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1

Handling ambient temperature changes in correlative thermal error compensation

Naumann Christian, Geist Alexander, Putz Matthias

Journal of Machine Engineering

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2023

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Vol. 23, No. 4

43--63

EN

Thermal errors are one of the leading causes for positioning inaccuracies in modern machine tools. These errors are caused by various internal and external heat sources and sinks, which shape the machine tool’s temperature field and thus its deformation. Model based thermal error prediction and compensation is one way to reduce these inaccuracies. A new composite correlative model for the compensation of both internal and external thermal effects is presented. The composite model comprises a submodel for slow long- and medium-term ambient changes, one for short-term ambient changes and one for all internal thermal influences. A number of model assumptions are made to allow for this separation of thermal effects. The model was trained using a large number of FE simulations and validated online in a five-axis machine tool with measurements in a climate chamber. Despite the limitations, the compensation model achieved good predictions of the thermal error for both normal ambient conditions (21°C) and extreme ambient conditions (35°C).

2

Predictive technology assessment by means of a structure-based method of machine learning

Pfaff Manja Mai-Ly, Frieß Uwe, Otto Andreas, Putz Matthias

Journal of Machine Engineering

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2020

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Vol. 20, No. 4

59--73

EN

From a user perspective, the current development of the generic term Industry 4.0 increasingly moves its orientation towards flexible production. Due to increasingly variable products with small quantities and the resulting high degree of adaptability of a plant over its entire operating phase, the need for rapid production commissioning gives rise to the demand for live commissioning support and technology evaluation of induced production start-ups. Classification axioms can be formed by 1-class learning procedures for the predictive state evaluation of subsequent production start-ups based on collected machine and process data from past production start-ups. The starting point is an adaptive algorithm that performs a dynamic tolerance band formation based on different criteria, emphasizing on adaptive characteristic segmentation. This first step represents comprehensive condition monitoring. Based on this algorithm, correlation considerations can be performed on the data structure, the measured variables, and the diagnostic parameters. Moreover, the structure of production systems can and should be included in the analyzation, so that probabilistic causalities can be postulated and then be added to the underlying data sets for quantification. Using these adaptive structure-based segmentations is the first step to interpret data sets of new production systems without the need for complex pre-configuration.

3

Comparison of basis functions for thermal error compensation based on regression analysis – a simulation based case study

Naumann Christian, Glänzel Janine, Putz Matthias

Journal of Machine Engineering

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2020

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Vol. 20, No. 4

28--40

EN

It is a well-known problem of milling machines, that waste heat from motors, friction effects on guides, the environment and the milling process itself greatly affect positioning accuracy and thus production quality. An economic and energy-efficient method of correcting this thermo-elastic positioning error is to gather sensor data (temperatures, axis positions, etc.) from the machine tool and the process and to use that information to predict and correct the resulting tool center point displacement using regression analysis. This paper compares multilinear characteristic diagrams, B-spline characteristic diagrams, Radial Basis Function fitting and Wavelet fitting in general and also in the context of thermal error compensation. The demonstrations are made using FEM simulation data from a machine tool demonstrator. The results show that all of the above kernel types, if properly used, are able to create good compensation models. However, high-dimensional multivariate analysis usually only works by adding grid structures and regularization.

4

Parameterization of environmental influences by automated characteristic diagrams for the decoupled fluid and structural-mechanical simulations

Glänzel Janine, Kumar Tharun Suresh, Naumann Christian, Putz Matthias

Journal of Machine Engineering

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2019

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Vol. 19, No. 1

98--113

EN

Thermo-elastic effects contribute the most to positioning errors in machine tools especially in operations where high precision machining is involved. When a machine tool is subjected to changes in environmental influences such as ambient air temperature, velocity or direction, then flow (CFD) simulations are necessary to effectively quantify the thermal behaviour between the machine tool surface and the surrounding air (fluid). Heat transfer coefficient (HTC) values effectively represent this solid-fluid heat transfer and it serves as the boundary data for thermo-elastic simulations. Thereby, deformation results can be obtained. This two-step simulation procedure involving fluid and thermo-structural simulations is highly complex and time-consuming. A suitable alternative for the above process can be obtained by introducing a clustering algorithm (CA) and characteristic diagrams (CDs) in the workflow. CDs are continuous maps of a set of input variables onto a single output variable, which are trained using data from a limited number of CFD simulations which is optimized using the clustering technique involving genetic algorithm (GA) and radial basis function (RBF) interpolation. The parameterized environmental influences are mapped directly onto corresponding HTC values in each CD. Thus, CDs serve as look-up tables which provide boundary data (HTC values along with nodal information) under several load cases (combinations of environmental influences) for thermo-elastic simulations. Ultimately, a decoupled fluid-structural simulation system is obtained where boundary (convection) data for thermo-mechanical simulations can be directly obtained from CDs and would no longer require fluid simulations to be carried out again. Thus, a novel approach for the correction of thermo-elastic deformations on a machine tool is obtained.

5

A new multigrid based method for characteristic diagram based correction of thermo-elastic deformations in machine tools

Naumann Christian, Putz Matthias

Journal of Machine Engineering

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2019

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Vol. 19, No. 4

42--57

EN

It is a well-known problem of milling machines, that waste heat from motors, friction effects on guides, environmental variations and the milling process itself greatly affect positioning accuracy and thus production quality. An economic and energy-efficient method of correcting this thermo-elastic positioning error is to gather sensor data (temperatures, axis positions, etc.) from the machine tool and the process and to use that information to predict and correct the resulting tool center point displacement using high dimensional characteristic diagrams. The computation of these characteristic diagrams leads to very large sparse linear systems of equations which require a vast memory and computation time to solve. This is particularly problematic for complex machines and varying production conditions which require characteristic diagrams with many input variables. To solve this issue, a new multigrid based method for the computation of characteristic diagrams will be presented, tested and compared to the previously used smoothed grid regression method.