The Experts below are selected from a list of 15300 Experts worldwide ranked by ideXlab platform
Qiang Cheng - One of the best experts on this subject based on the ideXlab platform.
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a Machining Accuracy improvement approach for a horizontal Machining center based on analysis of geometric error characteristics
The International Journal of Advanced Manufacturing Technology, 2021Co-Authors: Peng Niu, Qiang Cheng, Zhifeng Liu, Hongyan ChuAbstract:In the actual production, it is expected to find several sensitivity geometric errors which have great influence on Machining Accuracy, so as to provide references for the manufacturing, assembly, and other links of the machine tool, and fundamentally improve the working performance. To study this problem, a novel global sensitivity analysis (GSA) method is proposed. Based on the MBS theory, the spatial error model is established to analyze the local influence of geometric error on Machining Accuracy. An improved second-order partial correlation coefficient based on the Pearson product moment is proposed to analyze the correlation between geometric errors. In addition, the error parameters will change greatly in the stroke of the moving parts. However, the rapid fluctuation of geometric error value will have a dynamic impact on the Machining Accuracy of machine tools, which is rarely noticed in the previous sensitivity analysis. This change is defined as the fluctuation of error. The high fitting degree function of error-displacement is obtained by using the high order Fourier series and sine series. Then, the fluctuation of the error in the Machining stroke is analyzed by using the derivative of the function to the displacement. Through geometric error characteristics (including the local influence, correlation, and fluctuation) are studied comprehensively, the GSA of error is carried out. Finally, taking a Machining center as an example and combining the sensitivity analysis results, the improvement measures are proposed to verify the correctness of the method.
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Machining Accuracy reliability during the peripheral milling process of thin walled components
Robotics and Computer-integrated Manufacturing, 2019Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Zhiqiang Tao, Ligang CaiAbstract:Abstracts During the peripheral milling process of thin-walled components, a workpiece with poor rigidity will cause workpiece deformation error because of the milling force and result in the degradation of Machining Accuracy and related Machining ability of machine tools. As a result, how to obtain the workpiece deformation error and its effect on the surface Machining quality of workpiece is the focus of the research. Hence, a synthesis approach was developed in this study to analyze the Machining Accuracy reliability during the peripheral milling process of thin-walled components. The feedback mechanism between the milling force and milling deformation error was studied, and then a workpiece deformation error model based on an advanced neural fuzzy network was developed. By applying the D-H method, a Machining Accuracy model of the machine tool was established for the machine tool considering the workpiece deformation. Based on the reliability analysis method combined with R-F and Edge worth, a Machining Accuracy reliability model was developed, and then the reliability of Machining Accuracy during the peripheral milling process of thin-walled components was obtained. To verify this approach, a Machining experiment was conducted on a three-axis machine tool; the experimental results indicate that better predictive ability was achieved using the approach presented in the paper.
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a geometric error budget method to improve Machining Accuracy reliability of multi axis machine tools
Journal of Intelligent Manufacturing, 2019Co-Authors: Ziling Zhang, Ligang Cai, Qiang Cheng, Zhifeng LiuAbstract:Machining Accuracy reliability is considered to be one of the most important indexes in the process of performance evaluation and optimization design of the machine tools. Geometric errors, thermal errors and tool wear are the main factors to affect the Machining Accuracy and so affect the Machining Accuracy reliability of machine tools. This paper proposed a geometric error budget method that simultaneously considers geometric errors, thermal errors and tool wear to improve the Machining Accuracy reliability of machine tools. Homogeneous transformation matrices, neural fuzzy control theory and a tool wear predictive approach were employed to develop a comprehensive error model, which shows the influence of the geometric, thermal errors and tool wear to the Machining Accuracy of a machine tool. Based on Rackwite---Fiessler and Advanced First Order and Second Moment, a reliability model and a sensitivity model were put forward, which can deal with the errors of a machine tool drawn from any distribution. Then, a geometric error budget method of multi-axis NC machine tool was developed and formed into a mathematical model. In such method, the minimum cost of machine tool was the optimization objective, the reliability of the Machining Accuracy was the constraint, and the sensitivity was to identify the geometric errors to be optimized. An example conducted on a five-axis NC machine tool was used to explain and validate the proposed method.
