Research

Monitoring 3D bioprinted parts can have improve the control and reproducibility of manufactured parts, which would expedite the translation of these technologies from R&D to industrial and clinical applications.

Researchers

PI Kainerstorfer (BME, CIT), Co-PI Feinberg (BME & MSE, CIT)

Detecting and preventing attacks on Internet-of-Things (IoT) devices in manufacturing can be accomplished using device behavior profiling to create models that offer customized protection of devices.

Researchers

PI Sekar (ECE, CIT), Co-PI Beuth (MechE, CIT), Wolf (CIT)

Additive manufacture of robust ceramic materials, which have many advanced energy applications, can be realized using externally applied electromagnetic fields and machine learning process optimization.

Researchers

PI Singh (ML, SCS), Co-PIs Jayan (MechE, CIT), Beuth (MechE, CIT), Davis (EPP, CIT)

Automating the conventionally manual task of de-powdering additive manufactured parts using robots equipped with sensors and cleaning actuators can improve worker safety as well as cost and time efficiency.

Researchers

PI Shimada (MechE, CIT), Co-PI Kitani (RI, SCS)

The ability to more accurately predict the impacts of emerging technologies on labor markets can be achieved through a novel approach that combines engineering and economic cost models and can be used to accurately advise small to medium manufacturing enterprises on decisions to invest in technology.

Researchers

PI Kovak (Heinz), Co-PIs Ales (Tepper), Fuchs (EPP, CIT), Whitefoot (MechE & EPP, CIT)

Real-time advanced robotic manufacturing can be used to produce concrete façade panels with designed surface geometries that improve building efficiency.

Researchers

PI Bard (Architecture), Co-PIs Cupkova (Architecture), Bourne (RI, SCS), Washburn (RI, SCS and Chem, MCS)

A novel approach to 3D printing using a support bath can greatly expand the types of polymers that can be printed, enable chemical reactions of the printed materials to gain novel material properties, and increase the mechanical strength and reduce the print time of mechanical parts through design optimization.

Researchers

PI Feinberg (BME & MSE, CIT), Co-PIs Ozdoganlar (MechE, CIT), Kara (MechE, CIT), Bockstaller (MSE, CIT)

In a smart classroom setting, sensors coupled with machine learned classifiers will analyze the quality of tasks performed by trainees to enable real-time data review by an instructor using augmented reality who can efficiently support and guide trainees.

Researchers

PI McLaren (HCII, SCS), Co-PI Travers (RI, SCS)

Additive manufacturing of elastomeric soft robotic materials with electrically-actuating compartments can allow the material to be dismembered, reattached, and self-heal with complete functional recovery. Learn more about this project.

PI Islam (MSE, CIT), Co-PI Yao (HCII, SCS)

Additive manufacturing can reduce the complexity of producing fiber-reinforced composites (FRC) while also enabling new properties, such as flat FRC sheets that can self-assemble into complex curved structures on demand.

Researchers

PI Zhang (MechE, CIT), Co-PI Yao(HCII, SCS)

A global view of the operations of a manufacturing plant can be realized using a reinforcement learning framework to combine information collected from different measurement mechanisms and enable the ability to efficiently diagnose system faults.

Researchers

PI Joshi (ECE, CIT), Co-PIs Ozdoganlar (MechE, CIT) and Yagan (ECE, CIT)

Interactive systems that simulate the operation of additive manufacturing machines can be used to provide instructional support for learning how to use the machine and enable virtual training of large numbers of students and users.

Researchers

PI Wolf (CIT), Co-PIs McLaren (HCII), Davidson (ETC)

High entropy alloy coatings can be developed to improve the mechanical properties of metals in extreme environments.

Researchers

PI de Boer (MechE, CIT), Co-PI Webler (MSE, CIT)

Fundamental limitations of modern chemistry and chemical discovery can be overcome using highly parallelized, computer guided procedures to describe and predict the construction and destruction of chemical bonds and the behavior of molecules.

Researchers

PI Bernhard (Chem, MCS), Co-PIs Fragkiadaki (ML, SCS), Noonan (Chem, MCS), Poczos (ML, SCS), Yaron (Chem, MCS), Kowalewski (Chem, MCS)

Commercial implementation of metal additive manufacturing in parts and systems can be accelerated by combining 1) decision science and machine learning to improve candidate part selection and 2) tools developed to optimize part design, machine process parameters, part layout, and part consolidation.

Researchers

PI Davis (EPP, CIT), Co-PIs Fuchs (EPP, CIT), Vaishnav (EPP, CIT), Kara (MechE, CIT), Whitefoot (MechE & EPP, CIT), Poczos (ML, SCS), Singh (ML, SCS)