Department of Mechanical engineering

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Author: search.filters.author.Sharma, Panchagnula JayaprakashSubject: search.filters.subject.Acoustic emission

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    Effect of Cutting Environments on Drilling Induced Damage in GFRP Nanocomposites
    (ASME, 2021-02) Sharma, Panchagnula Jayaprakash
    Drilling is most commonly used secondary machining process for structural joining of Glass Fiber Reinforced Plastic (GFRP) composites. Performing drilling operations on GFRPs/Multi-Walled CarbonNanoTubes (MWCNTs) reinforced GFRPs is really a challenging task due to their non-homogeneity and anisotropic behavior, which directs to generation of material damages. The prime focus of current work is to identify the suitable process parameters for enhancing the performance of drilling of GFRP nanocomposites. In this study, the drilling experiments are conducted on 0.3wt.% MWCNT-GFRP nanocomposites with solid carbide, TiCN and TiAlN coated drills (6mm diameter) under dry and chilled air cutting environments. The dry drilling experiments are conducted without any assistance of cooling fluid in ambient condition. The chilled air at a temperature of 3°C was supplied from the vortex tube. Experimental data is used for ANOVA (balanced) analysis. The cutting parameters such as feed rate, cutting speed and tool type (coating) are considered as input and the measured thrust force, delamination factor and AE RMS signal are treated as output responses. From ANOVA results, it is observed that the influence of feed rate is more on thrust force as compared to cutting speed. The coefficients of determination (R2) shows good fit between thrust force and cutting parameters and the corresponding confidence levels are above 98% for all cutting environments. Similarly, R2 values of delamination factor and AE RMS signals are above 90% and 96% respectively. The minimum thrust force and torque values are noted as 12.61 N and 0.152 N-m respectively at lower feed rate (10 mm/min) and higher cutting speed (1500 RPM) using TiCN coated drill under chilled air cutting environment. The delamination factor is also low (1.025) under the same cutting conditions of minimum cutting forces. A good correlation exists between the thrust force vs. delamination factor (> 0.85) and the delamination factor vs. AE RMS signal (> 0.80) for the selected cutting environments. The recommended range of RMS voltage is 0.083 to 0.121 volts for producing the delamination free holes on GFRP nanocomposites.
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    Prediction of drilling induced delamination and circularity deviation in GFRP nanocomposites using deep neural network
    (Elsevier, 2022) Sharma, Panchagnula Jayaprakash; Jasti, Naga Vamsi Krishna
    Drilling of Glass Fiber Reinforced Polymer (GFRP) nanocomposites is most prevailing topic to understand the composite behaviour under different cutting conditions. The present study is mainly focused on prediction of drilling output responses such as delamination factor and circularity error randomly with the help of deep neural network (DNN) model. L9 orthogonal array is used for experimentation. Drilling operation is performed on 0.3 wt% multi-walled carbon nano tubes reinforced GFRPs with solid carbide, TiCN and TiAlN coated (6 mm- diameter) twist drills. Based on experimental results, two different deep neural network models are prepared with single and double hidden layers by varying node numbers such as 8, 16, 32, 64, and 128. Thrust force, Acoustic Emission RMS voltage, and drill type (coating) are considered as input to the neural network and delamination factor at exit, circularity error are treated as predicted output responses for the given network model. The revealed predicted results recommended that two hidden layers with 32 nodes network model give the lowest absolute error of 0.08% and 3.13% in delamination factor and circularity errors respectively. Similarly, the highest absolute error is identified as 4.19% in delamination factor and 13.14% in circularity error by single hidden layer with 128 nodes. Therefore, it is urged that DNN is the most suitable modelling technique for prediction of drilling responses on GFRP nano composites.