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geometric error compensation method based on floyd algorithm and product of exponential screw theory
Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2018Co-Authors: Qiang Cheng, Zhifeng Liu, Bingwei Sun, Qiunan FengAbstract:Error compensation technique is a recognized and cost-effective method to improve Machining Accuracy of machine tools. In this article, a new compensation method for geometric error is proposed bas...
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Machining Accuracy reliability analysis of multi axis machine tool based on monte carlo simulation
Journal of Intelligent Manufacturing, 2018Co-Authors: Yongsheng Zhao, Qiang Cheng, Hongwei Zhao, Bingwei SunAbstract:Although machine tool can meet the specifications while it is new, after a long period of cutting operations, the abrasion of contact surfaces and deformation of structures will degrade the Accuracy of machine tool due to the increase of the geometric errors in six freedoms. Therefore, how to maintain its Accuracy for quality control of products is of crucial importance to machine tool. In this paper, Machining Accuracy reliability is defined as the ability to perform its specified Machining Accuracy under the stated conditions for a given period of time, and a new method to analyze the sensitivity of geometric errors to the Machining Accuracy reliability is proposed. By applying Multi-body system theory, a comprehensive volumetric model explains how individual geometric errors affect the Machining Accuracy (the coupling relationship) was established. Based on Monte Carlo mathematic simulation method, the models of the Machining Accuracy reliability and sensitivity analysis of machine tools were developed. By taking the Machining Accuracy reliability as a measure of the ability of machine tool and reliability sensitivity as a reference of optimizing the basic parameters of machine tools, an illustrative example of a three-axis machine tool was selected to demonstrate the effectiveness of the proposed method.
Ligang Cai - One of the best experts on this subject based on the ideXlab platform.
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Machining Accuracy reliability during the peripheral milling process of thin walled components
Robotics and Computer-integrated Manufacturing, 2019Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Zhiqiang Tao, Ligang CaiAbstract:Abstracts During the peripheral milling process of thin-walled components, a workpiece with poor rigidity will cause workpiece deformation error because of the milling force and result in the degradation of Machining Accuracy and related Machining ability of machine tools. As a result, how to obtain the workpiece deformation error and its effect on the surface Machining quality of workpiece is the focus of the research. Hence, a synthesis approach was developed in this study to analyze the Machining Accuracy reliability during the peripheral milling process of thin-walled components. The feedback mechanism between the milling force and milling deformation error was studied, and then a workpiece deformation error model based on an advanced neural fuzzy network was developed. By applying the D-H method, a Machining Accuracy model of the machine tool was established for the machine tool considering the workpiece deformation. Based on the reliability analysis method combined with R-F and Edge worth, a Machining Accuracy reliability model was developed, and then the reliability of Machining Accuracy during the peripheral milling process of thin-walled components was obtained. To verify this approach, a Machining experiment was conducted on a three-axis machine tool; the experimental results indicate that better predictive ability was achieved using the approach presented in the paper.
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a geometric error budget method to improve Machining Accuracy reliability of multi axis machine tools
Journal of Intelligent Manufacturing, 2019Co-Authors: Ziling Zhang, Ligang Cai, Qiang Cheng, Zhifeng LiuAbstract:Machining Accuracy reliability is considered to be one of the most important indexes in the process of performance evaluation and optimization design of the machine tools. Geometric errors, thermal errors and tool wear are the main factors to affect the Machining Accuracy and so affect the Machining Accuracy reliability of machine tools. This paper proposed a geometric error budget method that simultaneously considers geometric errors, thermal errors and tool wear to improve the Machining Accuracy reliability of machine tools. Homogeneous transformation matrices, neural fuzzy control theory and a tool wear predictive approach were employed to develop a comprehensive error model, which shows the influence of the geometric, thermal errors and tool wear to the Machining Accuracy of a machine tool. Based on Rackwite---Fiessler and Advanced First Order and Second Moment, a reliability model and a sensitivity model were put forward, which can deal with the errors of a machine tool drawn from any distribution. Then, a geometric error budget method of multi-axis NC machine tool was developed and formed into a mathematical model. In such method, the minimum cost of machine tool was the optimization objective, the reliability of the Machining Accuracy was the constraint, and the sensitivity was to identify the geometric errors to be optimized. An example conducted on a five-axis NC machine tool was used to explain and validate the proposed method.
