In the present work, the dynamic characterization of carbon nano tubes (CNT)‐reinforced GFRP composite beam under elevated temperature has been investigated experimentally. GFRP composite beams without and with CNT reinforcement have been fabricated using vacuum‐assisted hand lay‐up technique. Natural frequencies and damping ratio of the composite beams for the first three modes are being measured under clamped‐free and clamped–clamped end conditions using experimental modal analysis. Further, the effect of variation of temperature of the GFRP composite beams without and with CNT reinforcement on natural frequencies and damping ratio have been studied. It was observed that the fundamental natural frequency of the CNT‐GFRP hybrid composite beam is approximately 17.43% and 18.46% higher than that of GFRP composite beams without CNT reinforcement under CF and CC boundary conditions, respectively. The fundamental natural frequency of CNT‐GFRP composite beam decreases by 3.47% and 11.19%, whereas the damping ratio at first mode increases by 22.64% and 35.41% under clamped‐free (CF) and CC boundary conditions, respectively when the temperature increased from 30°C to 60°C. POLYM. COMPOS., 40:464–470, 2019. © 2017 Society of Plastics Engineers
This paper deals with the design and development of a silver–polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver–polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability.
Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers.
Delamination in laminated structures is a concern in high-performance structural applications, which challenges the latest non-destructive testing techniques. This study assesses the delamination damage in the glass fiber-reinforced laminated composite structures using structural health monitoring techniques. Glass fiber-reinforced rectangular laminate composite plates with and without delamination were considered to obtain the forced vibration response using an in-house developed finite element model. The damage was diagnosed in the laminated composite using machine learning algorithms through statistical information extracted from the forced vibration response. Using an attribute evaluator, the features that made the greatest contribution were identified from the extracted features. The selected features were further classified using machine learning algorithms, such as decision tree, random forest, naive Bayes, and Bayes net algorithms, to diagnose the damage in the laminated structure. The decision tree method was found to be a computationally effective model in diagnosing the delamination of the composite structure. The effectiveness of the finite element model was further validated with the experimental results, obtained from modal analysis using fabricated laminated and delaminated composite plates. Our proposed model showed 98.5% accuracy in diagnosing the damage in the fabricated composite structure. Hence, this research work motivates the development of online prognostic and health monitoring modules for detecting early damage to prevent catastrophic failures of structures.
The dynamic characterization of honeycomb sandwich structure with CNT reinforced hybrid composite face sheet is performed using Finite element simulation. The element simulations. The Potential and Kinetic energies of the honeycomb sandwich beam were derived using classical laminate beam theory and Mindlin theory. The governing equations of motion for the sandwich composite beam is made up of Nomex material with a hybrid composite face sheet reinforced with CNT are obtained by applying the Hamilton rule in the finite element model. The functionality of current finite element modeling has been investigated to proving the dynamic properties acquired from the improved finite element model with the solution obtainable in the previous literature. A further various parametric study is performed to understand the effect of CNT content, thickness ratio, and boundary constraints on the honeycomb sandwich composition of dynamic properties with reinforced CNT of the hybrid composite face sheet is performed using finite element simulation. The CNT content in the honeycomb sandwich beam containing a remarkable effect on the natural frequency.
The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis experimental setup. The vibration signals under different fault conditions were acquired from the setup using an accelerometer. The condition monitoring of the hydraulic brake system using the vibration signal was processed using a machine learning approach. The machine learning approach has three phases, namely, feature extraction, feature selection, and feature classification. Histogram features were extracted from the vibration signals. The prominent features were selected using the decision tree. The selected features were classified using a fuzzy classifier. The histogram features and the fuzzy classifier combination produced maximum classification accuracy than that of the statistical features.
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