Surface Roughness Study on Forged Al-TiB2 Composite by Regression Analysis
Hemavathy S1, C N Chandrappa2, Prashanth Kumar K C3
1Hemavathy S, Assistant Professor, Department of Mechanical Engineering, RIT, (Karnataka), India.
2Dr. C N Chandrappa, Professor and Head, Department of Automobile Engineering, Archarya Institute of Technology, (Karnataka), India.
3Prashanth Kumar K C, Student, Department of Mechanical Engineering, RIT, (Karnataka), India.
Manuscript received on 09 February 2019 | Revised Manuscript received on 22 February 2019 | Manuscript Published on 04 March 2019 | PP: 480-486 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2087017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Aluminum composites are very rapidly replacing engineering metals and alloys because of its light weight and high strength in aerospace and biomedical applications etc. In the present work Al-TiB2 (Aluminum alloy A2024) composite is fabricated by In-Situ technique. The serious examination on impact of surface roughness of Aluminum TiB2 composite is done. The material is subjected for turning operation to contemplate the surface roughness. This investigation centers around building up an exact model for expectation of surface unpleasantness on manufactured composite. The working parameters are speed, feed, depth of cut and tool nose radius. One of the data mining techniques non-linear regression analysis is applied in developing the empirical model, this model is transferred to software by visual basic programming language. The test results show that the value of surface roughness is low at high cutting speed and comparatively high at low cutting speed. Surface roughness increases with increase in feed and depth of cut. However it decreases with increasing tool nose radius and surface roughness increases as what% of TiB2 increases in aluminum. The values of surface roughness of models compared with experimental results. This models developed in this study have a satisfactory compatibility in both model construction and verification and there is a scope for future work.
Keywords: Al 2024 alloy, In-situ Technique, Surface Roughness, Regression Analysis, Tool Nose Radius.
Scope of the Article: Composite Materials