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an approach of comprehensive error modeling and Accuracy allocation for the improvement of reliability and optimization of cost of a multi axis nc machine tool
The International Journal of Advanced Manufacturing Technology, 2017Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Ligang CaiAbstract:Machining Accuracy is critical for the quality and performance of a mechanical product, and the reliability of a multi-axis NC machine tool reflects the ability to reach and maintain the required Machining Accuracy. The objective of this study is to propose a general methodology that will simultaneously consider geometric errors and thermal-induced errors to allocate the geometric Accuracy of components, for improving Machining Accuracy reliability under certain design requirements. The multi-body system (MBS) theory was applied to develop a comprehensive volumetric error model, showing the coupling relationship between the individual errors of the components of this machine tool and their volumetric Accuracy. Additionally, a thermal error model was established based on the neural fuzzy control theory and was compared to the common thermal error modeling method called BP neural network. Based on the traditional cost model and the reliability analysis model, a geometric error-cost model and a geometric error-reliability model were established, taking the weighted function principle into consideration. Then, an allocation approach of the geometric errors, for optimizing total cost (manufacture and QLF) and reliability, subject to the geometrical and operational constraints of the machine tool, was proposed and formulated into a mathematical model, in order to perform the optimization process of Accuracy allocation by using the advanced NSGA-II algorithm. A case study was also performed in a five-axis machine tool, and the traditional NSGA algorithm was used for comparison. The optimization results for the five-axis Machining center showed that the proposed approach is effective and able to perform the optimization of geometric Accuracy and improve the Machining Accuracy and the reliability of the machine tool.
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an approach to optimize the Machining Accuracy retainability of multi axis nc machine tool based on robust design
Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology, 2016Co-Authors: Ligang Cai, Ziling Zhang, Qiang Cheng, Zhifeng LiuAbstract:Abstract Machining Accuracy retainability is considered to be one of the most important aspects in the process of performance evaluation and optimization design of the machine tools. Reliability based design optimization (RBDO) and robust design optimization (RDO) are the tools that search for safe structural systems with minimal variability of response when subjected to uncertainties in design parameters. The aim of this paper is to propose an approach that simultaneously considers reliability and robustness to allocate geometric Accuracy of components for improving Machining Accuracy retainability under certain design requirements. Based on homogenous transformation matrices (HTMs), a comprehensive volumetric model explaining how individual errors in the components of a machine affect its volumetric Accuracy (the coupling relationship) was established. By applying high-order moment standardization technique (HOMST), reliability and sensitivity with single failure mode were obtained and then the reliability model and the sensitivity model with multiple failure modes were developed and common methods such as Narrow bounds, AFOSM and Monte Carlo were used for verification and comparison. Meanwhile, a cost model taking manufacturing cost and present worth of expected quality loss into consideration was introduced. By taking the minimum possibility of failure and cost of machine tools as criterions and taking the reliability of machine tools as constraint of optimizing the basic parameters of machine tools, an approach to optimize the Machining Accuracy retainability of multi-axis NC machine tool based on robust design was developed and an example of improving the Machining Accuracy retainability for a five-axis NC machine tool was used to demonstrate the effectiveness of this approach. This study implies that it is possible to obtain the relationships between geometric errors and specify the Accuracy grades of main feeding components of mechanical assemblies.
