Effect of Milling Parameters on Amplitude Spectrum of Vibrations during Milling Materials Based on Wood

Hortobágyi, Áron; Koleda, Peter; Koleda, Pavol; Kminiak, Richard

doi:10.3390/app13085061

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…In most cases, the vibration in ‘y’ is more dominant than the other two axes. On the other hand, the literature suggests that vibration in the ‘z’ axis is dominant and most sensitive to wear [ 13 , 14 , 15 ] and related to the depth of cut value. In this work, Inconel 625 was machined with a depth of cut of 0.15 mm, and the vibration was measured along the ‘z’ axis.…”

Section: Methodsmentioning

confidence: 99%

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Natarajan

Mohanraj

Sakthivel

et al. 2023

Sensors

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Exact observing and forecasting tool conditions fundamentally affect cutting execution, bringing further developed workpiece machining accuracy and lower machining costs. Because of the unpredictability and time-differing nature of the cutting system, existing methodologies cannot achieve ideal oversight progressively. A technique dependent on Digital Twins (DT) is proposed to accomplish extraordinary accuracy in checking and anticipating tool conditions. This technique builds up a balanced virtual instrument framework that matches entirely with the physical system. Collecting data from the physical system (Milling Machine) is initialized, and sensory data collection is carried out. The National Instruments data acquisition system captures vibration data through a uni-axial accelerometer, and a USB-based microphone sensor acquires the sound signals. The data are trained with different Machine Learning (ML) classification-based algorithms. The prediction accuracy is calculated with the help of a confusion matrix with the highest accuracy of 91% through a Probabilistic Neural Network (PNN). This result has been mapped by extracting the statistical features of the vibrational data. Testing has been performed with the trained model to validate the model’s accuracy. Later, the modeling of the DT is initiated using MATLAB-Simulink. This model has been created under the data-driven approach. The physical–virtual balance of the DT model is acknowledged utilizing the advances, taking into consideration the detailed planning of the constant state of the tool’s condition. The tool condition monitoring system through the DT model is deployed through the machine learning technique. The DT model can predict the different tool conditions based on sensory data.

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Section: Methodsmentioning

confidence: 99%

Digital Twin-Driven Tool Condition Monitoring for the Milling Process

Natarajan

Mohanraj

Sakthivel

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the wood industry, particularly among manufacturers of furniture and construction joinery, the amount of wear that cutting tools sustain over their lifetime has important implications for both technology and cost [36][37][38]. Wilkowski and colleagues [39] performed ion implantation as part of their research on circular saw blades and wooden door frame production.…”

mentioning

confidence: 99%