Vibration monitoring of CNC machinery using MEMS sensors

Wszołek, G.; Czop, Piotr; Słoniewski, Jakub; Dogrusoz, Halit

doi:10.21595/jve.2019.20788

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…The validity of an efficient one-bay FE model of the inter-story floor, calibrated to the available experimental feedback, is discussed in this paper. Differing from past literature studies on machine-induced vibrations on rigid foundation systems (i.e., [9][10][11][12]), or focused on the machinery characterization only (i.e., [15][16][17]), the attention of the experimental analysis is in fact focused both on the movable components of the machines and on the corresponding response of the primary structure. Based on a dedicated experimental program, the final result takes the form of a synthetized signal for the CNC machine that can be used for refined numerical analyses of the primary system.…”

Section: Research Topicmentioning

confidence: 99%

Efficiency of Coupled Experimental–Numerical Predictive Analyses for Inter-Story Floors Under Non-Isolated Machine-Induced Vibrations

2020

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Machine-induced vibrations represent, for several reasons, a crucial design issue for industrial buildings. At the early design stage, special attention is thus required for the static and dynamic performance assessment of the load-bearing members, given that they should optimally withstand ordinary design loads but also potentially severe machinery operations. The knowledge and reliable description of the input vibration source is a key step, similarly to a reliable description of the structural system, to verify. However, such a kind of detailing is often unavailable and results in a series of simplified calculation assumptions. In this paper, a case-study eyewear factory built in 2019 is investigated. Its layout takes the form of a two-story, two-span (2 × 14.6 m) precast concrete frame (poor customer/designer communication on the final equipment resulted in various non-isolated computer numerical control (CNC) vertical machines mounted on the inter-story floor, that started to suffer from pronounced resonance issues. Following past experience, this paper investigates the validity of a coupled experimental–numerical method that could be used for efficient assessment predictive studies. Based on on-site experiments with Micro Electro-Mechanical Systems (MEMS) accelerometers mounted on the floor and on the machine (spindle included), the most unfavorable machine-induced vibration sources and operational conditions are first characterized. The experimental outcomes are thus used to derive a synthetized signal that is integrated in efficient one-bay finite element (FE) numerical model of the floor, in which the machine–structure interaction can be taken into account. The predictability of marked resonance issues is thus emphasized, with a focus on potential and possible limits of FE methods characterized by an increasing level of detailing and computational cost.

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Section: Research Topicmentioning

confidence: 99%

Efficiency of Coupled Experimental–Numerical Predictive Analyses for Inter-Story Floors Under Non-Isolated Machine-Induced Vibrations

2020

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“…In a recent study the authors used a MEMS installed sensor on a CNC machine with Fanuc controller to measure vibrations for maintenance purposes [19]. First, they carried out three case studies: optimal cutting values with a worn-out tool, cutting values with a new tool that breaks and does not break.…”

Section: Cnc Machine Condition Monitoring Systemmentioning

confidence: 99%

IIoT Machine Health Monitoring Models for Education and Training

Singh¹,

Hodgins²,

Gadhrri³

et al. 2022

IoT Applications Computing

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IoT, IIoT and Industry 4.0 technologies are leading the way for digital transformation in manufacturing, healthcare, transportation, energy, retail, cities, supply chain, agriculture, buildings, and other sectors. Machine health monitoring and predictive maintenance of rotating machines is an innovative IIoT use case in the manufacturing and energy sectors. This chapter covers how machine health monitoring can be implemented using advanced sensor technology as a basis for predictive maintenance in rotating devices. It also covers how sensor data can be collected from the devices at the edge, preprocessed in a microcontroller/edge node, and sent to the cloud or local server for advanced data intelligence. In addition, this chapter describes the design and operation of three innovative models for education and training supporting the accelerated adoption of these technologies in industry sectors.

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“…Wszoek et al. applied the wavelet method to spindle vibration monitoring and developed a spindle vibration monitoring system with strong anti‐interference ability [19]. Casamenti et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…munication, and used the method of vibration signal analysis to judge the working state of machinery[17,18]. Wszoek et al applied the wavelet method to spindle vibration monitoring and developed a spindle vibration monitoring system with strong anti-interference ability[19]. Casamenti et al studied the influence of load, position, and instantaneous acceleration on the measured signal, and proposed a machine tool condition monitoring system that can trace the fault source[20,21].…”

mentioning

confidence: 99%

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

Zhu

Samori

2022

The Journal of Engineering

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The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time-frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time-frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the timefrequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time-frequency characteristic quantity in real time.

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Vibration monitoring of CNC machinery using MEMS sensors

Cited by 14 publications

References 7 publications

Efficiency of Coupled Experimental–Numerical Predictive Analyses for Inter-Story Floors Under Non-Isolated Machine-Induced Vibrations

Efficiency of Coupled Experimental–Numerical Predictive Analyses for Inter-Story Floors Under Non-Isolated Machine-Induced Vibrations

IIoT Machine Health Monitoring Models for Education and Training

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

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