My primary research interests are in design optimization for additive manufacturing, multiscale methods, and computational mechanics. Currently, my research group is actively working on fast process modeling and topology optimization for metal additive manufacturing.
I joined University of Pittsburgh in 2008 as assistant professor and was promoted to associate professor in 2014 and to full professor in 2019. I am also directing the ANSYS Additive Manufacturing Research Laboratory at Pitt, which houses several of the most advanced metal 3D printers including the EOS DMLS, Optomec LENS, and ExOne binder jetting.
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 NASA, DOD, DOE, NSF, America Makes, ANSYS, etc. I am collaborating with the industry extensively in my computational research for additive manufacturing through the MOST-AM Consortium, which I founded in 2016 and now has 30+ member companies and research labs.
I received the NSF BRIGE award in 2009, the Board of Visitors Faculty Award from my engineering school in 2016, and the Carnegie Science Award in 2018.
The goal of this work is to explore using topology optimization to design support structure to mitigate residual stress induced build failure in laser powder bed fusion. To make topology optimization computationally tractable, the modified inherent strain method proposed by us is employed to perform fast prediction of residual stress in an AM build. The components with optimized support structures no longer suffer from stress-induced cracking after the designs are realized by AM, which proves the effectiveness of the proposed method.
 L. Cheng, X. Liang, J. Bai, Q. Chen, J. Lemon, and A. C. To, “On utilizing topology optimization to design support structure to prevent stress induced build failure in laser powder bed fusion,” Additive Manufacturing, accepted. [link]
 L. Cheng and A. C. To, “Part-scale build orientation optimization for minimizing residual stress and support volume for metal additive manufacturing: theory and experimental validation,” Computer-Aided Design, to appear.
Residual deformation and stress are one of the most critical issues in metal additive manufacturing (AM) techniques. It is a key challenge to predict the residual deformation in the part-scale by performing detailed process simulation for the entire part, which is prohibitively expensive and hence impractical. In this work, the modified inherent strain theory is proposed to enable efficient yet accurate prediction of residual deformation of AM components. The proposed theory allows for the calculation of inherent strain accurately based on a small-scale process simulation of a small representative volume out of a large component. The extracted mean inherent strain vector will be applied to a part-scale model layer-by-layer in order to simulate accumulation of the residual deformation by static finite element analysis. To verify the accuracy of the proposed method, the residual deformation and stress of different geometries after the AM processing are investigated, and the predicted residual deformation matches well with the experimental results.
 X. Liang, L. Cheng, Q. Chen, Q. Yang, and A. C. To, “A modified method for estimating inherent strains from detailed process simulation for fast residual distortion prediction of single-walled structures fabricated by directed energy deposition,” Additive Manufacturing, vol. 23, 471-486, 2018. [link]
 Q. Chen, X. Liang, D. Hayduke, J. Liu, L. Cheng, J. Oskin, R. Whitmore, and A. C. To, “An inherent strain based multiscale modeling framework for simulating part-scale residual deformation for direct metal laser sintering,” Additive Manufacturing, to appear.
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, and A. C. To, “Efficient design optimization of variable-density cellular structures for additive manufacturing: Theory and experimental validation,” Rapid Prototyping Journal, 23, 660-677, 2017. [link]
 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. [link]
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,” Additive Manufacturing, 16, 58-64, 2017. [link]
 J. Liu, W. Xiong, A. Behera, S. Thompson, and A. C. To, "Mean-field polycrystal plasticity modeling with grain size and shape effects for laser additive manufactured FCC metals," International Journal of Solids and Structures, 112, 35-42, 2017. [link]
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 [link]
 P. Zhang and A. C. To, "Broadband wave filtering of bioinspired hierarchical phononic crystal," Applied Physics Letters, vol. 102, 121910, 2013. [link]
Total Number of Journal Publications: 92
Ph.D. University of Nantes, France
Research: Fluid topoloy optimization for additve manufacturing
Ph.D. University of South Florian
Research: Fracture mechanics of AM materials
Ph.D. Indian Institute of Technology, Bombay
Research: Process-microstructure-property modeling of AM materials
M.S. Shanghai Jiaotong University
B.S. Xi'an Jiaotong University
Research: Topology optimization for additive manufacturing
M.S. Tsinghua University
B.S. Tsinghua University
Research: Inherent strain modeling of additive manufacturing
M.S. Beihang University
B.S. Beihang University
Research: Nonlinear topology optimization for additive manufacturing
M.S. Huazhong University of Science and Technology
B. S. Huazhong University of Science and Technology
Research: Inherent strain modeling of additive manufacturing
Jikai Liu (2016-2018), now profeessor at Shandong University
Jian Liu (2015-2018), now postdoc fellow at University of Pittsburgh
Xueming Yang (2009-2010), now professor at North China Electric Power University in China
Aditi Datta (2009-2011), now adjunct faculty at Purdue Universtiy Northwest
Qingcheng Yang (Ph.D. 2016), now postdoc fellow in Prof. Samnath Ghosh's group at Johns Hopkinds University
Pu Zhang (Ph.D. 2015), now assistant professor at SUNY-Binghamton University.
Emre Biyikli (Ph.D. 2015), now senior software engineer at MathWorks.
Mesut Kirca (Ph.D. 2013), now associate professor at Istanbul Technical University in Turkey
Yao Fu (Ph.D. 2013), now assistant professor at University of Cincinnati
Akihiro Takezawa (2019-present), associate professor at Hiroshima University in Japan
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
Yiming Ding (M.S. 2017), now an engineer at Groupe PSA
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