About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Process Monitoring in SolidStir® AM for Process Control and Quality Assurance |
| Author(s) |
Anurag Gumaste, Pankaj Kulkarni, Kumar Kandasamy |
| On-Site Speaker (Planned) |
Anurag Gumaste |
| Abstract Scope |
Additive manufacturing (AM) technologies, particularly solid-state processes like SolidStir® AM, are gaining traction for their ability to produce defect-free, high-performance components. However, real-time process monitoring and defect quantification remain critical challenges. This study investigates the use of in-situ force signals—spindle torque, feed force, and print force—as reliable indicators for monitoring the SolidStir® AM process and identifying defect formation. Experiments were conducted across varying process parameters, with force data continuously recorded during deposition. The results demonstrate strong correlations between fluctuations in force signatures and the occurrence of defects such as excessive flash, volumetric defects, and insufficient bonding. A data-driven approach could enable the development of predictive models for defect classification and severity quantification. The findings highlight the feasibility of using process outcome sensing as an integrated, non-destructive method for process health monitoring and quality assurance in SolidStir® AM, paving the way for closed-loop control strategies in advanced manufacturing. |