Position of Roller Gear Cam Surface

2010 ◽  
Vol 154-155 ◽  
pp. 396-400
Author(s):  
Rong Yu Ge ◽  
Qing Song Wang ◽  
Xu Qiang Shang
Keyword(s):  
Rotation Angle ◽  
Optimization Method ◽  
Compensation Method ◽  
Adaptive Compensation ◽  
Machining Error ◽  
Flexible Compensation ◽  
Tool Position ◽  
Tool Diameter ◽  
Adaptive Tool ◽  
Self Adaptive

The roller gear cam surface is often machined by the unequal diameter manufacture method, which means the tool diameter is smaller than that of the roller and the tool position compensation method is most used. For tool position of roller gear cam, it is important to confirm the compensation vector for the tool position compensation method, including compensation value and direction. In the paper, a new self-adaptive tool position optimization method is proposed, which make minimizing the normal machining error as the object function and make two compensation factors as the optimization variables. This algorithm can find out the best compensation direction and value by the tool position optimization for any cam rotation angle and make the tool position self-adaptive and flexible compensation according to the machining error. A numerical calculation example shows that the optimization algorithm can feasibly reduce the machining error. At last a conclusion has been drawn that the radius difference between the tool and the roller is the best compensation value and the best compensation direction is not fixed.

A novel error compensation method for multistage machining processes based on differential motion vector sets of multiple contour points

2020 ◽  
pp. 1-23
Author(s):  
Mengrui Zhu ◽  
Guangyan Ge ◽  
Xiaobing Feng ◽  
Zhengchun Du ◽  
Jianguo Yang
Keyword(s):  
Error Compensation ◽  
Tool Path ◽  
Motion Vector ◽  
Zero Point ◽  
Compensation Method ◽  
Machining Processes ◽  
Machined Surface ◽  
Differential Motion ◽  
Variation Propagation ◽  
Workpiece Setup

Abstract Modeling the variation propagation based on the stream of variation (SoV) methodology for multistage machining processes (MMPs) has been investigated intensively in the past two decades, however little research is conducted on the variation reduction and the existing work fails to be applied to irregular features caused by the machining-induced variation varying with the positions of the contour points on the machined surface. This paper proposes a novel error compensation method for MMPs through modifying the tool path to reduce variation for general features. The method based on differential motion vector (DMV) sets of multiple contour points is presented to represent the deviation of the irregular feature. Then the conventional SoV model is further extended to more accurately describe variation propagation for irregular features considering the actual datum-induced variations and the varying machining-induced variations, especially the deformation errors for the low stiffness workpiece. Based on the extended SoV model and error equivalence mechanism, the datum error and fixture error are transformed to the equivalent tool path error. Then the original tool path is modified through shifting the machine zero point of machine tools with no need for changing the original G code and workpiece setup. A real cutting experiment validates the effectiveness of the proposed error compensation method for MMPs with an average precision improvement of over 60%. The application of the extended SoV model significantly contributes to compensating more complex error sources for MMPs, such as the clamp force, the internal residual stress, etc.

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