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| Content Provider | Springer Nature Link |
|---|---|
| Author | Pellicer, Narcis Ciurana, Joaquim Delgado, Jordi |
| Copyright Year | 2009 |
| Abstract | Electro discharge machining process (EDM) is frequently used when machining of high complex and accurate features is required. Indeed, it is specially recommended for hard materials and micro-machined features. However, due to the process nature, there is still incomprehension on process parameters influence at the final quality features, ending up by lower productivity and quality ratios. On the other hand, fashioning and re-shaping of required electrodes for each feature are time consuming phases and the number of stored electrodes is very high. Therefore, in order to increase the global EDM process productivity, quality and flexibility, standardized simple electrode shapes, capable to machine different features, must be found. This study presents the influence of the main EDM process parameters and different tool geometries on basic process performance measures. A set of designed experiments with varying parameters such as pulsed current, open voltage, pulse time and pulse pause time are carried out in H13 steel using different geometries of copper electrodes. In addition, material removal rate , surface roughness and different dimensional and geometrical micro-accuracies are analyzed through statistical methods. Results help to select appropriate EDM process parameters to machine parts depending on product requirements. |
| Starting Page | 575 |
| Ending Page | 584 |
| Page Count | 10 |
| File Format | |
| ISSN | 09565515 |
| Journal | Journal of Intelligent Manufacturing |
| Volume Number | 22 |
| Issue Number | 4 |
| e-ISSN | 15728145 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2009-10-01 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | EDM MRR Surface roughness Process parameters Accuracy ANOVA Production/Logistics/Supply Chain Control, Robotics, Mechatronics Manufacturing, Machines, Tools |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Artificial Intelligence Software |
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