Vibrational damping behaviors of graphene nanoplatelets reinforced NR/EPDM nanocomposites

Mohamad, Noraiham; Yaakub, Juliana; Maulod, Hairul Effendy Ab; Jeefferie, Abd Razak; Yuhazri, M. Y.; Lau, Kin Tak; Ahsan, Q.; Shueb, Mohamad Iqbal; Othman, Raja Nor Izawati Raja

doi:10.15282/jmes.11.4.2017.28.0294

Cited by 22 publications

(10 citation statements)

References 24 publications

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“…There are numerous number of studies carried out to improve the properties of engine mounting to isolate the engine vibration, optimizing the engine performance, extending the engine's life span and saving power consumption [2,[5][6]. Our previous studies on GNPs reinforced NR/EPDM blends proven to increase the heat dissipation [4] and improve vibration damping [7]; reflecting their potential to be used in the engine mounting. Unfortunately, there are currently no available research investigating the effect of thermal aging on tensile properties of both NR/EPDM blends and nanocomposites.…”

Section: Introductionmentioning

confidence: 99%

Tensile Properties Degradation of Thermally Aged NR/EPDM Blend and Nanocomposites for Engine Mounting

Karim¹,

Mohamad²,

Lin³

et al. 2020

MJCSM

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Nowadays, NR/EPDM have drawn great attention to most researchers to be utilized in an engine mounting. In service, majority of structures and components are subjected to service condition such as heat, liquids and stress of dynamic loading. This research is an effort to compare the thermal effects on physical and mechanical properties of NR/EPDM blend and NR/EPDM filled graphene nanoplatelets (GNPs) for the potential usage in the engine mounting. The blends and nanocomposites were prepared through a melt compounding using a Banbury Internal Mixer, exposed to thermal cycles and tensile tested. The materials were heated alternately for 10 minutes at 60℃ and 120℃ for 0, 35, 70 and 150 cycles. Thermally aged NR/EPDM nanocomposites showed higher performance under tensile stress if compared to NR/EPDM blend. The tensile strength and elastic modulus were consistently 40% higher than the strength of the blend. This is due to the reinforcing effects of GNPs and their good interaction with the matrix. The reduction of tensile properties was due to the embrittlement effect and in line with the increase in amorphous phases from the broadening of XRD spectra. This has proven that the NR/EPDM nanocomposites can withstand thermal cycles, oils and fluctuating stress better than NR/EPDM blend and exhibited higher potential to be utilized as the engine mount material.

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Section: Introductionmentioning

confidence: 99%

Tensile Properties Degradation of Thermally Aged NR/EPDM Blend and Nanocomposites for Engine Mounting

Karim¹,

Mohamad²,

Lin³

et al. 2020

MJCSM

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“…In order to avoid any sort of equipment failures, it is very important to have a good prevention strategy. Vibration is a powerful tool that helps to provide a good information regarding the state of the assets either equipment or structure [4][5][6][7]. For machinery monitoring, vibration-based is one of the most popular techniques used in order to maintain and sustain industrial assets due to its simple, low-cost and easiness of implementation [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

Isham

Leong

Hee

et al. 2019

JMES

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Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.

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“…Formerly, in fault diagnosis, CNN is only applied to automatically select features from the generated vibration signal which is in onedimensional input of time domain. Vibration signals provide a useful information regarding the state of the assets either equipment or structure [7][8][9]. Although the direct usage of CNN on the generated vibration signals is widely used and step-less, only time data could be extracted whereas the significant spatial data of the signals which in this case is the sequence of frequency as stated by Janssens et al [10] could not be interpreted.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

Waziralilah

Abu

Lim

et al. 2019

JMES

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The vast impact on machinery that is rooted by bearing degradation thus pinpointing bearing fault diagnosis as indubitably very crucial. The research is innovated to diagnose the fault in bearing by implementing deep learning approach which is Convolutional Neural Network (CNN) that has superiority over image processing and pattern recognition. A novel model comprises of Gabor Transform and CNN is proposed whereby Gabor Transform is utilized in representing the raw vibration signals into its image representation. The CNN architecture is augmented for a better accuracy of the bearing fault diagnosis model. To date, the method combination has never been deployed in establishing fault diagnosis model. Plus, the usage of Gabor Transform in mechanical area especially in bearing fault diagnosis is meagrely reported. Scant researches in mechanical diagnosis are dedicated to work on the image representation of the vibration data whereas the CNN works better when fed by images input due to its unique strength of CNN in images processing and spatial awareness. At the end of the research, it is perceived that the proposed model comprises of Gabor Transform and CNN can diagnose the bearing faults with 100% accuracy and perform better than when CNN is fed with raw signals.

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Vibrational damping behaviors of graphene nanoplatelets reinforced NR/EPDM nanocomposites

Cited by 22 publications

References 24 publications

Tensile Properties Degradation of Thermally Aged NR/EPDM Blend and Nanocomposites for Engine Mounting

Tensile Properties Degradation of Thermally Aged NR/EPDM Blend and Nanocomposites for Engine Mounting

Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

Bearing fault diagnosis employing Gabor and augmented architecture of convolutional neural network

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