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Analysis on two wheeler chassis frame of e-bike subjected to static and impact loads

100%

Konada N. K. , Suman K. N. S.

Journal of Mechanical and Energy Engineering

2020

tom Vol. 4, No 1

15--26

The future of automotive industry is to design and develop electrical vehicles to control the emissions released from gaseous fuels and not to release any harmful gases in to atmosphere. In this work, an attempt was made to analyze the behavior of two wheeler E- bike with alternative materials of frame such as Aluminum alloy (Al-A), Titanium alloy (Ti-A), Grey cast iron (G-CI), Carbon fiber epoxy (CF-E), and Structural steel (ST-S) and compared with AISI-1020 material. The frame of Yamaha R15 is initially modeled by using solid work and imported to Ansys. Static analysis was performed by applying a load of 1500 N on the frame and Impact analysis was performed by applying velocity of 27.7 m/s along X direction. The values of equivalent von Mises stress and total deformation for all the materials are observed. After performing static analysis, it was observed that, CF- Epoxy material exhibited higher value of Equivalent von Mises stress (7.659*107 Pa) and lower total deformation of (0.304×10-6 mm) compared to remaining materials. Based on impact analysis , it was observed that, CF-E exhibited better von Mises stress of (3994.9 MPa) closer to AISI 1020 and structural steel materials and total deformation is observed to be lower value (0.1106 mm) compared to remaining materials.

Optimizing cutting parameters in hard turning of AISI 52100 steel using TOPSIS approach

80%

Umamaheswarrao P. , Ranga Raju D. , Suman K. N. S. , Ravi Sankar B.

Journal of Mechanical and Energy Engineering

2019

tom Vol. 3, No 3

227--232

In the present work optimization of cutting parameters is performed while hard turning of AISI 52100 steel with polycrystalline cubic boron nitride (PCBN) tools using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Experiments are planned and conducted based on Center Composite Rotatable Design (CCD) of the Response Surface Method (RSM). Cutting speed, feed, depth of cut, nose radius and negative rake angle are considered as input parameters. In this study machining force (F) and surface roughness (Ra) are measured during the experiment. Analysis of variance (ANOVA) is deployed to determine the influence of process parameters. Obtained optimal parameters are speed 200 rpm, feed 0.1 mm/rev, depth of cut 0.8 mm, nose radius 1.2 mm and negative rake angle 45º.