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| Content Provider | Springer Nature Link |
|---|---|
| Author | Khan, Imtiaz Ali Rajput, Tikam Singh |
| Copyright Year | 2012 |
| Abstract | This study provides predictive models for the functional relationship between input and output variables of wire cut electrical discharge machine (WEDM) environment using alloy steel (HCHCr). Multi-objective optimization of the process parametric combinations is attempted by modeling WEDM process by use of artificial neural networks (ANN). This work provide an optimized input data set to WEDM system and the results show improvement with better productivity, reduced cutting time and product cost at the cutting speed and surface finish. At experimental result, the surface quality decreases as cutting speed increases and 1.371 mm/ min becomes the maximum cutting speed obtained with good surface finish of 0.387 micron. The results show the potential to improve production efficiency and part quality. |
| Starting Page | 1989 |
| Ending Page | 1995 |
| Page Count | 7 |
| File Format | |
| ISSN | 22347593 |
| Journal | International Journal of Precision Engineering and Manufacturing |
| Volume Number | 13 |
| Issue Number | 11 |
| e-ISSN | 20054602 |
| Language | English |
| Publisher | Korean Society for Precision Engineering |
| Publisher Date | 2012-10-31 |
| Publisher Place | Springer |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | WEDM Modeling ANN Multi-objective optimization Cutting speed Surface finish Industrial and Production Engineering Materials Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Mechanical Engineering Electrical and Electronic Engineering |
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