Autonomously triggered model updates for self-learning thermal error compensation

Zimmermann, Nico; Breu, Mario; Mayr, Josef; Wegener, Konrad

doi:10.1016/j.cirp.2021.04.029

Cited by 27 publications

(12 citation statements)

References 13 publications

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“…where Rsd k is the Rsd value of the kth batch of experiments, and they can be calculated by Equation (17). K = 46 is the total number of batches of the predicted experiments.…”

Section: Modeling Effect Analysis Of Different Numbers Of Atismentioning

confidence: 99%

“…Based on the concept of this algorithm, researchers have proposed the rough set theory combined with the grey correlation degree algorithm [ 10 ] and fuzzy clustering combined with the correlation coefficient algorithm [ 11 ] to select the TSPs. Second, the algorithms commonly used to establish the thermal error prediction model (TEPM) include the multiple linear regression algorithm [ 6 , 12 , 13 ], time series algorithm [ 14 , 15 ], neural network algorithm [ 7 , 8 , 10 , 13 ], and support vector machine algorithm [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Year-Round Thermal Error Modeling and Compensation for the Spindle of Machine Tools Based on Ambient Temperature Intervals

Wei

Feng

2022

Sensors

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The modeling and compensation method is a common method for reducing the influence of thermal error on the accuracy of machine tools. The prediction accuracy and robustness of the thermal error model are two key performance measures for evaluating the compensation effect. However, it is difficult to maintain the prediction accuracy and robustness at the desired level when the ambient temperature exhibits strong seasonal variations. Therefore, a year-round thermal error modeling and compensation method for the spindle of machine tools based on ambient temperature intervals (ATIs) is proposed in this paper. First, the ATIs applicable to the thermal error prediction models (TEPMs) under different ambient temperatures are investigated, where the C-Means clustering algorithm is utilized to determine ATIs. Furthermore, the prediction effect of different numbers of ATIs is analyzed to obtain the optimal number of ATIs. Then, the TEPMs corresponding to different ATIs in the annual ambient temperature range are established. Finally, the established TEPMs of ATIs are used to predict the experimental data of the entire year, and the prediction accuracy and robustness of the proposed ATI model are analyzed and compared with those of the low and high ambient temperature models. The prediction accuracies of the ATI model are 20.6% and 41.7% higher than those of the low and high ambient temperature models, respectively, and the robustness is improved by 48.8% and 62.0%, respectively. This indicates that the proposed ATI method can achieve high prediction accuracy and robustness regardless of the seasonal temperature variations throughout the year.

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“…where Rsd k is the Rsd value of the kth batch of experiments, and they can be calculated by Equation (17). K = 46 is the total number of batches of the predicted experiments.…”

Section: Modeling Effect Analysis Of Different Numbers Of Atismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Year-Round Thermal Error Modeling and Compensation for the Spindle of Machine Tools Based on Ambient Temperature Intervals

Wei

Feng

2022

Sensors

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“…Several attempts at realizing long-term thermal error compensation have been made [27,45,47,48,60,70]. One of the ways to realize long-term compensation is to train and test the model on data from different seasons of the year [27].…”

Section: Case Studymentioning

confidence: 99%

“…There is always a risk that the model trained in this way will become inaccurate in the future due to a change in the conditions. In order to prevent this, self-learning has been employed [45,47,48,60,70]. The R-Test procedure was used to measure the thermal error by means of a measuring probe.…”

Section: Case Studymentioning

confidence: 99%

“…Then an investigation into increasing the cost-effectiveness of the self-compensation system by using the detection of changes in the thermal conditions, based on a one-class support vector machine (SVM), was carried out [70]. Thanks to such detection the machine tool productivity loss was reduced by 78% with no significant decrease in self-compensation system precision.…”

Section: Case Studymentioning

confidence: 99%

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Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Czwartosz¹,

Jędrzejewski²

2022

Journal of Machine Engineering

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The current development of production engineering takes place through the innovative improvement of machine tools and machining processes at the constantly growing application of intelligent self-improvement functions. Machine learning opens up possibilities for machine tool self-improvement in real time. This paper discusses the state of knowledge relating to the application of machine learning for precise and cost-effective thermal error self-compensation. Data acquisition and processing, models and model learning and self-learning methods are also considered. Three highly effective error compensation systems (supported with machine learning) are analysed and conclusions and recommendations for future research are formulated.

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Adaptive Thermal Error Compensation Model of a Horizontal Machining Centre

Horejš

Mareš

Straka

et al. 2023

Lecture Notes in Production Engineering

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The state-of-the-art method to reduce CNC machine tool thermal errors is real-time error compensation based on the thermal error estimation models. However, it is difficult to establish a thermal error compensation model with good versatility, high accuracy, and strong robustness due to various manufacturing conditions and a thermally varying surrounding environment. It causes that thermal behaviour of the machine tools is nonlinear and varying in real time. Consequently, the pre-trained and non-adaptive model may not be accurate and robust enough for long-term application. The presented research shows a systematic adaptation technique to update the thermal error compensation model of a horizontal machining centre under varying conditions, which differ from the calibration test. System identification theory is applied to build a dynamic thermal error model for a horizontal machining centre based on calibration test. Linear parametric models of autoregressive with external input (ARX) present an established dynamic method, and its modelling and calculation speed are suitable for real-time applications. Additionally, process-intermittent probing and thermal error model are integrated into the machine management software of the horizontal machining centre to monitor and compensate for thermal errors at the tool centre point (TCP) in real time using C#/C++ programming language. The results show that the prediction accuracy measured as peak-to-peak values and the normalized root mean squared error of the thermal error compensation models are improved by up to 33% and 51%, respectively, when adaptive compensation model is applied.

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Autonomously triggered model updates for self-learning thermal error compensation

Cited by 27 publications

References 13 publications

Year-Round Thermal Error Modeling and Compensation for the Spindle of Machine Tools Based on Ambient Temperature Intervals

Year-Round Thermal Error Modeling and Compensation for the Spindle of Machine Tools Based on Ambient Temperature Intervals

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Adaptive Thermal Error Compensation Model of a Horizontal Machining Centre

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