Surface Roughness Measurement of Machined Surfaces by Machine Vision Technique
P. Naresh1, Syed Altaf Hussain2, B. Durga Prasad3
1P. Naresh, Research Scholar, Department of Mechanical Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu (Andhra Pradesh), India.
2Syed Altaf Hussain, Professor, Department of Mechanical Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal (Andhra Pradesh), India.
3B. Durga Prasad, Professor, Department of Mechanical Engineering, Jawaharlal Nehru Technological University Anantapur, Anantapuramu, (Andhra Pradesh), India.
Manuscript received on 27 February 2019 | Revised Manuscript received on 14 March 2019 | Manuscript Published on 17 March 2019 | PP: 129-134 | Volume-7 Issue-ICETESM18, March 2019 | Retrieval Number: ICETESM31|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: Aluminium material is extremely used in many manufacturing industry sectors, there are several uses of aluminium, i.e., formability, high strength, thermal resistance, flexibility and high resistance towards corrosion. The advantages in different engineering fields demand various machining operations to be performed on metal matrix composites (MMCs). In the present investigation, the machine vision technique is used to observe roughness of surface in turning of MMCs composites. The process parameters considered are weight percentage of titanium carbide, feed, cutting speed and depth of cut. Experiments are planned and executed according to central composite design technique on all geared lathe machine using TiCN-Al2O3 tool insert. During machining operation the machining surfaces are captured through machine vision technique. A program is written in MATLAB which is used to analyze Ga values for bitmap of captured images and stylus probe instrument is used to estimate surface finish of machined surfaces.
Keywords: Central Composite Design, Machining, Machine Vision Technique, Surface Roughness.
Scope of the Article: Machine Design