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1

Software-defined workpiece positioning for resource-optimized machine tool utilization

Ströbel Robin, Probst Yannik, Hutt Louisa, Fleischer Jürgen

Journal of Machine Engineering

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2023

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Vol. 23, No. 1

71--84

EN

Advancing climate change, tense world markets, and political pressure steadily increase the demand for resource-optimized production solutions. Herby, the positioning of the raw material in the machine tool is an important factor that has received little attention. Traditionally, this is done centrally on the machine table, which leads to locally increased wear of the feed axis. Furthermore, positioning directly influences energy consumption during machining. Consequently, the longest possible component utilization through optimum wear and energy optimization creates a direct conflict of objectives. To solve this conflict, this paper presents an automated approach for software-defined workpiece positioning and NC-Code optimization regarding the axis-specific energy consumption and the spindle condition of ball screws. An approach for mapping the energy consumption and the directly measured spindle condition is presented. Both represent input variables of the cost function. Approaches for the optimization of the position as well as for the practical implementation are proposed.

2

Cyber-physical test environment for the identification of interacting wear effects in feed axes

Bott Alexander, Stöbel Robin, Osen Gregor, Fleischer Jürgen

Journal of Machine Engineering

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2023

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Vol. 23, No. 1

123--132

EN

For a comprehensive optimization and control of production processes, cyber-physical systems are necessary to include machines' time-dependent properties. These wear effects in machine tools, especially the feed axes, can significantly influence the process quality and are a steady research focus. However, the interaction of wear effects between different feed axes has received little attention. Especially models that represent the combined wear influence of different interacting feed axes on the control parameters and machine dynamics hold great potential. To close this knowledge gap, this paper proposes a cyber-physical test environment to identify the interaction of wear effects in feed axes. For this test environment, the relevant boundary conditions of different feed axes in machine tools and their systematic interaction are presented. Through these conditions, a physical test setup is derived and, analogous to this, a virtual model is created. This holistic approach represents the physical and virtual interaction between different components.

3

Misalignment detection on linear feed axis with FFT and statistical analysis using motor current

Demetgül Mustafa, Gu Minjie, Hillenbrand Jonas, Zhao Yicheng, Gönnheimer Philipp, Fleischer Jürgen

Journal of Machine Engineering

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2022

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Vol. 22, No. 2

31--42

EN

The linear feed axes are critical subsystems in many production machines and have important responsibilities such as transporting workpieces and tools in the process. Therefore, the component’s working condition is crucial for the production of high-quality products. Because these systems gradually deteriorate, it is necessary to detect these changes and occurring faults with condition monitoring systems. In this study, the motor current of feed axes is monitored for axis misalignment that occurs during or after assembly. We conduct diagnosis with Fast Fourier Transform (FFT) and statistical methods in order to differentiate different misalignment scenarios and operating constraints of the feed axis. Different states are achieved by simulating left and right axis misalignment and operating the table at different speeds and strokes.

4

Unsupervised detection of state changes during operation of machine elements

Hillenbrand Jonas, Fleischer Jürgen

Journal of Machine Engineering

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2021

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Vol. 21, No. 2

35--46

EN

Interpretation of sensor data from machine elements is challenging, if no prior knowledge of the system is available. Evaluation methods must adapt surrounding conditions and operation modes. As supervised learning models can be time-consuming to set up, unsupervised learning poses as alternative solution. This paper introduces a new clustering scheme that incorporates iterative cluster retrieval in order to track the clustering results over time. The approach is used to identify changing machine element states such as operating conditions and undesired changes, like incipient damage or wear. We show that knowledge about the evolving clusters can be used to identify operation and failure events. The approach is validated for machine elements with slide and roll contacts, such as ball screws and bearings. The data used has been captured using vibration and acoustic emission sensors. The results show a general applicability to the unsupervised monitoring of machine elements using the proposed approach.

5

Seamless and modular architecture for autonomous machine tools

Fleischer Jürgen, Puchta Alexander, Gönnheimer Philipp

Journal of Machine Engineering

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2021

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Vol. 21, No. 3

40--46

EN

In machine tools, existing solutions for process monitoring and condition monitoring rely on additional sensors or the machine control system as data sources. For a higher level of autonomy, it becomes necessary to combine several data sources, which may be within or outside of the machine. Another requirement for autonomy is additional computing power, which may be hosted on edge devices or in the cloud. A seamless and modular architecture, where sensors are integrated in smart machine components or smart sensors, which are in turn connected to edge devices and cloud platforms, provides a good basis for the incremental realisation of autonomy in all phases of the machine life cycle.

6

Analysis of Basis Weight Uniformity Indexes for the Evaluation of Fiber Injection Molded Nonwoven Preforms

Moll Patrick, Wang Shaofan, Coutandin Sven, Fleischer Jürgen

Autex Research Journal

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2021

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Vol. 21, no. 3

341--351

EN

Fiber injection molding is an innovative approach for the manufacturing of nonwoven preforms but products currently lack a homogeneous fiber distribution. Based on a mold-integrated monitoring system, the uniformity of the manufactured preforms will be investigated. As no universally accepted definition or method for measuring uniformity is accepted yet, this article aims to find a suitable uniformity index for evaluating fiber injection molded nonwovens. Based on a literature review, different methods are implemented and used to analyze simulated images with given distribution properties, as well as images of real nonwovens. This study showed that quadrant-based methods are suitable for evaluating the basis weight uniformity. It has been found that the indexes are influenced by the number of quadrants. Changes in sample size do not affect the indexes when keeping the quadrant number constant. The quadrants-based calculation of the coefficient of variation showed the best suitability as it shows good robustness and steady index for varying degrees of fiber distribution.