College - Author 1
College of Engineering
Department - Author 1
Industrial and Manufacturing Engineering Department
Advisor
Aditya Chivate College of Engineering Industrial and Manufacturing Engineering
Acknowledgements
CSU-STEM Pathways and Reseach Alliance (CSU-SPaRA) and the CSU Chancellor’s Office
Date
10-2025
Abstract/Summary
This project investigates the use of acoustic signals captured during Fused Deposition Modeling (FDM) 3D printing to predict part quality and detect process anomalies. Traditional quality monitoring in FDM often relies on visual inspection or post-process evaluation, which can be slow and inconsistent. This research explores a low-cost, non-contact alternative using microphones and accelerometers to capture real-time audio and vibration signatures of the printing process. By applying signal processing and machine learning techniques to these acoustic signals, the project aims to classify part quality and identify defects such as under-extrusion, layer misalignment, or nozzle clogging. The outcomes have potential applications in smart manufacturing, predictive maintenance, and low-cost quality assurance systems.
October 1, 2025.
Included in
URL: https://digitalcommons.calpoly.edu/ceng_surp/165