The Improvement of the Machining Accuracy by Factor Analysis Approach

2012 ◽  
Vol 201-202 ◽  
pp. 333-336
Author(s):  
Zheng Hua Huang ◽  
Cheng Rong Jiang
Keyword(s):  
Factor Analysis ◽  
Machining Process ◽  
Analysis Approach ◽  
Machining Accuracy ◽  
Analysis Method ◽  
Machining Error ◽  
Error Sources ◽  
Machining Errors ◽  
Process System ◽  
Statistical Analysis Method

In machining, a complete machining process system consists of the machine tool, the fixture, the tool and the part together, the various errors are also inevitable. The factor analysis approach and the statistical analysis method were put forward to study the machining accuracy on the basis of the error sources analysis of the machining accuracy, and the measures were introduced to improve the machining accuracy by using the factor analysis approach. The change laws are grasped by the analysis of the machining errors, so as to take the appropriate measure to reduce the machining error and improve the machining accuracy.

A Physical Machining Accuracy Predicting Model in Turning

2007 ◽  
Vol 329 ◽  
pp. 675-680 ◽  
Author(s):  
Sheng Fang Zhang ◽  
Zhi Hua Sha ◽  
Ren Ke Kang
Keyword(s):  
Manufacturing Process ◽  
Physical State ◽  
Cutting Parameters ◽  
Machining Process ◽  
Work Piece ◽  
Machining Accuracy ◽  
Predicting Model ◽  
Machining Precision ◽  
Machining Errors ◽  
Process System

During the Machining process of a part, along with the generation of new surfaces, various machining errors are produced. These machining errors depend on the characteristic of the manufacturing process system, as there are so many undetermined factors in the process system, it is very difficult to determine the machining accuracy of the workpiece. To the operator, the final accuracy of the part is very ambiguous, he can only consider the shape of the workpiece, and machining accuracy always be controlled by selecting different sets of cutting parameters. So the machining process is always time-consuming and costly. Therefore, it is very necessary to establish the accuracy predicting model to the workpiece. In this paper, According to the characteristic of turning, tool nose is abstracted into a “tangential point”, “three instantaneous centers” method is presented to get the reality shape of the workpiece. Using this method, and with the demarcating the errors in process system, the workpiece shaping model including multi-error is established. The model can not only describe the physical state of the workpiece, but also calculate the machining accuracy of the workpiece conveniently. In this paper, ‘three instantaneous centers’ method is developed to get a workpiece reality shape in turning. Using this method, the workpiece shaping model including multi-error is established. The model can not only describe the physical state of the work-piece, but also calculate the machining precision of the work-piece online.

Experimental study on cutting factors of multi-layer machining for large aerospace thin-walled structural parts

2021 ◽  
pp. 095440892110403
Author(s):  
Hangzhuo Yu ◽  
Han Zhong ◽  
Yong Chen ◽  
Lei Lin ◽  
Jing Shi ◽  
...  
Keyword(s):  
Coordinate System ◽  
Experimental Method ◽  
Cutting Parameters ◽  
Machining Process ◽  
Machining Error ◽  
Machining Errors ◽  
Inner Surface ◽  
Thin Walled ◽  
Optimal Cutting Parameters ◽  
Cutting Path

Large aerospace thin-walled structures will produce deformation and vibration in the machining process, which will cause machining error. In this paper, a cutting experimental method based on multi-layer machining is proposed to analyze the influence of cutting tool, cutting path, and cutting parameters on machining error in order to obtain the optimal cutting variables. Firstly, aiming at the situation that the inner surface of the workpiece deviates from the design basis, the laser scanning method is used to obtain the actual shape of the inner surface, and the method of feature alignment is designed to realize the unification of the measurement coordinate system and machining coordinate system. Secondly, a series of cutting experiments are used to obtain the machining errors of wall thickness under different cutting tools, cutting paths, and cutting parameters, and the variation of machining errors is analyzed. Thirdly, a machining error prediction model is established to realize the prediction of machining error, and the multi-objective optimization method is used to optimize the cutting parameters. Finally, a machining test was carried out to validate the proposed cutting experimental method and the optimal cutting parameters.

