Modeling of material removal rate in micro EDM using neural network
By: Singh,Arun Pratap
.
Contributor(s): Tripathi, Shikha | Singh,D.K.
Publisher: New Delhi Journals Pub 2018Edition: Vol 4(1).Description: 30-39p.Subject(s): EXTC Engineering | advanced machining methods | advanced manufacturing methods | MLPOnline resources: Click here
In:
International journal of microelectronics and digital integrated circuitsSummary: In micro-electrical discharge machining (Micro EDM) machining, material removal rate (MRR) is the particular main output parameter. Here we will discover various machining parameters get an effect on the particular MRR. For production industries, to maximize the results from Micro EDM, appropriate predictive designs for MRR must end up being constructed. This particular report utilizes neural network modeling to predict MRR over the particular machining time for assortment of cutting conditions around EDM. Some experimental data for machining of mild steel are obtained along with the experimental data obtained via conducted findings on EDM in the particular lab. The data-sets from MRR were utilized to learn the particular neural network models. Trained neural network models were utilized in predicting MRR to get other cutting conditions.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2018141 |
In micro-electrical discharge machining (Micro EDM) machining, material removal rate (MRR) is the particular main output parameter. Here we will discover various machining parameters get an effect on the particular MRR. For production industries, to maximize the results from Micro EDM, appropriate predictive designs for MRR must end up being constructed. This particular report utilizes neural network modeling to predict MRR over the particular machining time for assortment of cutting conditions around EDM. Some experimental data for machining of mild steel are obtained along with the experimental data obtained via conducted findings on EDM in the particular lab. The data-sets from MRR were utilized to learn the particular neural network models. Trained neural network models were utilized in predicting MRR to get other cutting conditions.
There are no comments for this item.