Browse By Repository:

 
 
 
   

Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm

Minhat, Mohamad and Abd Rahman, Md Nizam and Abbas, Adnan Jameel (2014) Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm. In: iDECON 2014 – International Conference on Design and Concurrent Engineering, 21 - 23 September 2014, Melaka.

[img] Microsoft Word (Cutting parameters optimization of mild steel via AIS Heuristic Algorithm)
idecon2014_submission_126_(1).docx - Published Version
Restricted to Repository staff only until 1 April 2020.

Download (339Kb)

Abstract

The minimum cost and higher productivity represent the main challengers in recent Industrial renaissance. Selecting the optimal cutting parameters play a big role in achieving these aims. Heat generated in cutting zone area is an important factor affects on work piece and cutting tool properties. The surface finish quality specifies the product success and integrity. In this paper, the temperature generated in cutting zone (shear zone and chip-tool interface zone) and work piece surface roughness will be optimized. The results analysis achieved using Artificial Immune System (AIS) intelligent algorithm. A mild steel type (S45C) work piece and tungsten insert cutting tool type (SPG 422) via dry CNC turning operation used in experimental results. The optimum cutting parameters (cutting velocity, depth of cut and feed rate) calculated by (AIS) algorithm to obtain the simulated and ideal cutting temperature and surface roughness. An infrared camera type (Flir E60) used for temperature measurement and a portable surface roughness device used for roughness measurement. The experimental results showed that the ideal cutting temperature (110 Cо) and surface roughness (0.49 µm) occurred at (0.3 mm) depth of cut , (0.06 mm) feed rate and (60 m/min) cutting velocity. AIS accuracy in finding the ideal cutting temperature and surface roughness is (91.7 %) and (89.2 %) respectively. The analysis showed that the predicted results compared with experimental are very close which referred that this intelligent system can be used to estimate the cutting temperature and surface roughness in the turning operation of mild steel.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TS Manufactures
Divisions: Faculty of Manufacturing Engineering > Department of Manufacturing Process
Depositing User: Dr. Mohamad Minhat
Date Deposited: 19 Nov 2014 01:33
Last Modified: 28 May 2015 04:32
URI: http://eprints.utem.edu.my/id/eprint/13545

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year