Monitoring and Prognostics for Broaching Processes by Integrating Process Knowledge
| dc.contributor.author | Tian, Wenmeng | en |
| dc.contributor.committeechair | Camelio, Jaime A. | en |
| dc.contributor.committeemember | Jin, Ran | en |
| dc.contributor.committeemember | Wells, Lee Jay | en |
| dc.contributor.committeemember | Woodall, William H. | en |
| dc.contributor.department | Industrial and Systems Engineering | en |
| dc.date.accessioned | 2017-08-08T08:00:39Z | en |
| dc.date.available | 2017-08-08T08:00:39Z | en |
| dc.date.issued | 2017-08-07 | en |
| dc.description.abstract | With the advancement of sensor technology and data processing capacities, various types of high volume data are available for process monitoring and prognostics in manufacturing systems. In a broaching process, a multi-toothed broaching tool removes material from the workpiece by sequential engagement and disengagement of multiple cutting edges. The quality of the final part, including the geometric integrity and surface finish, is highly dependent upon the broaching tool condition. Though there has been a considerable amount of research on tool condition monitoring and prognostics for various machining processes, the broaching process is unique in the following aspects: 1) a broaching process involves multiple cutting edges, which jointly contribute to the final part quality; 2) the resharpening and any other process adjustments to the tool can only be performed with the whole broaching tool or at least a whole segment of the tool replaced. The overarching goal of this research is to explore how engineering knowledge can be used to improve process monitoring and prognostics for a complex manufacturing process like broaching. This dissertation addresses the needs for developing new monitoring and prognostics approaches based on various types of data. Specifically, the research effort focuses on 1) the use of in-situ force profile data for real-time process monitoring and fault diagnosis, 2) degradation characterization for broaching processes on an individual component level based on image processing; and 3) system-level degradation modeling and remaining useful life prediction for broaching processes based on multiple images. | en |
| dc.description.abstractgeneral | Big data have been providing both opportunities and challenges for product quality assurance and improvement in modern manufacturing systems. In aerospace industry, broaching processes are one of the most important manufacturing processes as they are used to produce the turbine discs in the jet engine. Nonconforming turbine disc quality, either in terms of compromised surface finish or geometry accuracy, will lead to malfunction or even catastrophic failures in the aircraft engines. One of the major sources that lead to nonconforming product quality is excessive tool wear accumulation and other abrupt malfunctions of the broaching tools. In broaching processes, multiple cutting edges are sequentially pushed or pulled through the workpiece, and each cutting edge is responsible to shape the workpiece into a specific intermediate shaped contour. Therefore, a broaching process can be regarded as a multistage manufacturing process with variation propagating through the multiple cutting edges. The overarching goal of this dissertation is to explore how process knowledge can be used to improve process monitoring and prognostics for a complex manufacturing process like broaching. This dissertation focuses on the quality assurance and improvement for broaching processes which includes: 1) timely abrupt process fault detection; 2) tool performance degradation quantification; and 3) remaining tool life prediction, which contributes to both methodological development and practical applications in advanced sensing analytics in manufacturing systems. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12391 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/78680 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Broaching | en |
| dc.subject | Degradation | en |
| dc.subject | Health Index | en |
| dc.subject | Image Processing | en |
| dc.subject | Machine Vision System | en |
| dc.subject | Physical Process Models | en |
| dc.subject | Profile Monitoring | en |
| dc.subject | Prognostics | en |
| dc.subject | Remaining Useful Life | en |
| dc.subject | Tool Wear | en |
| dc.title | Monitoring and Prognostics for Broaching Processes by Integrating Process Knowledge | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Industrial and Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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