Analysis of the Vibration Characteristic of an Experimental Turning Lathe Using Artificial Neural Networks

Çetinkaya, Mehmet Bahadır; Esim, Emir; İşci, Muhammed

doi:10.1007/s13369-020-05162-6

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…The back propagation (BP) learning algorithm is basically based on the philosophy of feedback of the total error in a feedforward network structure to the neurons in the hidden layer for the purpose of increasing the success of training [28]. The effect of a neuron on related output is directly associated with the weight value of that neuron.…”

Section: Cascade-forward Back-propagation Artificial Neural Networkmentioning

confidence: 99%

Analysis of the Vibration Characteristics of a Leaf Spring System Using Artificial Neural Networks

Çetinkaya

İşci

2022

Sensors

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The real-time vibrations occurring in a leaf spring system may cause undesirable effects, such as stresses, strains, deflections, and surface deformations over the system. In order to detect the most appropriate working conditions in which the leaf spring system will work more stably and also to design optimized leaf spring systems, these external effects have to be detected with high accuracy. In this work, artificial neural network-based estimators have been proposed to analyze the vibration effects on leaf spring systems. In the experimental studies carried out, the vibration effects of low, medium, and high-pressure values applied by a hydraulic piston on a steel leaf spring system have been analyzed by a 3-axial accelerometer. After the experimental studies, the Radial Basis Artificial Neural Network (RBANN) and Cascade-Forward Back-Propagation Artificial Neural Network (CFBANN) based nonlinear artificial neural network structures have been proposed to analyze the vibration data measured from the leaf spring system under relevant working conditions. The simulation results represent that the RBANN structure can estimate the real-time vibrations occurring on the leaf spring system with higher accuracy and reaches lower RMS error values when compared to the CFBANN structure. In general, it can be concluded that the RBANN and CFBANN network structures can successfully be used in the estimation of real-time vibration data.

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Section: Cascade-forward Back-propagation Artificial Neural Networkmentioning

confidence: 99%

Analysis of the Vibration Characteristics of a Leaf Spring System Using Artificial Neural Networks

Çetinkaya

İşci

2022

Sensors

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Study of different cutting fluids effect on the coupling characteristics of milling noise-vibration and surface roughness of TA2 pure titanium

Li,

et al. 2024

Journal of Manufacturing Processes

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Analysis of the Vibration Characteristic of an Experimental Turning Lathe Using Artificial Neural Networks

Cited by 2 publications

References 21 publications

Analysis of the Vibration Characteristics of a Leaf Spring System Using Artificial Neural Networks

Analysis of the Vibration Characteristics of a Leaf Spring System Using Artificial Neural Networks

Study of different cutting fluids effect on the coupling characteristics of milling noise-vibration and surface roughness of TA2 pure titanium

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