To realize high quality, additively manufactured parts, real-time process monitoring and advanced predictive modeling tools are crucial for accelerating quality assurance and quality control in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and prediction of surface roughness in fused deposition modeling (FDM). This paper presents a new data-driven approach to surface roughness prediction in FDM. A real-time monitoring system is developed to monitor the health condition of a 3D printer and FDM processes using multiple sensors. A predictive model is built by random forests (RFs). Experimental results have shown that the predictive model is capable of predicting the surface roughness of a printed part with very high accuracy.
Skip Nav Destination
ASME 2018 13th International Manufacturing Science and Engineering Conference
June 18–22, 2018
College Station, Texas, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5137-1
PROCEEDINGS PAPER
Surface Roughness Prediction in Additive Manufacturing Using Machine Learning
Dazhong Wu,
Dazhong Wu
University of Central Florida, Orlando, FL
Search for other works by this author on:
Yupeng Wei,
Yupeng Wei
Pennsylvania State University, University Park, PA
Search for other works by this author on:
Janis Terpenny
Janis Terpenny
Pennsylvania State University, University Park, PA
Search for other works by this author on:
Dazhong Wu
University of Central Florida, Orlando, FL
Yupeng Wei
Pennsylvania State University, University Park, PA
Janis Terpenny
Pennsylvania State University, University Park, PA
Paper No:
MSEC2018-6501, V003T02A018; 6 pages
Published Online:
September 24, 2018
Citation
Wu, D, Wei, Y, & Terpenny, J. "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning." Proceedings of the ASME 2018 13th International Manufacturing Science and Engineering Conference. Volume 3: Manufacturing Equipment and Systems. College Station, Texas, USA. June 18–22, 2018. V003T02A018. ASME. https://doi.org/10.1115/MSEC2018-6501
Download citation file:
363
Views
Related Proceedings Papers
Related Articles
The MechProcessor: Helping Novices Design Printable Mechanisms Across Different Printers
J. Mech. Des (November,2015)
Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing
J. Manuf. Sci. Eng (October,2017)
Filter Cake Properties of Water-Based Drilling Fluids Under Static and Dynamic Conditions Using Computed Tomography Scan
J. Energy Resour. Technol (December,2013)
Related Chapters
Part A: Farm Waste to Energy
Biomass and Waste Energy Applications