The Manufacturing Futures Initiative will highlight, strengthen, support, and advance five pillars of manufacturing research at Carnegie Mellon University:

Robotics, Additive manufacturing, Advanced materials, Biomanufacturing, Textiles and fabrics

Source: College of Engineering

Pie chart depicting CMU departments in MFI.

Source: College of Engineering

Research projects

Through a $1.7 million seed grant program, 10 projects were awarded in 2017 to initiate novel, interdisciplinary research related to advanced manufacturing in areas of materials, design, robotics, learning science, policy, and machine learning. The funded participants include 28 faculty researchers across 6 schools and colleges and 12 departments.

Materials for soft robotics through additive molecular manufacturing

A new controlled polymerization methodology for additive manufacturing of materials can overcome barriers to producing soft robotics, such as artificial skin sensors and artificial muscles.

Research goals

  • Develop a new methodology for additive manufacturing of materials for soft robotics via controlled polymerization. This approach can address barriers in the domains of soft-matter electronics and programmable matter.
Researchers

Krzysztof Matyjaszewski (Chemistry), Tomasz Kowalewski (Chemistry), and Carmel Majidi (Mechanical Engineering)

 

Emerging technologies, labor outcomes, and policy responses

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.

Research goals
  • Combine engineering and economic approaches to model the labor market impacts of emerging technologies.
Researchers

Laurence Ales (Tepper School of Business), Brian K. Kovak (H. John Heinz III College of Information Systems and Public Policy), and Katie Whitefoot (Engineering and Public Policy and Mechanical Engineering)

 

Technology change in manufacturing: implications for magnitude and nature of work

By combining engineering production modeling and empirical shop-floor data, researchers can develop a method to quantify the implications of emerging technologies for the nature of work and wages, which will allow them to accurately advise small to medium manufacturing enterprises on decisions to invest in technology.

Research goals 
  • Use engineering production modeling and empirical shop-floor data to quantify implications of emerging technologies for the nature of work and wages prior to investment.
Researchers

Erica Fuchs (Engineering and Public Policy) and Katie Whitefoot (Mechanical Engineering and Engineering and Public Policy)

 

Cost minimization in additive manufacturing through optimal structure, process, and layout strategies

Tools can be developed to optimize design part structure, machine process parameters, and part layout in order to significantly reduce the cost of metal additive manufacturing.

Research goals
  • Manage design part structure, machine process parameters, and layout to significantly reduce costs of metals additive manufacturing.
Researchers

Kate S. Whitefoot (Engineering and Public Policy and Mechanical Engineering), Burak Kara (Mechanical Engineering), and Burak Ozdoganlar (Mechanical Engineering) 

 

Identifying product opportunities: expert heuristics in scientific decision-making

Commercial implementation of metal additive manufacturing in parts and systems can be accelerated using decision science and machine learning to improve part selection.

Research goals
  • Accelerate commercialization of metal additive manufacturing in parts and systems using decision science and machine learning to improve part selection for metal additive manufacturing.
Researchers

Erica Fuchs (Engineering and Public Policy), Alex Davis (Engineering and Public Policy), and Parth Vaishnav (Engineering and Public Policy)

 

Freeform reversible embedding 3-D printing (FRE-3DP) as a transformative platform for polymer additive manufacturing

A novel approach to 3-D printing materials using a support bath can greatly expand the types of polymers that can be 3-D printed, the throughput (speed) of the printing process, and the complexity and mechanical properties of the printed parts.

Research goals 
  • Demonstrate novel capabilities that greatly expand the types of polymers that can be 3-D printed, the throughput (speed) of the printing process, and the complexity and mechanical properties of printed parts.
Researchers

Adam Feinberg (Biomedical Engineering and Materials Science and Engineering) and Burak Ozdoganlar (Mechanical Engineering)

 

Machine learning in support of additive manufacturing

Machine learning can be used to develop a better, more predictive understanding of additive manufactured parts and processes through implementing tools that rapidly evaluate powder feedstocks and part microstructures and that afford process monitoring capabilities at the melt pool and layer levels.

Research goals
  • Develop tools to rapidly evaluate AM powder feedstocks and part microstructures.
  • Develop process monitoring capabilities at the melt pool and layer levels.
Researchers

Liz Holm (Materials Science and Engineering), Jack Beuth (Mechanical Engineering), Burak Kara (Mechanical Engineering), Barnabas Poczos (College of Engineering's Machine Learning Department), Anthony D. Rollett (Materials Science and Engineering), Mahadev Satyanarayanan (School of Computer Science)

 

VirtualCellLab: accelerating tacit knowledge in cell-culture manufacturing

Machine learning interfaces used for workforce development in areas such as cell manufacturing can enable workers to rapidly gain expertise by comparing images in a large data set in order to accurately identify cell conditions and suggest actions.

Research goals
  • Scaffold comparison between cell samples to help learners get tacit expertise in cell culture.
  • Use machine learning to detect cell condition, and suggest actions.
  • Use human comparisons as one input feature.
  • Grow cell manufacturing and increase steady demand and rising wages at least till 2022.
Researchers

Chinmay Kulkarni (Human Computer Interaction Institute) and Rebecca Taylor (Mechanical Engineering and Biomedical Engineering)

 

Tooled deposition of high-performance building components for post-processing of 3-D printed architectures

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

Research goals
  • Link simulation of high-performance building components with advanced robotic manufacturing.
  • Produce concrete façade panels featuring articulated, varied surface geometries without dedicated molds.

Researchers

Dana Cupkova (Architecture), Joshua Bard (Architecture), Newell Washburn (Chemistry and Materials Science and Engineering), and Garth Zeglin (Robotics Institute)

 

Simplifying 3-D model design

Software engineering concepts, such as modularity, can be used to increase the flexibility of computer aided design tools used for 3-D printed parts and speed up the iterative process of making parts.

Research goals
  • Use software engineering concepts, such as modularity, to enable greater flexibility in computer aided design of 3-D printed parts.
  • Speed up the iterative process of making 3-D printed objects.
Researchers

Scott Hudson (School of Computer Science) and Simon Lucey (Robotics Institute)