Northwestern University Robert R. McCormick School of Engineering and Applied Science

Advanced Manufacturing Processes Laboratory

2020 NSF Graduate Research Fellowship Awarded to Marisa BisramASME establishes Ehmann Manufacturing Medal Ryan Fellowship 2020LS-DYNA® MAT_COMPRF (MAT_293)Department of Defense’s Most Prestigious Single-Investigator AwardDeep Neural Networks in Additive Manufacturing

2020 NSF Graduate Research Fellowship Awarded to Marisa Bisram

This prestigious Fellowship recognizes and supports outstanding graduate students in NSF-supported science, technology, engineering, and mathematics disciplines.

ASME establishes Ehmann Manufacturing Medal

This medal will be awarded to the best journal paper published in ASME's Journal of Micro- and Nano-Manufacturing

Ryan Fellowship 2020

The fellowship, created in 2007, supports the finest graduate students in the country and provides them with the education and experience to assume leadership roles in the realm of nanotechnology.

LS-DYNA® MAT_COMPRF (MAT_293)

AMPL has developed a non-orthogonal material model of woven composites in the preforming process.

Department of Defense’s Most Prestigious Single-Investigator Award

Prof. Cao recognized for research in areas of importance to the DoD. This single investigator award includes a $3 million, five-year grant to fund cutting-edge research.

Deep Neural Networks in Additive Manufacturing

AMPL in collaboration with CUCIS has developed a deep Recurrent Neural Network (RNN) structure to accurately predict AM thermal properties with high temporal and spatial resolutions.

The Advanced Manufacturing Processes Lab (AMPL) develops computer-integrated systems for innovative manufacturing processes, including subtractive, deformation-based, and additive processes. These systems are established based on the fundamental understanding of the multi-physics of material deformation behavior during the process, combined with cost-effective simulation tools, intelligent design algorithms and the implementation of advanced control theories. These open-architecture systems aim at reducing development time, providing manufacturing flexibility, increasing material manufacturability, and reducing manufacturing cost. They are excellent platforms for R&D in innovative manufacturing processes, cyber-physical system integration, the Internet of Things, and cybersecurity.

Through our research platform, we provide an excellent training ground for our undergraduate and graduate students, and postdoctoral fellows to advance their critical thinking skills, to gain independent research capabilities, to build-up collaborative team-work experience and to become responsible citizens.