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fluctuation prediction of Machining Accuracy for multi axis machine tool based on stochastic process theory
Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2015Co-Authors: Zhifeng Liu, Qiang Cheng, Qiunan Feng, Ligang CaiAbstract:Geometric error has significant influence on the processing results and reduces Machining Accuracy. Machine tool geometric errors can be interpreted as a deterministic value with an uncertain fluct...
Zhifeng Liu - One of the best experts on this subject based on the ideXlab platform.
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a Machining Accuracy improvement approach for a horizontal Machining center based on analysis of geometric error characteristics
The International Journal of Advanced Manufacturing Technology, 2021Co-Authors: Peng Niu, Qiang Cheng, Zhifeng Liu, Hongyan ChuAbstract:In the actual production, it is expected to find several sensitivity geometric errors which have great influence on Machining Accuracy, so as to provide references for the manufacturing, assembly, and other links of the machine tool, and fundamentally improve the working performance. To study this problem, a novel global sensitivity analysis (GSA) method is proposed. Based on the MBS theory, the spatial error model is established to analyze the local influence of geometric error on Machining Accuracy. An improved second-order partial correlation coefficient based on the Pearson product moment is proposed to analyze the correlation between geometric errors. In addition, the error parameters will change greatly in the stroke of the moving parts. However, the rapid fluctuation of geometric error value will have a dynamic impact on the Machining Accuracy of machine tools, which is rarely noticed in the previous sensitivity analysis. This change is defined as the fluctuation of error. The high fitting degree function of error-displacement is obtained by using the high order Fourier series and sine series. Then, the fluctuation of the error in the Machining stroke is analyzed by using the derivative of the function to the displacement. Through geometric error characteristics (including the local influence, correlation, and fluctuation) are studied comprehensively, the GSA of error is carried out. Finally, taking a Machining center as an example and combining the sensitivity analysis results, the improvement measures are proposed to verify the correctness of the method.
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Machining Accuracy reliability during the peripheral milling process of thin walled components
Robotics and Computer-integrated Manufacturing, 2019Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Zhiqiang Tao, Ligang CaiAbstract:Abstracts During the peripheral milling process of thin-walled components, a workpiece with poor rigidity will cause workpiece deformation error because of the milling force and result in the degradation of Machining Accuracy and related Machining ability of machine tools. As a result, how to obtain the workpiece deformation error and its effect on the surface Machining quality of workpiece is the focus of the research. Hence, a synthesis approach was developed in this study to analyze the Machining Accuracy reliability during the peripheral milling process of thin-walled components. The feedback mechanism between the milling force and milling deformation error was studied, and then a workpiece deformation error model based on an advanced neural fuzzy network was developed. By applying the D-H method, a Machining Accuracy model of the machine tool was established for the machine tool considering the workpiece deformation. Based on the reliability analysis method combined with R-F and Edge worth, a Machining Accuracy reliability model was developed, and then the reliability of Machining Accuracy during the peripheral milling process of thin-walled components was obtained. To verify this approach, a Machining experiment was conducted on a three-axis machine tool; the experimental results indicate that better predictive ability was achieved using the approach presented in the paper.
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a geometric error budget method to improve Machining Accuracy reliability of multi axis machine tools
Journal of Intelligent Manufacturing, 2019Co-Authors: Ziling Zhang, Ligang Cai, Qiang Cheng, Zhifeng LiuAbstract:Machining Accuracy reliability is considered to be one of the most important indexes in the process of performance evaluation and optimization design of the machine tools. Geometric errors, thermal errors and tool wear are the main factors to affect the Machining Accuracy and so affect the Machining Accuracy reliability of machine tools. This paper proposed a geometric error budget method that simultaneously considers geometric errors, thermal errors and tool wear to improve the Machining Accuracy reliability of machine tools. Homogeneous transformation matrices, neural fuzzy control theory and a tool wear predictive approach were employed to develop a comprehensive error model, which shows the influence of the geometric, thermal errors and tool wear to the Machining Accuracy of a machine tool. Based on Rackwite---Fiessler and Advanced First Order and Second Moment, a reliability model and a sensitivity model were put forward, which can deal with the errors of a machine tool drawn from any distribution. Then, a geometric error budget method of multi-axis NC machine tool was developed and formed into a mathematical model. In such method, the minimum cost of machine tool was the optimization objective, the reliability of the Machining Accuracy was the constraint, and the sensitivity was to identify the geometric errors to be optimized. An example conducted on a five-axis NC machine tool was used to explain and validate the proposed method.