scholarly journals Evaluation of the Software Industry Competitiveness in Jilin Province Based on Factor Analysis

2015 ◽  
Vol 14 (4) ◽  
pp. 101-108
Author(s):  
Pinchao Meng ◽  
Weishi Yin ◽  
Yanzhong Li
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Evaluation System ◽  
Software Industry ◽  
Evaluation Model ◽  
Jilin Province ◽  
Analysis Method ◽  
Statistical Analysis Method ◽  
Set Up ◽  
Economic Indices

Abstract In this paper 12 economic indices of the software industry in 30 cities/provinces in China are used to set up an evaluation system for the competitiveness of the regional software industry. By using the statistical analysis method of factor analysis, an evaluation model of the comprehensive competitiveness of the software industry for each city/province is built. Taking Beijing and Shanghai as examples, the comprehensive competitiveness and problems of the software industry in Jilin province are compared and analyzed.

A Data-Driven Machining Error Analysis Method for Finish Machining of Assembly Interfaces of Large-Scale Components

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Wei Fan ◽  
Lianyu Zheng ◽  
Wei Ji ◽  
Xun Xu ◽  
Lihui Wang ◽  
...  
Keyword(s):  
Error Analysis ◽  
Large Scale ◽  
Tool Path ◽  
Cutting Parameters ◽  
Pso Algorithm ◽  
Data Driven ◽  
Machining Accuracy ◽  
Machining Error ◽  
Spatial Statistical Analysis ◽  
Machining Errors

Abstract To guarantee the final assembly quality of the large-scale components, the assembly interfaces of large components need to be finish-machined on site. Such assembly interfaces are often in low-stiffness structure and made of difficult-to-cut materials, which makes it hard to fulfill machining tolerance. To solve this issue, a data-driven adaptive machining error analysis and compensation method is proposed based on on-machine measurement. Within this context, an initial definite plane is fitted via an improved robust iterating least-squares plane-fitting method based on the spatial statistical analysis result of machining errors of the key measurement points. Then, the parameters of the definite plane are solved by a simulated annealing-particle swarm optimization (SA-PSO) algorithm to determine the optimal definite plane; it effectively decomposes the machining error into systematic error and process error. To reduce these errors, compensation methods, tool-path adjustment method, and an optimized group of cutting parameters are proposed. The proposed method is validated by a set of cutting tests of an assembly interface of a large-scale aircraft vertical tail. The results indicate that the machining errors are successfully separated, and each type of error has been reduced by the proposed method. A 0.017 mm machining accuracy of the wall-thickness of the assembly interface has been achieved, well fulfilling the requirement of 0.05 mm tolerance.

scholarly journals A New Error Prediction Method for Machining Process Based on a Combined Model

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wei Zhou ◽  
Xiao Zhu ◽  
Jun Wang ◽  
Yan Ran
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Machining Process ◽  
Error Prediction ◽  
Combined Model ◽  
Machining Error ◽  
Advantages And Disadvantages ◽  
Model Combining ◽  
Machining Errors ◽  
The Individual

Machining process is characterized by randomness, nonlinearity, and uncertainty, leading to the dynamic changes of machine tool machining errors. In this paper, a novel model combining the data processing merits of metabolic grey model (MGM) with that of nonlinear autoregressive (NAR) neural network is proposed for machining error prediction. The advantages and disadvantages of MGM and NAR neural network are introduced in detail, respectively. The combined model first utilizes MGM to predict the original error data and then uses NAR neural network to forecast the residual series of MGM. An experiment on the spindle machining is carried out, and a series of experimental data is used to validate the prediction performance of the combined model. The comparison of the experiment results indicates that combined model performs better than the individual model. The two-stage prediction of the combined model is characterized by high accuracy, fast speed, and robustness and can be applied to other complex machining error predictions.

