CNC Milling Tool Head Imbalance Prediction Using Computational Intelligence Methods

Żabiński, Tomasz; Mączka, Tomasz; Kluska, Jacek; Kusy, Maciej; Gierlak, Piotr; Hanus, Robert; Prucnal, S.; Sęp, Jarosław

doi:10.1007/978-3-319-19324-3_45

Cited by 13 publications

(9 citation statements)

References 8 publications

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“…For example, sensors (E2.1) are used to collect data concerning energy consumption by machines, temperature of working engines, vibration of working equipment, condition of equipment construction elements, etc. [150,151]. The collected data is analyzed to identify the anomalies that can lead to failures, threaten the health and life of employees, cause additional costs or ecological disasters.…”

Section: Big Data and Analyticsmentioning

confidence: 99%

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

et al. 2021

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The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

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Section: Big Data and Analyticsmentioning

confidence: 99%

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

et al. 2021

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“…Vibration sensors have been selected due to the relatively easy installation of them in industrial CNC machines (in relation to force sensors) and the lower impact on system operation by external disturbances (in relation to acoustic emission). Motor current sensors have not been used in this study, as it is a continuation of previous authors' work focusing on the use of vibration sensors [25].…”

Section: Testbed and Experiments Descriptionmentioning

confidence: 99%

“…4. From the authors' experience and other works, it is clear that signal features in the frequency domain instead of features in the time domain should be considered for building a classifier [29], [30], [25], [24]. Thus, the vector Z = [Z (k) , .…”

Section: Input Data Preparation For Aann Implementationmentioning

confidence: 99%

FPGA-Embedded Anomaly Detection System for Milling Process

et al. 2021

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The main goal of this work is to design a supervising controller able to detect an anomaly in the milling process and implement the soultion in Field Programmable Gate Array (FPGA) chip. Executing this task, the controller continuously monitors the vibration signal coming from the acceleration sensor, installed on the milling machine, and striving to isolate new vibration patterns which are different from typical patterns recorded for the correct milling process. The detection method relies on determining selected signal features in the frequency domain and applying an auto-associative neural network (AANN) for novelty detection. It has been shown that by exercising the frequency spectrum of the vibration signal, extracting specific features of the signal's spectrum, and using an auto-associative neural network, it is possible to detect anomalies in a milling process with relatively high efficiency. The accuracy, sensitivity, specificity, precision, and false alarm rate are equal to 94.3, 100, 91.2, 88.9, and 8.8 percent. All necessary calculations can be accomplished by the developed single-chip FPGA embedded supervising controller. The controller allows high-speed calculations under low power consumption. It characterizes high reliability and low price compared to typically encountered solutions.

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“…The use of robots and the Industry 4.0 concept implies the need for advanced diagnostic systems including automatic detection of cutting tool failures, which is the subject of many research works [14][15][16][17][18][19][20]. The CIM (computer-integrated machining) systems which are currently in use require exchange of information not just about a single operation, but a continuous sequence of operating events that lead to manufacturing of a finished product.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Industrial Robot Tool Damage Based on Force Measurement

et al. 2020

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This paper proposes a solution for diagnosing the condition of the tools applied in metal alloy robotic machining. Determination of the condition of a cutting tool requires specific methods that depend on the damage type or the wear level. Methods that involved measurements of basic geometric dimensions of cutting tools based on force sensor, and testing the forces of inertia caused by tool unbalance were applied in the work covered herein. Damage of cutting tool, including unbalance, fracture or complete tool wear were detected with a system of force measurement, which was part of the standard equipment of the robot in a process used to investigate the methods covered herein. All methods proposed herein were performed automatically and require no human intervention. This paper presents examples of solutions for determining the condition of a grinding pin, which is a common machining tool.

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CNC Milling Tool Head Imbalance Prediction Using Computational Intelligence Methods

Cited by 13 publications

References 8 publications

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

FPGA-Embedded Anomaly Detection System for Milling Process

Automatic Detection of Industrial Robot Tool Damage Based on Force Measurement

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