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Fast Prediction Of Thermal Distortion In Metal Powder Bed Fusion Additive Manufacturing: Part 2, A Quasi-Static Thermo-Mechanical Model, Hao Peng, Morteza Ghasri-Khouzani, Shan Gong, Ross Attardo, Pierre Ostiguy, Ronald B. Rogge, Bernice Aboud Gatrell, Joseph Budzinski, Charles Tomonto, Joel Neidig, M. Ravi Shankar, Richard Billo, David B. Go, David Hoelzle
Fast Prediction Of Thermal Distortion In Metal Powder Bed Fusion Additive Manufacturing: Part 2, A Quasi-Static Thermo-Mechanical Model, Hao Peng, Morteza Ghasri-Khouzani, Shan Gong, Ross Attardo, Pierre Ostiguy, Ronald B. Rogge, Bernice Aboud Gatrell, Joseph Budzinski, Charles Tomonto, Joel Neidig, M. Ravi Shankar, Richard Billo, David B. Go, David Hoelzle
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The additive manufacturing (AM) process metal powder bed fusion (PBF) can quickly produce complex parts with mechanical properties comparable to that of wrought materials. However, thermal stress accumulated during Metal PBF may induce part distortion and even cause failure of the entire process. This manuscript is the second part of two companion manuscripts that collectively present a part-scale simulation method for fast prediction of thermal distortion in Metal PBF. The first part provides a fast prediction of the temperature history in the part via a thermal circuit network (TCN) model. This second part uses the temperature history from the TCN …
Fast Prediction Of Thermal Distortion In Metal Powder Bed Fusion Additive Manufacturing: Part 1, A Thermal Circuit Network Model, Hao Peng, Morteza Ghasri-Khouzani, Shan Gong, Ross Attardo, Pierre Ostiguy, Bernice Aboud Gatrell, Joseph Budzinski, Charles Tomonto, Joel Neidig, M. Ravi Shankar, Richard Billo, David B. Go, David Hoelzle
Fast Prediction Of Thermal Distortion In Metal Powder Bed Fusion Additive Manufacturing: Part 1, A Thermal Circuit Network Model, Hao Peng, Morteza Ghasri-Khouzani, Shan Gong, Ross Attardo, Pierre Ostiguy, Bernice Aboud Gatrell, Joseph Budzinski, Charles Tomonto, Joel Neidig, M. Ravi Shankar, Richard Billo, David B. Go, David Hoelzle
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The additive manufacturing (AM) process metal powder bed fusion (PBF) can quickly produce complex parts with mechanical properties comparable to wrought materials. However, thermal stress accumulated during PBF induces part distortion, potentially yielding parts out of specification and frequently process failure. This manuscript is the first of two companion manuscripts that introduce a computationally efficient distortion and stress prediction algorithm that is designed to drastically reduce compute time when integrated in to a process design optimization routine. In this first manuscript, we introduce a thermal circuit network (TCN) model to estimate the part temperature history during PBF, a major computational …