Sensor-based Online Process Monitoring in Advanced Manufacturing

Effective quality improvement in the manufacturing industry is continually pursued. There is an increasing demand for real-time fault detection, and avoidance of destructive post-process testing. Therefore, it is desirable to employ sensors for in-process monitoring, allowing for real-time quality assurance. Chapter 3 describes the application of sensor based monitoring to additive manufacturing, in which sensors are attached to a desktop model fused deposition modeling machine, to collect data during the manufacturing process. A design of experiments plan is conducted to provide insight into the process, particularly the occurrence of process failure. Subsequently, machine learning classification techniques are applied to detect such failure, and successfully demonstrate the future potential of this platform and methodology. Chapter 4 relates the application of online, image-based quantification of the surface quality of workpieces produced by cylindrical turning. Representative samples of cylindrical shafts, machined by turning under various conditions, are utilized, and an apparatus is constructed for acquiring images while the part remains mounted on a lathe. The surface quality of these specimens is analyzed, employing an algebraic graph theoretic approach, and preliminary regression modeling displays an average surface roughness (Ra) prediction error of less than 8%. Prediction occurs in less than 2 seconds, showing the capability for future application in a real-time, quality control setting. Both of these cases, in additive manufacturing and in turning, are validated using real experimental data and analysis, showing application of sensor-based online process monitoring in multiple manufacturing areas.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Industrial and Systems Engineering

Department dc:contributor.department

Industrial and Systems Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2016

Author dc:creator

• Roberson, David Mathew III

Chair dc:contributor.committeechair

• Kong, Zhenyu

Committee members dc:contributor.committeemember

• Rao, Prahalad
• Jin, Ran

• Additive manufacturing
• Fused Deposition Modeling
• Machining
• Turning
• Sensor-based Monitoring

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:8391

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/72911