Browsing by Author "Gurumurthy, B.M."

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Mechanical Properties and Characterization of Hybrid Composition Reinforced with Natural Fibers
(Springer, 2024) Dayanand; Bheemanalli, A.; Sangamesh; Gurumurthy, B.M.; Ravishankar, K.S.
The current work involves fabrication of hybrid composite by using sisal and roselle natural fibers as reinforcing elements or fillers with epoxy resin (LAPOX L12) and Hardener or catalyst (K6) by hand lay-up method with a 35:75 ratio. Enhancement of mechanical properties in polymer hybrid composites is exhibited by the possible intermixture of roselle and sisal fibers [1]. The effect of loose and continuous fiber (CLFR) and woven mat fiber-reinforced (WMFR) hybrid composite laminates were tested to evaluate the mechanical and physical performance exhibited by them. Water absorption test along with thickness swelling test was carried out and the data was recorded for reference. The tensile strength and modulus of WMFR composite (dry) are reduced by 35% and 17%, respectively, and compressive strength and modulus of WMFR composite (dry) are reduced by 17% and 33%, respectively. It was also noticed that Erosion rate of the samples increases as the increase of sand particle size, sand concentration, and erosion rate is high in case CLFR (4.93%) composite. Water absorption is high in CLFR than in WMFR composite. SEM analysis revealed that fiber pull-out, de-bonding, matrix softening, fiber rupture, sliding tracks, debris, and cracks were the reasons for the failure of composites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Thrust and torque force analysis in the drilling of aramid fibre-reinforced composite laminates using RSM and MLPNN-GA
(2018) Anarghya, A.; Harshith, D.N.; Rao, N.; Nayak, N.S.; Gurumurthy, B.M.; Abhishek, V.N.; Patil, I.G.S.
Aramid Fibre Reinforced Plastic composites are difficult to be drilled due to anisotropic material properties. Currently, soft computing techniques are used as alternatives to conventional mathematical models, which is robust and can deal with inaccuracy and uncertainty. In this paper, drilling of Aramid Fibre Reinforced Plastics (AFRPs) was carried out using Taguchi L54 experimental layout. Drilling tool used in this experiment was solid carbide. The purpose of this study was to find optimum combination of drilling parameters to obtain minimum thrust and torque force to reduce the delamination. Also, this paper proposed a prediction model of Multilayer Perception Neural Network optimized by Genetic Algorithm (MLPNN-GA). Moreover, RSM technique was used to evaluate the influence of process parameters (spindle speed, feed rate, drill point angle and drill diameter on thrust force and torque. The prediction capability of both RSM and MLPNN-GA was compared with Response optimizer for thrust force and torque. The investigation demonstrated that drill point angle is the primary factor affecting thrust force and drill diameter influences the torque force on the drill bit. Overall, this study recommends the use of high speed and low feed combination and drill point angles of 90 118 to reduce the delamination of the materials in the drilling of AFRP composites. 2018 The Authors
Thrust and torque force analysis in the drilling of aramid fibre-reinforced composite laminates using RSM and MLPNN-GA
(Elsevier Ltd, 2018) Anarghya, A.; Harshith, D.N.; Rao, N.; Nayak, N.S.; Gurumurthy, B.M.; Abhishek, V.N.; Patil, I.G.S.
Aramid Fibre Reinforced Plastic composites are difficult to be drilled due to anisotropic material properties. Currently, soft computing techniques are used as alternatives to conventional mathematical models, which is robust and can deal with inaccuracy and uncertainty. In this paper, drilling of Aramid Fibre Reinforced Plastics (AFRPs) was carried out using Taguchi L54 experimental layout. Drilling tool used in this experiment was solid carbide. The purpose of this study was to find optimum combination of drilling parameters to obtain minimum thrust and torque force to reduce the delamination. Also, this paper proposed a prediction model of Multilayer Perception Neural Network optimized by Genetic Algorithm (MLPNN-GA). Moreover, RSM technique was used to evaluate the influence of process parameters (spindle speed, feed rate, drill point angle and drill diameter on thrust force and torque. The prediction capability of both RSM and MLPNN-GA was compared with Response optimizer for thrust force and torque. The investigation demonstrated that drill point angle is the primary factor affecting thrust force and drill diameter influences the torque force on the drill bit. Overall, this study recommends the use of high speed and low feed combination and drill point angles of 90°–118° to reduce the delamination of the materials in the drilling of AFRP composites. © 2018 The Authors