Depth of Cut Monitoring for Hybrid Manufacturing Using Acoustic Emission Sensor
| dc.creator | Gaja, Haythem | |
| dc.creator | Liou, Frank | |
| dc.date.accessioned | 2021-10-19T21:16:54Z | |
| dc.date.available | 2021-10-19T21:16:54Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | Laser Metal Deposition LMD is a hybrid manufacturing process consist of a laser deposition system combined with a 5-axis CNC milling system. During laser deposition many parameters and their interaction affect the dimensional accuracy of the part produced, powder flow rate, laser power and travel speed are some of these parameters. Sensing the acoustic emission during milling marching gives feedback information regarding depth of metal being cut subsequent part dimensions, if an error in dimensions is found certain actions, such as remaching, close loop control, or laser remelting can be carried out to correct it. Thus a reliable hybrid manufacturing management system requires that a depth-of-cut detection system be integrated with the milling machine architecture. This work establishes, first a methodology to detect an acoustic emission signal, so that the acoustic emission characteristics of the milling could be analyzed. Second, it sought to relate these acoustic data to machining parameters to detect depth-of-cut. | en_US |
| dc.description.department | Mechanical Engineering | en_US |
| dc.identifier.uri | https://hdl.handle.net/2152/89340 | |
| dc.language.iso | eng | en_US |
| dc.publisher | University of Texas at Austin | en_US |
| dc.relation.ispartof | 2015 International Solid Freeform Fabrication Symposium | en_US |
| dc.rights.restriction | Open | en_US |
| dc.subject | depth of cut detection | en_US |
| dc.subject | acoustic emission | en_US |
| dc.subject | artificial neural network | en_US |
| dc.title | Depth of Cut Monitoring for Hybrid Manufacturing Using Acoustic Emission Sensor | en_US |
| dc.type | Conference paper | en_US |