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Recent advances in precision, sustainability and safety of machine tools

Uhlmann Eckart

Journal of Machine Engineering

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2023

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Vol. 23, No. 3

5--25

EN

Machine tools are the main driver of economic, environmental and social sustainability in industrial production. The ongoing shift from mass production to highly individualized, small batch manufacturing requires machine tools to be more flexible to changing needs while maintaining at least the same level of productivity. However, flexibility and productivity are at odds with the necessity for resource and energy efficiency. At the same time, more sophisticated workpiece specifications are pushing the boundaries regarding precision and dynamics of machine tools. In such a high-performance context, machine safety plays a major role and is becoming increasingly challenging due to higher kinetic energies of moving components. This paper examines recent advances in machine tool precision, sustainability, and safety. Six comprehensive case studies are provided to illustrate how these improvements contribute to an increased productivity. Hardware and software solutions for pose-controlled robotic manufacturing and thermoelectrically tempered high-performance spindles will be presented. Modular machine tool frames based on building blocks and an adaptive cooling system with thermoelectric generators for linear direct drives demonstrate their major impact on resource and energy efficiency. Machine safety is addressed through an analysis of potential hazards as well as improved protective measures. Model-based predictions precisely identify critical process parameters that lead to unbalance-induced failure of slim tool extensions, while on the protection side, new statistical models are applied to assess the protective performance of safeguards much more accurately. The cutting-edge technologies for machine tools presented in this paper will help manufacturers to cope with current and future challenges in industrial production.

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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.

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Method of the environmentally friendly dry single-pass honing with use of the superimposed low frequency oscillations

Rozek Andre, Uhlmann Eckart

Journal of Machine Engineering

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2023

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Vol. 23, No. 3

116--129

EN

Single-pass honing is used as a finishing process to meet high demands regarding form and dimensional accuracy of drilled holes. The disadvantages of single-pass honing compared to the conventional long-stroke honing are high process forces and torques as well as an increased risk of chip space clogging of the abrasive stones. Following this, the oscillation-superimposed single-pass honing without cutting fluid has been conducted in this work, which is promising when it comes to the environmentally friendly improvement of machining processes. It was shown that the omitted lubrication and flushing effect of the contact zone between the tool and the workpiece could be compensated with the aid of the superimposed oscillations. The process forces of the dry honing process are up to 37% lower compared to the conventional process, the height of the surface profile Rz decreases by 33% and the form deviations decrease up to 47%. Hence, the new method allows the saving of resources, while improving the work results.

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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.

5

Enabling of automatically generation of cutting paths for three-dimensional pre-contouring with waterjet trimming

Reder Waldemar, Uhlmann Eckart

Journal of Machine Engineering

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2023

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Vol. 23, No. 2

66--76

EN

Abrasive Water Injector Jet Cutting (AWIJC) is a flexible machining process for manufacturing high-performance materials, such as titan- and nickel-base-alloys. Due to the low ductility and thermal conductivity of these materials, conventional machining is struggling with high tool costs and wear. The tool wear in AWIJC is independent of the machined material, and the process has the potential to provide a cost-efficient solution in machining high-performance materials. Trimming, a near-net-shape pre-contouring with multi-stage AWIJC, requires a detailed knowledge of cutting paths for all steps in advance. In order to enable a geometrical flexible manufacturing process, an automatically cutting path generation is necessary. This article presents an application developed with NX Open using Visual Basic. The application TrimCAD is able to provide all necessary geometries for trimming based on the geometries of initial and finished parts. Furthermore, it is possible to adjust the number of cuts and the degree of pre-contouring. All geometries are automatically exported as a standardized three-dimensional STEP-file. The STEP-geometries can be processed to the CAM-processor of the waterjet machine. TrimCAD is an innovative possibility to machine three-dimensional parts made of high-performance materials.

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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.

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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.

