Abstract
In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex machining optimization problems.
| Original language | English (US) |
|---|---|
| Title of host publication | Transactions of the North American Manufacturing Research Institute of SME 2006 - Papers Presented at NAMRC 34 |
| Pages | 611-618 |
| Number of pages | 8 |
| State | Published - Sep 21 2006 |
| Event | 34th North American Manufacturing Research Conference - Milwaukee, WI, United States Duration: May 23 2006 → May 26 2006 |
Publication series
| Name | Transactions of the North American Manufacturing Research Institute of SME |
|---|---|
| Volume | 34 |
Other
| Other | 34th North American Manufacturing Research Conference |
|---|---|
| Country | United States |
| City | Milwaukee, WI |
| Period | 5/23/06 → 5/26/06 |
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All Science Journal Classification (ASJC) codes
- Mechanical Engineering
- Industrial and Manufacturing Engineering
Keywords
- Multi-objective optimization
- Neural networks and hard turning
- Particle swarm optimization
Cite this
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Swarm-intelligent neural network system (SINNS) based multi-objective optimization of hard turning. / Karpat, Yiǧit; Özel, Tuǧrul.
Transactions of the North American Manufacturing Research Institute of SME 2006 - Papers Presented at NAMRC 34. 2006. p. 611-618 (Transactions of the North American Manufacturing Research Institute of SME; Vol. 34).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Swarm-intelligent neural network system (SINNS) based multi-objective optimization of hard turning
AU - Karpat, Yiǧit
AU - Özel, Tuǧrul
PY - 2006/9/21
Y1 - 2006/9/21
N2 - In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex machining optimization problems.
AB - In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS). The results showed that particle swarm optimization is an effective method for solving multi-objective optimization problems, and an integrated system of neural networks and swarm intelligence can be used in solving complex machining optimization problems.
KW - Multi-objective optimization
KW - Neural networks and hard turning
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=33748706420&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33748706420&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0872638480
SN - 9780872638488
T3 - Transactions of the North American Manufacturing Research Institute of SME
SP - 611
EP - 618
BT - Transactions of the North American Manufacturing Research Institute of SME 2006 - Papers Presented at NAMRC 34
ER -