My primary research interests are in multiscale methods, design optimization for additive manufacturing, and computational nanomechanics. Currently, my research group is actively working on developing the "Lattice Structure Design Optimization" software for generating optimal lightweight design for 3D printing.
I joined University of Pittsburgh in 2008 as assistant professor and have been associate professor since 2014. I am also directing the ANSYS Additive Manufacturing Research Laboratory at Pitt, which houses several of the most advanced metal and plastic 3D printers including the EOS DMLS, Optomec LENS, ExOne binder jetting, and Stratasys Objet systems.
I did my undergraduate study at UC Berkeley and master's study at MIT. I obtained my Ph.D. from UC Berkeley under the supervision of Shaofan Li and Steve Glaser. I also conducted postdoctoral research with Wing Kam Liu at Northwestern University.
My research has been supported by NSF, America Makes, DoD, NASA, NRC, ANSYS, etc. I am collaborating with the industry extensively in my computational research for additive manufacturing.
I received the NSF BRIGE award in 2009 and the 2016-17 Board of Visitors Faculty Award from my engineering school.
Cellular structures can be employed effectively in lightweight structural design to overcome some of the manufacturing limitations existing in additive manufacturing (AM). For this purpose, a homogenization-based topology optimization method is proposed to optimize variable-density cellular structures efficiently. First, homogenization is performed to capture the effective mechanical properties of cellular structures through the scaling law as a function of relative density. Second, the scaling law is employed directly in the topology optimization algorithm to compute the optimal density distribution for the part being optimized. Third, a new technique is presented to reconstruct the CAD model of the optimal variable-density cellular structure. The proposed method is validated by comparing the results obtained through homogenized model, full scale simulation, and experimentally testing the optimized parts after being additive manufactured. The test examples demonstrate that the proposed homogenization-based method is efficient, accurate, and is able to produce manufacturable designs.
 L. Cheng, P. Zhang, E. Biyikli, J. Bai, J. Robbins, M. Lynch, E. Butcher, and A. C. To, “Efficient design optimization of variable-density cellular structures for additive manufacturing: Theory and experimental validation,” Rapid Prototyping Journal, 2016. (accepted)
 P. Zhang, J. Toman, Y. Yu, E. Biyikli, M. Kirca, M. Chmielus, and A. C. To, “Efficient design-optimization of variable-density hexagonal cellular structure by additive manufacturing: Theory and validation," ASME Journal of Manufacturing Science and Engineering, vol. 137, 021004, 2015.
Advances in additive manufacturing (AM) technology have made it possible to manufacture complex-shaped metal components strong enough for real engineering applications. To date, the process-microstructure-property relationship for AM metals has mostly been investigated experimentally, which is expensive and time-consuming since the parameter space is quite large. The lack of a reliable theoretical model for predicting such relationship makes it difficult to design AM components. The goal of this research is to establish a theoretical model that is capable of predicting the microstructure (texture, grain size, shape and subgrain features length scale) and mechanical properties (strength and anisotropy) of an AM metal based on the input process parameters (beam power, scan speed, preheat, and scanning strategy).
 J. Liu and A. C. To, "Quantitative texture prediction of epitaxial columnar grains in additive manufacturing using selective laser melting,” (submitted)
In the last five years, we have been developing a new energy-based concurrent atomistic/continuum framework called the Mutiresolution Molecular Mechanics (MMM) that includes formulation for both the statics (MMS) and dynamics (MMD) methods. By introducing a novel energy sampling framework, MMM aims at accurately and efficiently approximating the atomic energy of the system at different resolutions without the cumbersome interfacial treatment in existing methods. The key features of the MMM method are: (1) consistency with the atomistics framework, (2) consistency with the order of shape functions introduced, and (3) flexibility in energy approximation with respect to accuracy and efficiency. Under the energy sampling framework, several sampling schemes have been devised and tested for interface compatibility, and compared to existing methods. The proposed MMM method demonstrates very good accuracy in solving many different problems such as crack propagation and surface relaxation problems when compared to full molecular statics.
 E. Biyikli and A. C. To, “Multiresolution molecular mechanics: adaptive analysis,” Computer Methods in Applied Mechanics and Engineering, vol. 305, 682-702, 2016.
 Q. Yang and A. C. To, "Multiresolution molecular mechanics: a unified and consistent framework for general finite element shape functions," Computer Methods in Applied Mechanics and Engineering, vol. 283, 384-418, 2015.
We believe nature optimizes certain mechanical properties of biological materials by designing microstructure. Recently, we have discovered interesting mechanical behaviors in hierarchical structure found in many biocomposites. For example, hierarchical structure can enhance wave filtering and damping figure of merits significantly.
 P. Zhang, M. Heyne, and, A. C. To, “Biomimetic staggered composites with highly enhanced energy dissipation: modeling, 3D printing, and testing” Journal of Mechanics and Physics of Solids, vol. 83, 285-300, 2015
 P. Zhang and A. C. To, "Broadband wave filtering of bioinspired hierarchical phononic crystal," Applied Physics Letters, vol. 102, 121910, 2013.
Total Journal Publications: 66
Ph.D. University of Maryland, Baltimore County
Research: Crystal plasticity modeling of AM Metals
Ph.D. University of Alberta
Research: Topology optimization for AM
M.S. Shanghai Jiaotong University
B.X. Xi'an Jiaotong University
Research: Topology optimization for AM cellular structures
M.S. Xi'an Jiaotong University
B.S. Xi'an Jiaotong University
Research: AM process modeling
Xueming Yang (2009-2010), now associate professor at North China Electric Power University in China
Aditi Datta (2009-2011)
Qingcheng Yang (Ph.D. 2016), now postdoc fellow in Prof. Long-Qing Chen's group at Penn State University
Pu Zhang (Ph.D. 2015), now postdoc associate at University of Manchester in UK
Emre Biyikli (Ph.D. 2015), now senior software engineer at MathWorks.
Mesut Kirca (Ph.D. 2013), now assistant professor at Istanbul Technical University in Turkey
Yao Fu (Ph.D. 2013), now postdoc associate at Oak Ridge National Laboratory, accepted an offer to join University of Cincinnati as assistant professor
Cengiz Baykasoglu (2015-2016), now associate professor at Hetit University in Turkey
Lili Wang, (2011-2012), now assistant professor at Shanghai University of Engineering Science
Dariush Mohammadyani, (2011-2012), now postdoc at Johns Hopkins University
Jiaxi Bai (M.S. 2016), now software engineer at ANSYS
Yiqi Yu (M.S. 2014), now software engineer at ANSYS
Ashtuosh Giri (M.S. 2012), now PhD student at University of Virginia