In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes

ABSTRACT

One major challenge of implementing Directed Energy Deposition (DED) Additive Manufacturing (AM) for production is the lack of understanding of its underlying process–structure–property relationship. Parts manufactured using the DED technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The objective of this research is to characterize the underlying thermo-physical dynamics of the DED process, captured by melt pool signals, and predict porosity during the build. Herein we propose a novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as an AM part is being built. Self-Organizing Maps (SOMs) are then used to further analyze the two-dimensional melt pool image streams to identify similar and dissimilar melt pools. X-ray tomography is used to experimentally locate porosity within the Ti-6Al-4V thin-wall specimen, which is then compared with predicted porosity locations based on the melt pool analysis. Results show that the proposed method based on the temperature distribution of the melt pool is able to predict the location of porosity almost 96% of the time when the appropriate SOM model using a thermal profile is selected. Results are also compared with a previous study, that focuses only on the shape and size of the melt pool. We find that the incorporation of thermal distribution significantly improves the accuracy of porosity prediction. The significance of the proposed methodology based on the melt pool profiles is that this can lead the way toward in situ monitoring and minimize or even eliminate pores within the AM parts.

KEYWORDS:

• Additive manufacturing
• porosity
• clustering
• in situ monitoring
• melt pool

Disclosure

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Additional information

Funding

Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-15-2-0025.

Notes on contributors

Mojtaba Khanzadeh

Mojtaba Khanzadeh is a Ph.D. student in the Industrial and Systems Engineering Department at Mississippi State University and is pursuing another M.Sc. degree in the Statistics Department at Mississippi State University. He received his M.Sc. and B.Sc. in industrial and systems engineering from Sharif University of Technology in 2015 and 2013, respectively. Mojtaba Khanzadeh’s research interests focus on using machine learning techniques for process characterization in Additive Manufacturing. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS), the Institute of Industrial and Systems Engineers (IISE), and American Society of Mechanical Engineers (ASME).

Sudipta Chowdhury

Sudipta Chowdhury received his B.Sc. in industrial and systems engineering from Shahjalal University of Science and Technology in 2014. He is currently a graduate student in the Department of Industrial and Systems Engineering at Mississippi State University.

Mark A. Tschopp

Mark A. Tschopp is the Regional Lead for ARL Central at the U.S. Army Research Laboratory, having previously been a materials engineer, team leader, and branch chief in the Weapons and Materials Research Directorate. His primary research interests lie in integrating computational and experimental techniques to design materials for lightweight vehicle applications, soldier protection systems, and lethality applications in support of the warfighter and the mission of the U.S. Army. He was elected as an ASME Fellow in 2017.

Haley R. Doude

Haley R. Doude is a research engineer at the Center for Advanced Vehicular Systems at Mississippi State University (MSU). She received a competitive NASA fellowship to fund her doctoral degree research in friction stir welding. She earned her Ph.D. in mechanical engineering from MSU in 2014 and her bachelor’s degree in biological engineering from MSU in 2006. Her background is in material science with a focus on design and processing of metals. Current projects include steel alloy design and material science studies related to additive manufacturing. Sponsors for her research include DoD, NASA, and industrial partners.

Mohammad Marufuzzaman

Mohammad Marufuzzaman received his Ph.D. in industrial and systems engineering from Mississippi State University in 2014. He then joined the department as an assistant professor in August 2015. His main areas of interest are in supply chain optimization with an application in renewable energy, stochastic programming, decomposition methods, solving large-scale network flow problems, and supply chain risk management. His publications have appeared in journals such as Transportation Science, Annals of Operations Research, Computers and Operations Research, Transportation Research Part E, International Journal of Production Economics, and several conference proceedings. He is a member of INFORMS and IIE.

Linkan Bian

Linkan Bian is an assistant professor in the Industrial and Systems Engineering Department at Mississippi State University. He received his Ph.D. in industrial and systems engineering from Georgia Institute of Technology in 2013. He also holds a dual M.S. degree in statistics and mathematics from Michigan State University and a B.S. degree in applied mathematics from Beijing University. His research interests focus on the combination of advanced statistics and stochastic methods for system modeling, diagnosis, and prognosis. Applications of his research include advanced manufacturing systems and supply chains. He is currently participating in a DoD project focusing on uncertainty quantification and process optimization in Additive Manufacturing processes. His research is also funded by FedEx Express. His publications have appeared in journals such as Institute of Industrial Engineers (IIE) Transactions, Statistical Analysis and Data Mining, Naval Research Logistics, and several conference proceedings. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial and Systems Engineers (IISE).