The Outcome of Turning Factors on the Machining Characteristics While Turning 655M13 Steel Alloy using Tialn Coated Carbide Insert
Venkatesh.P1, C.Ramesh Kannan2, Milon Selvam Dennison3
1Venkatesh.P*, Research Scholar, Department of Mechanical Engineering, Karpagam Academy of Higher Education, Coimbatore-641021, INDIA.
2C.Ramesh Kannan, Associate Professor, Mechanical Engineering, Karpagam Academy of Higher Education, INDIA.
3Milon Selvam Dennison, Mechanical Engineering, Kampala International University, Kampala, UGANDA.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1251-1260 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5416029320 /2020©BEIESP | DOI: 10.35940/ijeat.C5416.029320
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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: This exploration is carried out to reveal the outcome of turning factors such as cutting velocity, depth of cut and feed rate on the surface roughness, mean cutting force and tool-work interface temperature on turning cylindrical 655M13 steel alloy components. The experiments are designed based on (33) full factorial design and conducted on a turning centre with Titanium Aluminium Nitride (TiAlN) layered carbide tool of 0.8mm nose radius, simultaneously cutting forces such as feed force, thrust force and tangential force and the tool-work interface temperature are observed using calibrated devices. The surface roughness of the turned steel alloy parts is deliberated by means of a precise surface roughness apparatus. Prediction models are created for average surface roughness, mean cutting force and tool-work interface temperature by nonlinear regression examination with the aid of MINITAB numerical software. The optimum machining conditions are confirmed with the aid of a Genetic Algorithm. The outcome of each turning factor on the surface roughness, mean cutting force and tool-work interface temperature is studied and presented accordingly.
Keywords: 655M13; Lathe; Surface roughness; cutting force; TiAlN; Genetic Algorithm; Regression analysis