An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates

Mayr, Josef; Blaser, Philip; Ryser, Adrian; Hernandez-Becerro, Pablo

doi:10.1016/j.cirp.2018.04.001

Cited by 67 publications

(31 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The functionality of the ALC procedure is explained in [Blaser 2017, Blaser 2018, Mayr 2018. However, it is briefly reviewed in this section, since it provides the basis for the research presented in this paper.…”

Section: Adaptive Learning Control (Alc) For Thermal Error Compensationmentioning

confidence: 99%

“…To the fact, that the measurement intervals of the onmachine inspection can be triggered arbitrarily, the sample rate of the actual measured thermal errors is changing constantly. Mayr et al [Mayr 2018] presented an adaptive self-learning algorithm for thermal error compensation for arbitrary sample rates. It is shown that with the extension of a weighting matrix for the least squares (LSQ) estimation of the ARX system parameters, the thermal model can handle any sampling rate coming from the arbitrary TCP measurements.…”

Section: Adaptive Learning Control (Alc) For Thermal Error Compensationmentioning

confidence: 99%

“…As one can see, the environmental temperature is fluctuating in daily cycles and is heavily influenced by the hall door right next to the machine. As shown in [Blaser 2017, Blaser 2018, Mayr 2018, the inputs as seen in Fig. 4 can be used for the ALC and yield accurate compensation results for the presented load cases.…”

Section: Parameter Study Of Alcmentioning

confidence: 99%

“…Compensation is a process where the thermal errors present at a particular time are corrected by adjusting the position of a machine's axes by an amount equal to the errors at that position. Researchers have employed various techniques such as a finite-element method for instance by [Mayr 2015a, Mian 2013] and finite-difference method by for example [Bossmanns 1999, Mayr 2009 in modelling the thermal characteristics. However, building a physical model can be a great challenge due to problems of establishing the boundary conditions and accurately obtaining the characteristics of heat transfer.…”

Section: Introductionmentioning

confidence: 99%

“…In some publications, approaches can be found, where also multiple variables, such as the spindle speed, motor current, historical information etc. are used as inputs to model to the thermal errors, see [Brecher 2009, Gebhardt 2013, Mayr 2015b, Yang 2005] just to name a few. According to Yang and Ni [Yang 2003] models that only consider the current state of the machine are considered a static approach and are widely used for machine tool thermal error modeling in industry.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Long-Term Thermal Compensation of 5-Axis Machine Tools Due to Thermal Adaptive Learning Control

Blaser¹,

Mayr²,

Wegener³

2019

MM SJ

View full text Add to dashboard Cite

This paper presents a prediction and compensation approach for thermal errors of 5-axis machine tools, based on supervised online machine learning. Process-intermittent probing is used to identify and update a thermal autoregressive with exogenous input (ARX) model. The approach is capable of predicting and compensating thermal displacements of the tool center point based on changes in the environmental temperature, load-dependent changes and boundary condition changes and states, like dry or wet machining. The self-optimized machine tool shows very stable long-term behavior under drastically varying machining and boundary conditions. The implementation is validated on a set of thermal test pieces. The test pieces show that the major share of thermal workpiece errors are reduced by the thermal adaptive learning control.

show abstract

Section: Adaptive Learning Control (Alc) For Thermal Error Compensationmentioning

confidence: 99%

Section: Adaptive Learning Control (Alc) For Thermal Error Compensationmentioning

confidence: 99%

Section: Parameter Study Of Alcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Long-Term Thermal Compensation of 5-Axis Machine Tools Due to Thermal Adaptive Learning Control

Blaser¹,

Mayr²,

Wegener³

2019

MM SJ

View full text Add to dashboard Cite

show abstract

Development of Thermal Displacement Prediction Model and Thermal Deformation Measurement Methods

Ota

Mori

Irino

2023

Lecture Notes in Production Engineering

View full text Add to dashboard Cite

In machine tools, it is important to maintain accuracy against heat in the entire machining space. The reduction of thermal displacement is achieved by measuring it accurately and then clarify the relationship between the temperature change and the thermal deformation and then creating the prediction and compensation model or applying the countermeasure to suppress the thermal deformation. To solve this issue, the authors have developed methods to measure thermal deformation in two-dimensional space using a laser tracker and cameras. A laser tracker method achieved higher accuracy and smaller measurement uncertainty. Vision-based methods achieved equivalent measurement accuracy to a laser tracker method at shorter measurement time and lower cost. To compensate thermal displacement, convolutional neural network model was developed. Thermal displacement was predicted from temperature sensors with higher accuracy than conventional models. These methods contribute to the development of higher precision machine tools.

show abstract

Thermal Error Compensation Models Utilizing the Power Consumption of Machine Tools

Lang

Zimmermann

Mayr

et al. 2023

Lecture Notes in Production Engineering

View full text Add to dashboard Cite

Thermal errors are among the most significant contributors to deviations of products manufactured on modern machine tools (MTs). Reducing them is typically achieved through either design adaptation, active cooling of the MT and its environment, or compensation using measurements or model-based predictions. Model-based compensation strategies promise to have the lowest environmental footprint by far. In general, a compensation model needs to be accurate, robust to changing boundary conditions and must require only minimal experimental efforts as this reduces the productivity of the MT. Model inputs such as temperature measurements or the power consumption of various components, can be used to predict the thermal errors. The temperature inputs require additional sensors, effort and cost for the MT manufacturer to install and ensure up-time while the power consumption could be logged and are typically provided from the control system anyway. Adaptive compensation models are created using four different sets of inputs consisting of 13 temperature sensors and 7 power measurements. While the best results were obtained with all 20 inputs, the 7 energy recordings give similar results as the 13 temperature sensors if the environmental temperature is considered. The volumetric RMSE was reduced by 72% and the maximal error from 32.75 µm to 9.5 µm. ARX models proved to be suitable and even outperform more complex model structures such as LSTM and especially those without time dependency such as feed forward neural networks.

show abstract

An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates

Cited by 67 publications

References 11 publications

Long-Term Thermal Compensation of 5-Axis Machine Tools Due to Thermal Adaptive Learning Control

Long-Term Thermal Compensation of 5-Axis Machine Tools Due to Thermal Adaptive Learning Control

Development of Thermal Displacement Prediction Model and Thermal Deformation Measurement Methods

Thermal Error Compensation Models Utilizing the Power Consumption of Machine Tools

Contact Info

Product

Resources

About