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geometric error compensation method based on floyd algorithm and product of exponential screw theory
Proceedings of the Institution of Mechanical Engineers Part B: Journal of Engineering Manufacture, 2018Co-Authors: Qiang Cheng, Zhifeng Liu, Bingwei Sun, Qiunan FengAbstract:Error compensation technique is a recognized and cost-effective method to improve Machining Accuracy of machine tools. In this article, a new compensation method for geometric error is proposed bas...
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an approach of comprehensive error modeling and Accuracy allocation for the improvement of reliability and optimization of cost of a multi axis nc machine tool
The International Journal of Advanced Manufacturing Technology, 2017Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Ligang CaiAbstract:Machining Accuracy is critical for the quality and performance of a mechanical product, and the reliability of a multi-axis NC machine tool reflects the ability to reach and maintain the required Machining Accuracy. The objective of this study is to propose a general methodology that will simultaneously consider geometric errors and thermal-induced errors to allocate the geometric Accuracy of components, for improving Machining Accuracy reliability under certain design requirements. The multi-body system (MBS) theory was applied to develop a comprehensive volumetric error model, showing the coupling relationship between the individual errors of the components of this machine tool and their volumetric Accuracy. Additionally, a thermal error model was established based on the neural fuzzy control theory and was compared to the common thermal error modeling method called BP neural network. Based on the traditional cost model and the reliability analysis model, a geometric error-cost model and a geometric error-reliability model were established, taking the weighted function principle into consideration. Then, an allocation approach of the geometric errors, for optimizing total cost (manufacture and QLF) and reliability, subject to the geometrical and operational constraints of the machine tool, was proposed and formulated into a mathematical model, in order to perform the optimization process of Accuracy allocation by using the advanced NSGA-II algorithm. A case study was also performed in a five-axis machine tool, and the traditional NSGA algorithm was used for comparison. The optimization results for the five-axis Machining center showed that the proposed approach is effective and able to perform the optimization of geometric Accuracy and improve the Machining Accuracy and the reliability of the machine tool.
C. Y. Chan - One of the best experts on this subject based on the ideXlab platform.
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Investigation of the effects of spindle unbalance induced error motion on Machining Accuracy in ultra-precision diamond turning
International Journal of Machine Tools and Manufacture, 2015Co-Authors: W. B. Lee, C. Y. ChanAbstract:In ultra-precision Machining, error motions of the aerostatic bearing spindle (ABS) have significant effects on the Machining Accuracy. Spindle unbalance is a critical factor attributing to error motions of the ABS. Much work currently has been focused on the measurement of error motions and spindle balancing. However, the unbalance induced spindle error motion (UISEM) and the corresponding effects on Machining Accuracy are not well understood. In this paper, a dynamics model of the ABS was established to characterize the UISEM and its dynamic behavior with consideration of the unbalance effects. A series of groove turning experiments were especially designed to investigate the UISEM. Good agreement between theoretical and experimental results was achieved, demonstrating the low frequency enveloping phenomenon of the error motions of the ABS, identified as the unique superposition effects of two motion components at high frequency in the spindle vibration. In addition, the experimental result reveals that the relative distance between the rotational axis of the ABS and the tool tip varies with respect to the different spindle speeds, significantly degrading the Machining Accuracy.
