Engineering Commons^™

Prediction Of Meltpool Depth In Laser Powder Bed Fusion Using In-Process Sensor Data, Part-Level Thermal Simulations, And Machine Learning, Grant King

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

The goal of this thesis is the prevention of flaw formation in laser powder bed fusion additive manufacturing process. As a step towards this goal, the objective of this work is to predict meltpool depth as a function of in-process sensor data, part-level thermal simulations, and machine learning. As motivated in NASA's Marshall Space Flight Center specification 3716, prediction of meltpool depth is important because: (1) it can serve as a surrogate to estimate process status without the need for expensive post-process characterization, and (2) the meltpool depth provides an avenue for rapid qualification of microstructure evolution. To achieve the …

Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale

Library Philosophy and Practice (e-journal)

Additive Manufacturing has wide application range including healthcare, Fashion, Manufacturing, Prototypes, Tooling etc. AM techniques are subjected to various defects that may be printing defects or anomalies in machine. There is gap between current AM techniques and smart manufacturing since current AM lacks in build sensors necessary for process monitoring and fault detection. Both of these issues can be solved by incorporating real-time monitoring into AM. So the study is carried out to identify recent work done in AM to improve current system. For this bibliometric study Scopus database is used, study is kept limited to year 2010-2021 and English …

Thermal Modeling Of Additive Manufacturing Using Graph Theory: Validation With Directed Energy Deposition, Jordan Severson

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Metal additive manufacturing (AM/3D printing) offers unparalleled advantages over conventional manufacturing, including greater design freedom and a lower lead time. However, the use of AM parts in safety-critical industries, such as aerospace and biomedical, is limited by the tendency of the process to create flaws that can lead to sudden failure during use. The root cause of flaw formation in metal AM parts, such as porosity and deformation, is linked to the temperature inside the part during the process, called the thermal history. The thermal history is a function of the process parameters and part design.

Consequently, the first step …

Design Rules For Additive Manufacturing – Understanding The Fundamental Thermal Phenomena To Reduce Scrap, M. Reza Yavari, Kevin D. Cole, Prahalada K. Rao

Department of Mechanical and Materials Engineering: Faculty Publications

The goal of this work is to predict the effect of part geometry and process parameters on the direction and magnitude of heat flow heat flux in parts made using metal additive manufacturing (AM) processes. As a step towards this goal, the objective of this paper is to develop and apply the mathematical concept of heat diffusion over graphs to approximate the heat flux in metal AM parts as a function of their geometry. This objective is consequential to overcome the poor process consistency and part quality in AM. Currently, part build failure rates in metal AM often exceed 20%, …