Adaptive input selection for thermal error compensation models

Zimmermann, Nico; Lang, Sebastian; Blaser, Philip; Mayr, Josef

doi:10.1016/j.cirp.2020.03.017

Cited by 41 publications

(25 citation statements)

References 15 publications

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“…In this study, the number of ATIs ranges from 2 to 6 with a step size of 1. Then, the prediction accuracy and robustness with different number of ATIs are calculated using Equations ( 20) and (21). The calculation results are plotted in Figure 9 to visually show the influence of the number of ATIs on the prediction effects.…”

Section: Modeling Effect Analysis Of Different Numbers Of Atismentioning

confidence: 99%

“…Recently, researchers have further studied the thermal error modeling algorithm of CNC machine tools [ 18 , 19 , 20 , 21 , 22 ] to improve the accuracy and robustness of thermal error prediction. However, the thermal error data in these studies had very small variations of ambient temperature, and the influence of ambient temperature was rarely considered.…”

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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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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show abstract

“…people, machines) in the neighbourhood of the machine tool or a change in the machining program. The model's accuracy can be restored through self-learning consisting in model updating on the basis of additionally (autonomically) acquired temperature and thermal error measurements [45,47,48]. The cost of an improvement in model accuracy is a reduction in machine tool productiveness.…”

Section: Gist Of Machine Learningmentioning

confidence: 99%

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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“…This allows a precise representation of the dynamic system behavior, compared to the static models which are often used in industry [53]. In comparison to the model parameters, the model inputs of the original TALC are predefined and cannot be adapted during the compensation phase.…”

Section: Ensemble Deep Transfer Learningmentioning

confidence: 99%

Operator Integrated – Concept for Manufacturing Intelligence

Wegener¹,

Weikert²,

Mayr³

et al. 2021

Journal of Machine Engineering

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Increasing autonomy and sustainability are major goals in manufacturing. Main technological trends provide enablers for achieving these goals and need to be implemented and combined in manufacturing machinery in a suitable manner. The paper exposes a vision of modern manufacturing machines, where the complexity of manufacturing processes is handled within the manufacturing machine and a simplistic front end is presented to the operator, which means that major elements of operators' tasks are fulfilled by the intelligence of the machine. Research vectors paving the ground for this concept from different points of view are then discussed. Research is presented on intelligent grinding, intelligent recognition and suppression of chatter, adaptive thermal and motion error compensation exploting also self-learning abilities. It is necessary to point out, that not only intelligent mastering of process and machine becomes more and more important but communications among machine tools enabling process chain overarching intelligent approaches and creating intelligent factories.

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Adaptive input selection for thermal error compensation models

Cited by 41 publications

References 15 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

Operator Integrated – Concept for Manufacturing Intelligence

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