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Modelling the age-related decrease in ballistic limit velocity of polycarbonate vision panels using a Johnson-Cook material model coupled with variable failure criteria

Uhlmann Eckart, Polte Mitchel, Bergström Nils, Le Vu Ninh

Journal of Machine Engineering

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2023

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Vol. 23, No. 3

86--97

EN

Machine tools are equipped with polycarbonate vision panels that allow the operator to observe the machining process and protect him from ejected fragments. Adequate protection is demonstrated by impact tests. However, polycarbonate is subject to aging processes, which diminish the protective performance of such panels. This paper presents an approach for modelling aging effects on the ballistic limit velocity of polycarbonate using Finite Element simulations. A Johnson-Cook material model in conjunction with variable failure criteria was used for the simulations. Aging effects on the ballistic limit velocity were included in the model by adjusting the failure criteria. Material parameters and failure criteria were derived from experimental impact and tensile tests on unaged and aged polycarbonate specimen. The numerical results predict the ballistic limit velocity with a maximum deviation of 0.98%. The model provides a more in-depth understanding of the aging effects on the safety performance of polycarbonate vision panels.

2

Estimation of external force-torque vector based on double encoders of industrial robots using a hybrid Gaussian process regression and joint stiffness model

Uhlmann Eckart, Polte Mitchel, Blumberg Julian

Journal of Machine Engineering

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2023

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Vol. 23, No. 3

56--68

EN

Industrial robots are increasingly used in industry for contact-based manufacturing processes such as milling and forming. In order to meet part tolerances, it is mandatory to compensate tool deflections caused by the external force-torque vector. However, using a third-party measuring device for sensing the external force-torque vector lowers the cost efficiency. Novel industrial robots are increasingly equipped with double encoders, in order to compensate deviations caused by the gearboxes. This paper proposes a method for the usage of such double encoders to estimate the external force-torque vector acting at the tool centre point of an industrial robot. Therefore, the joint elasticities of a six revolute joint industrial robot are identified in terms of piecewise linear functions based on the angular deviations at the double encoders when an external force-torque vector is applied. Further, initial deviations between the encoder values caused by gravitational forces and friction are modelled with a Gaussian process regression. Combining both methods to a hybrid model enables the estimation of external force-torque vectors solely based on measurements of the joint angles of secondary encoders. Based on the proposed method, additional measurement equipment can be saved, which reduces investment costs and improves robot dynamics.

3

Hyperparameter optimization of artificial neural networks to improve the positional accuracy of industrial robots

Uhlmann Eckart, Polte Mitchel, Blumberg Julian, Li Zhoulong, Kraft Adrian

Journal of Machine Engineering

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2021

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Vol. 21, No. 2

47--59

EN

Due to the rising demand for individualized product specifications and short product innovation cycles, industrial robots gain increasing attention for machining operations as milling and forming. Limitations in their absolute positional accuracy are addressed by enhanced modelling and calibration techniques. However, the resulting absolute positional accuracy stays in a range still not feasible for general purpose milling and forming tolerances. Improvements of the model accuracy demand complex, often not accessible system knowledge on the expense of realtime capability. This article presents a new approach using artificial neural networks to enhance positional accuracy of industrial robots. A hyperparameter optimization is applied, to overcome the downside of choosing an appropriate artificial neural network structure and training strategy in a trial and error procedure. The effectiveness of the method is validated with a heavy-duty industrial robot. It is demonstrated that artificial neural networks with suitable hyperparameters outperform a kinematic model with calibrated geometric parameters.

4

Accuracy in force estimation applied on a piezoelectric fine positioning system for machine tools

Uhlmann Eckart, Polte Mitchel, Triebel Florian, Overbeck Rasmus, Thom Simon

Journal of Machine Engineering

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2021

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Vol. 21, No. 2

24--34

EN

In order to improve the accuracy of machine tools, the use of additional active modules meeting the requirements of the “Plug & Produce” approach is focused. In this context one approach is the installation of a high precision positioning table for online compensation of machine tool deflections. For the model-based determination of the deflection, the knowledge of the effecting process force is crucial. This article examines the use of displacement sensors for force estimation in a piezoelectric system. The method is implemented on a high precision positioning table applicable in milling machine tools. In order to compensate nonlinear effects of piezoelectric actuators, a hysteresis operator is implemented. Experimental investigations are carried out to quantify the influence of preload stiffness, preload force and workpiece weight. Finally, a resolution d ≤ 78 N could be achieved and further improvements to meet the requirements for online compensation of machine tool deflection are discussed.

5

Modelling of a thermoelectric self-cooling system based on thermal resistance networks for linear direct drives in machine tools

Uhlmann Eckart, Salein Sebastian, Polte Mitchel, Triebel Florian

Journal of Machine Engineering

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2020

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Vol. 20, No. 1

43--57

EN

The use of direct drives in linear and rotary axes as well as increased power density of main drives offer the potential to raise feet rate, acceleration and thus allow higher productivity of machine tools. The induced heat flow rates of these drives could lead to thermo-elastic deformations of precision related machine tool components. In order to reduce thermally caused displacements of the tool-center-point and to prevent a negative impact on the achievable accuracy, the induced heat flow rates of main drives must be dissipated by effective cooling systems. These systems account for a major share of the machine tool’s total energy consumption.With the intention to overcome the area of conflict regarding productivity and energy efficiency, a so called thermoelectric self-cooling system has been developed. To convert a proportion of thermal losses into electrical energy, thermoelectric generators are placed in the heat flow between the primary part of a linear direct drive and the cooling system. The harvested energy is directly supplied to a pump of the water cooling circuit, which operates a decentralised cooling system with reasonable coolant flow rates. For predicting the thermoelectric system behaviour and to enable a model-based design of thermoelectric self-cooling systems, a thermal resistance network as a system simulation in MATLAB/Simulink is presented. The model is applied to a feed unit with a linear direct drive and allows the calculation of harvested energy as well as the simulation of steady and transient states of the cooling system. The comparison of simulative and experimental determined data indicates a predominantly high model prediction accuracy with short simulation times. At an early stage of development the model turns out to be a powerful tool for design and analysis of water flow thermoelectric self-cooling systems.