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Wear evaluation of CVD diamond coated high-performance drilling tools for machining of carbon fiber reinforced plastics (CFRP)

Uhlmann Eckart, Reimers Walter, Hinzmann Daniel, Christiansen Gerret, Böttcher Katrin

Journal of Machine Engineering

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2020

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Vol. 20, No. 2

104--113

EN

The application of carbon fiber reinforced plastics (CFRP) as lightweight construction material in aerospace industry is based on the favorable weight-to-strength ratio. But the inherent material properties pose great challenges for the tool- as well as the manufacturing industry. In terms of economic industrial production processes, the quality of machined workpieces exhibits poor reproducibility combined with high tool wear. For this purpose, high-performance drilling tools with different CVD diamond coatings and carbide substrates with varying binder content were tested and analyzed in order to assess coating adhesion and workpiece quality. Due to a reduction of cobalt binder within the tungsten carbide-based tool substrates, an increase of tool performance regarding borehole quantity until coating delamination is demonstrated. While the reduction of tool wear on the rake face of the drilling tools can be correlated with the cutting tool performance, the online monitoring of cutting forces does not explicitly identify damaged cutting tools during machining.

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Optimising the grinding wheel design for flute grinding processes utilising numerical analysis of the complex contact conditions

Uhlmann Eckart, Gülzow Bernhard, Muthulingam Arunan

Journal of Machine Engineering

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2020

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Vol. 20, No. 3

85--94

EN

Resource efficiency is gaining relevance in every aspect of production. Hence, cutting tools are exposed to high demands regarding productivity and quality. Considering the various grinding operations in tool manufacturing, flute grinding is the most significant process step as it defines the peripheral cutting edge and the rake face. Therefore, it has a substantial influence on the machining behaviour of, for example, milling tools. When it comes to helical flutes, the complex contact conditions between grinding wheel and tool blank during the multiaxial grinding process are particularly difficult to determine. Due to the lack of knowledge about those contact conditions, the grinding wheels typically used for flute grinding cannot wholly meet the actual process requirements. In order to optimise the design of the grinding wheels, a numerical model was developed. Based on that, a simulation tool was implemented to analyse the complex contact conditions during flute grinding depending on the process parameters and tool/workpiece geometry. The influence of different grinding parameters on the effective contact length, the specific material removal rate and the equivalent chip thickness was evaluated by employing the computer-based model. The generated results were then used to develop a new optimised tool concept for a more efficient flute grinding process.

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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.

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An architectural design for measurement uncertainty evaluation in cyber-physical systems

Pilar von Pilchau Wenzel, Gowtham Varun, Gruber Maximilian, Riedl Matthias, Koutrakis Nikolaos-Stefanos, Tayyub Jawad, Hähner Jörg, Eichstädt Sascha, Uhlmann Eckart, Polte Julian, Frey Volker, Willner Alexander

Annals of Computer Science and Information Systems

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2020

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Vol. 22

53--57

EN

Several use cases from the areas of manufacturing and process industry, require highly accurate sensor data. As sensors always have some degree of uncertainty, methods are needed to increase their reliability. The common approach is to regularly calibrate the devices to enable traceability according to national standards and Syst\`eme international (SI) units - which follows costly processes. However, sensor networks can also be represented as Cyber Physical Systems (CPS) and a single sensor can have a digital representation (Digital Twin) to use its data further on. To propagate uncertainty in a reliable way in the network, we present a system architecture to communicate measurement uncertainties in sensor networks utilizing the concept of Asset Administration Shells alongside methods from the domain of Organic Computing. The presented approach contains methods for uncertainty propagation as well as concepts from the Machine Learning domain that combine the need for an accurate uncertainty estimation. The mathematical description of the metrological uncertainty of fused or propagated values can be seen as a first step towards the development of a harmonized approach for uncertainty in distributed CPS in the context of Industrie 4.0. In this paper, we present basic use cases, conceptual ideas and an agenda of how to proceed further on.