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Investigation of the effects of spindle unbalance induced error motion on Machining Accuracy in ultra-precision diamond turning
Elsevier, 2015Co-Authors: Huang P, W. B. Lee, C. Y. ChanAbstract:In ultra-precision Machining, error motions of the aerostatic bearing spindle (ABS) have significant effects on the Machining Accuracy. Spindle unbalance is a critical factor attributing to error motions of the ABS. Much work currently has been focused on the measurement of error motions and spindle balancing. However, the unbalance induced spindle error motion (UISEM) and the corresponding effects on Machining Accuracy are not well understood. In this paper, a dynamics model of the ABS was established to characterize the UISEM and its dynamic behavior with consideration of the unbalance effects. A series of groove turning experiments were especially designed to investigate the UISEM. Good agreement between theoretical and experimental results was achieved, demonstrating the low frequency enveloping phenomenon of the error motions of the ABS, identified as the unique superposition effects of two motion components at high frequency in the spindle vibration. In addition, the experimental result reveals that the relative distance between the rotational axis of the ABS and the tool tip varies with respect to the different spindle speeds, significantly degrading the Machining Accuracy.Department of Industrial and Systems Engineerin
Ziling Zhang - One of the best experts on this subject based on the ideXlab platform.
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Machining Accuracy reliability during the peripheral milling process of thin walled components
Robotics and Computer-integrated Manufacturing, 2019Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Zhiqiang Tao, Ligang CaiAbstract:Abstracts During the peripheral milling process of thin-walled components, a workpiece with poor rigidity will cause workpiece deformation error because of the milling force and result in the degradation of Machining Accuracy and related Machining ability of machine tools. As a result, how to obtain the workpiece deformation error and its effect on the surface Machining quality of workpiece is the focus of the research. Hence, a synthesis approach was developed in this study to analyze the Machining Accuracy reliability during the peripheral milling process of thin-walled components. The feedback mechanism between the milling force and milling deformation error was studied, and then a workpiece deformation error model based on an advanced neural fuzzy network was developed. By applying the D-H method, a Machining Accuracy model of the machine tool was established for the machine tool considering the workpiece deformation. Based on the reliability analysis method combined with R-F and Edge worth, a Machining Accuracy reliability model was developed, and then the reliability of Machining Accuracy during the peripheral milling process of thin-walled components was obtained. To verify this approach, a Machining experiment was conducted on a three-axis machine tool; the experimental results indicate that better predictive ability was achieved using the approach presented in the paper.
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a geometric error budget method to improve Machining Accuracy reliability of multi axis machine tools
Journal of Intelligent Manufacturing, 2019Co-Authors: Ziling Zhang, Ligang Cai, Qiang Cheng, Zhifeng LiuAbstract:Machining Accuracy reliability is considered to be one of the most important indexes in the process of performance evaluation and optimization design of the machine tools. Geometric errors, thermal errors and tool wear are the main factors to affect the Machining Accuracy and so affect the Machining Accuracy reliability of machine tools. This paper proposed a geometric error budget method that simultaneously considers geometric errors, thermal errors and tool wear to improve the Machining Accuracy reliability of machine tools. Homogeneous transformation matrices, neural fuzzy control theory and a tool wear predictive approach were employed to develop a comprehensive error model, which shows the influence of the geometric, thermal errors and tool wear to the Machining Accuracy of a machine tool. Based on Rackwite---Fiessler and Advanced First Order and Second Moment, a reliability model and a sensitivity model were put forward, which can deal with the errors of a machine tool drawn from any distribution. Then, a geometric error budget method of multi-axis NC machine tool was developed and formed into a mathematical model. In such method, the minimum cost of machine tool was the optimization objective, the reliability of the Machining Accuracy was the constraint, and the sensitivity was to identify the geometric errors to be optimized. An example conducted on a five-axis NC machine tool was used to explain and validate the proposed method.