Modeling and Analysis Effects of Material Removal on Machining Dynamics in Milling of Thin-Walled Workpiece

2011 ◽  
Vol 223 ◽  
pp. 671-678 ◽  
Author(s):  
Ming Luo ◽  
Ding Hua Zhang ◽  
Bao Hai Wu ◽  
Ming Tang
Keyword(s):  
Material Removal ◽  
Dynamic Properties ◽  
Removal Rate ◽  
Milling Process ◽  
Machining Process ◽  
Machining Accuracy ◽  
Modeling And Analysis ◽  
Process System ◽  
Material Choice ◽  
Thin Walled

In aerospace industry, thin-walled workpieces are widely used in order to reduce the weight and to fulfill the high demands of their later applications. These workpieces are usually highly sophisticated and difficult to machine according to their geometry and material choice. In this paper, influence of material removal within the thin-walled workpiece machining operation on the dynamic properties of the workpiece and the machining process system is discussed. Aiming at learning about dynamic properties evolution during the machining operation, different milling processes of thin-walled plate are studied. Numerical simulation methods are employed in the study to investigate the dynamic properties evolution and machining stability with the material removal process in the milling process of thin-walled workpiece. The investigation results are expected to be used for designing optimized material removal sequence, which will guarantee highly material removal rate as well as highly machining accuracy of thin-walled workpiece.

Factor Analysis and Compensation Method on NC Machining Error

2012 ◽  
Vol 426 ◽  
pp. 231-234
Author(s):  
Chun Ying Huang ◽  
Xiao Guang Fu
Keyword(s):  
Factor Analysis ◽  
Cutting Tool ◽  
Nc Machining ◽  
Transmission System ◽  
Error Source ◽  
Machining Error ◽  
Positioning Accuracy ◽  
Machining Errors ◽  
Tool Setting ◽  
Radius Compensation

It is analyzed the error source on NC machining and the error caused by tool setting,tools-tip arc radius compensation and processing route.It is proposed to use the optical apparatus for tool setting, as far as possible to reduce the workpiece machining errors caused by the different high between cutting tool tip point and axis center. It is adoptted one-way slowly feeding and the clearance error of transmission system is eliminated, the positioning accuracy is ensured.

Analysis of Machining Error in Numerical Control Milling

2013 ◽  
Vol 312 ◽  
pp. 710-713
Author(s):  
Jing Jun Cui
Keyword(s):  
Thermal Deformation ◽  
Numerical Control ◽  
Machining Accuracy ◽  
Machining Error ◽  
Important Indicator ◽  
Multiple Factors ◽  
Machining Errors ◽  
Cutter Wear ◽  
Finished Surface ◽  
Experimental Study Design

Generally speaking, the error in machining is an important indicator measuring the accuracy of finished surface. The machining error often occurs in numerical control milling. Such error will be influenced by multiple factors, such as cutter wear, thermal deformation, machine tool deformation, vibration or positioning error. Nowadays, though our science and technology develops rapidly, machining error problem in numerical control milling occurs frequently. At present, several methods can be applied to forecast machining error problems in numerical control milling, including on the basis of machining theory, experimental study, design study and artificial intelligence. The analysis and forecast of machining error problems in numerical control milling can to some extent improve the degree of machining errors so as to promote the machining accuracy in milling. The author expresses the views on machining error problems according to current situations of numerical control milling.

scholarly journals Effects of Machining Errors on Optical Performance of Optical Aspheric Components in Ultra-Precision Diamond Turning

Micromachines ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 331
Author(s):  
Yingchun Li ◽  
Yaoyao Zhang ◽  
Jieqiong Lin ◽  
Allen Yi ◽  
Xiaoqin Zhou
Keyword(s):  
Diamond Turning ◽  
Machining Accuracy ◽  
Aspheric Surface ◽  
Optical Performance ◽  
Machining Error ◽  
Optical Components ◽  
Optical Lens ◽  
Machining Errors ◽  
Ultra Precision ◽  
Multi Body

Optical aspheric components are inevitably affected by various disturbances during their precision machining, which reduces the actual machining accuracy and affects the optical performance of components. In this paper, based on the theory of multi-body system, we established a machining error model for optical aspheric surface machined by fast tool servo turning and analyzed the effect of the geometric errors on the machining accuracy of optical aspheric surface. We used the method of ray tracing to analyze the effect of the surface form distortion caused by the machining error on the optical performance, and identified the main machining errors according to the optical performance. Finally, the aspheric surface was successfully applied to the design of optical lens components for an aerial camera. Our research has a certain guiding significance for the identification and compensation of machining errors of optical components.

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