Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model

Abdulshahed, Ali; Longstaff, Andrew P.; Fletcher, Simon; Potdar, Akshay

doi:10.1016/j.jmsy.2016.08.006

Cited by 92 publications

(32 citation statements)

References 37 publications

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“…From the performance comparison of MLR1 and MLR2, it is concluded that the proposed TMPs selection method improves the accuracy of thermal error compensation. Two reasons can accounts for MLR2 poor performance: firstly, the input of MLR2 may include temperature variables which is uncorrelated or weak related to thermal error, the noise carried by these temperature has a negative influence on the robustness of thermal error model (Abdulshahed et al 2016); secondly, the strong correlation among temperature variable will cause multicollinearity, which may also lead to the inaccuracy of prediction(El-Dereny and Rashwan, 2011). Comparing MLR1 and MLR3, it is found that the proposed TMPs selection method performs better than Miao's method in reducing average error and reducing standard deviation.…”

Section: Fig 14 the Prediction Results Of Mlr2mentioning

confidence: 99%

“…And the values of these parameters are mainly determined by researchers experience, although the number of selected TMPs may be crucial to thermal error model (ZHANG et al, 2013). Too less TMPs may lead to poor prediction accuracy while too many may have a negative influence on a model's robustness (Abdulshahed et al, 2016). Therefore, in this research, except for considering eliminating noisy TMPs and redundant TMPs, potential subsets searching based on MRMR and wrapper method are combined to determine the optimal number of selected TMPs, which can contribute to the performance of thermal error model.…”

Section: Introductionmentioning

confidence: 99%

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Selection of key temperature measuring points for thermal error modeling of CNC machine tools

Liu¹,

Chen²,

Lou³

et al. 2018

JAMDSM

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Thermal errors account for more than 40% of the machining errors in CNC machine tools, and even reach 70% in precision and ultra-precision machine tools. One approach to reducing thermal errors is to build thermal error prediction models by monitoring the temperature field of machine tools with the data-driven modeling approaches. Usually the data-driven approaches have over-fitting and under-fitting problems. The prediction performances of the data-driven models are greatly dependent on the input of the models, namely the number of temperature measuring points (TMPs) and their locations. In this paper, a selection method of key TMPs is presented to improve the accuracy and generalization of predictive models. In this selection method, correlation analysis is used to eliminate the uncorrelated or weakly correlated TMPs to thermal errors at first; then the Minimum Redundancy Maximum Relevance (MRMR) is presented to narrow the searching scope of TMPs; finally Wrapper method is used to test the candidate set of TMPs with cross-validation accuracy to find the key TMPs. To validate this method, a test platform is built on the CNC gantry drilling machine ZK5540A. 56 Fiber Bragg Grating (FBG) temperature sensors are amounted on the body, column, spindle, and base of this machine. 7 key TMPs was selected from the 56 ones with this method. And Multiple linear regression (MLR) approach is used to build the thermal error prediction model with the 7 key TMPs, 56 TMPs and key TMPs selected by Miao's method (Miao et al., 2014) respectively. The result shows that the prediction model built with MLR using the 7 key TMPs is much more accurate and has stronger generalization.

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Section: Fig 14 the Prediction Results Of Mlr2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Selection of key temperature measuring points for thermal error modeling of CNC machine tools

Liu¹,

Chen²,

Lou³

et al. 2018

JAMDSM

View full text Add to dashboard Cite

show abstract

“…To verify the performance of the proposed model, this paper used grey neural network architecture [16][17][18][19]. Fig.…”

Section: Health Index and Proposed Neural Network Architecturementioning

confidence: 99%

“…} is a fragment of the streaming health data. AGO is used to preprocess the original data into approximate monotonic sequence data [19]. The purpose of this transformation is to weaken the random fluctuation in series data.…”

Section: Health Index and Proposed Neural Network Architecturementioning

confidence: 99%

On-line prediction remaining useful life for ball bearings via grey NARX

Niu¹,

Tong²,

Cao³

et al. 2019

J. vibroeng.

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The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields.

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“…Instead, Bosetti et al [26] propose a complex reticular structure based on FBGs associated with a computation algorithm that allows for the reconstruction of the displacement field. A recent interesting work has been developed by Abdulshahed et al [27]. This study shows a new modelling methodology for compensation of the thermal errors.…”

Section: Introductionmentioning

confidence: 99%

A Kinematic Model to Compensate the Structural Deformations in Machine Tools Using Fiber Bragg Grating (FBG) Sensors

et al. 2017

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Structural deformations are one of the most significant factors that affects machine tool (MT) positioning accuracy. These induced errors are complex for accurate representation by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through fiber Bragg grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fiber Reinforced Polymers (CFRP), equipped with FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of Finite Element (FE) simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The aim of this study was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed by varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of the TTP, thereby confirming the effectiveness of the proposed system.

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Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model

Cited by 92 publications

References 37 publications

Selection of key temperature measuring points for thermal error modeling of CNC machine tools

Selection of key temperature measuring points for thermal error modeling of CNC machine tools

On-line prediction remaining useful life for ball bearings via grey NARX

A Kinematic Model to Compensate the Structural Deformations in Machine Tools Using Fiber Bragg Grating (FBG) Sensors

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