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an approach of comprehensive error modeling and Accuracy allocation for the improvement of reliability and optimization of cost of a multi axis nc machine tool
The International Journal of Advanced Manufacturing Technology, 2017Co-Authors: Ziling Zhang, Qiang Cheng, Zhifeng Liu, Ligang CaiAbstract:Machining Accuracy is critical for the quality and performance of a mechanical product, and the reliability of a multi-axis NC machine tool reflects the ability to reach and maintain the required Machining Accuracy. The objective of this study is to propose a general methodology that will simultaneously consider geometric errors and thermal-induced errors to allocate the geometric Accuracy of components, for improving Machining Accuracy reliability under certain design requirements. The multi-body system (MBS) theory was applied to develop a comprehensive volumetric error model, showing the coupling relationship between the individual errors of the components of this machine tool and their volumetric Accuracy. Additionally, a thermal error model was established based on the neural fuzzy control theory and was compared to the common thermal error modeling method called BP neural network. Based on the traditional cost model and the reliability analysis model, a geometric error-cost model and a geometric error-reliability model were established, taking the weighted function principle into consideration. Then, an allocation approach of the geometric errors, for optimizing total cost (manufacture and QLF) and reliability, subject to the geometrical and operational constraints of the machine tool, was proposed and formulated into a mathematical model, in order to perform the optimization process of Accuracy allocation by using the advanced NSGA-II algorithm. A case study was also performed in a five-axis machine tool, and the traditional NSGA algorithm was used for comparison. The optimization results for the five-axis Machining center showed that the proposed approach is effective and able to perform the optimization of geometric Accuracy and improve the Machining Accuracy and the reliability of the machine tool.
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an approach to optimize the Machining Accuracy retainability of multi axis nc machine tool based on robust design
Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology, 2016Co-Authors: Ligang Cai, Ziling Zhang, Qiang Cheng, Zhifeng LiuAbstract:Abstract Machining Accuracy retainability is considered to be one of the most important aspects in the process of performance evaluation and optimization design of the machine tools. Reliability based design optimization (RBDO) and robust design optimization (RDO) are the tools that search for safe structural systems with minimal variability of response when subjected to uncertainties in design parameters. The aim of this paper is to propose an approach that simultaneously considers reliability and robustness to allocate geometric Accuracy of components for improving Machining Accuracy retainability under certain design requirements. Based on homogenous transformation matrices (HTMs), a comprehensive volumetric model explaining how individual errors in the components of a machine affect its volumetric Accuracy (the coupling relationship) was established. By applying high-order moment standardization technique (HOMST), reliability and sensitivity with single failure mode were obtained and then the reliability model and the sensitivity model with multiple failure modes were developed and common methods such as Narrow bounds, AFOSM and Monte Carlo were used for verification and comparison. Meanwhile, a cost model taking manufacturing cost and present worth of expected quality loss into consideration was introduced. By taking the minimum possibility of failure and cost of machine tools as criterions and taking the reliability of machine tools as constraint of optimizing the basic parameters of machine tools, an approach to optimize the Machining Accuracy retainability of multi-axis NC machine tool based on robust design was developed and an example of improving the Machining Accuracy retainability for a five-axis NC machine tool was used to demonstrate the effectiveness of this approach. This study implies that it is possible to obtain the relationships between geometric errors and specify the Accuracy grades of main feeding components of mechanical assemblies.
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geometric Accuracy allocation for multi axis cnc machine tools based on sensitivity analysis and reliability theory
Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2015Co-Authors: Qiang Cheng, Ziling Zhang, Guojun Zhang, Ligang CaiAbstract:Machining Accuracy of a machine tool is influenced by geometric errors produced by each part and component. Different errors have varying influence on the Machining Accuracy of a tool. The aim of this study is to optimize errors to get a desired performance for a numerical control machine tool. Applying multi-body system theory, a volumetric error model was constructed to track and compensate effects of errors during operation of the machine, and to relate the functional specifications on volumetric Accuracy to the permissible errors on the joints and links of the machine. Error sensitivity analysis was used to identify the influence of different errors (especially the errors which have large influences) on volumetric error. Based on First Order and Second Moment theory, an error allocation approach was developed to optimize allocation of manufacturing and assembly tolerances along with specifying the operating conditions to determine the optimal level of these errors so that the cost of controlling them ...
