Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm

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. Th...

Full description

Saved in:
Bibliographic Details
Main Authors: Minhat, Mohamad, Abd Rahman, Md Nizam, Abbas, Adnan Jameel
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/13545/1/idecon2014_submission_126_%281%29.docx
http://eprints.utem.edu.my/id/eprint/13545/
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknikal Malaysia Melaka
Language: English
id my.utem.eprints.13545
record_format eprints
spelling my.utem.eprints.135452015-05-28T04:32:33Z http://eprints.utem.edu.my/id/eprint/13545/ Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm Minhat, Mohamad Abd Rahman, Md Nizam Abbas, Adnan Jameel TJ Mechanical engineering and machinery TS Manufactures 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. 2014-09-21 Conference or Workshop Item PeerReviewed application/msword en http://eprints.utem.edu.my/id/eprint/13545/1/idecon2014_submission_126_%281%29.docx 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.
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle TJ Mechanical engineering and machinery
TS Manufactures
Minhat, Mohamad
Abd Rahman, Md Nizam
Abbas, Adnan Jameel
Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
description 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.
format Conference or Workshop Item
author Minhat, Mohamad
Abd Rahman, Md Nizam
Abbas, Adnan Jameel
author_facet Minhat, Mohamad
Abd Rahman, Md Nizam
Abbas, Adnan Jameel
author_sort Minhat, Mohamad
title Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
title_short Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
title_full Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
title_fullStr Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
title_full_unstemmed Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
title_sort cutting parameters optimization of mild steel via ais heuristics algorithm
publishDate 2014
url http://eprints.utem.edu.my/id/eprint/13545/1/idecon2014_submission_126_%281%29.docx
http://eprints.utem.edu.my/id/eprint/13545/
_version_ 1665905549527482368