2023 Call for Seed Grant Proposals

Abstract

Designing factories that efficiently deploy digitally controlled equipment such as robotics and 3D printing is essential to making manufacturing more efficient, sustainable, and locally-oriented. However, current design tools are not accessible to small and medium-sized manufacturers (SMMs) – which comprise over 95% of the supply chain in most developed nations such as the U.S. and Portugal – and therefore many manufacturers lack the ability to make data-driven decisions to invest in automation.. The goal of this project is to create a more capable and affordable factory simulation approach, integrating automated generation of simulation models with machine learning methods for generative design of factories, and so enable more resilient, distributed production networks. To properly incorporate principles of quality management, both at the factory and supply chain levels, we will collaborate with colleagues from U. Minho. We plan to apply this tool with companies in Portugal, as well as in Massachusetts, and share the outcomes across the collaboration. The results will help guide manufacturers to make investments in advanced equipment that improves productivity and reduce their carbon footprint, and the tool will be further extensible to simulate highly distributed production systems within future cities or even in space.

MIT PIs
John Hart, Professor,Department of Mechanical Engineering

PT PIs
Paulo Sampaio, Professor de Qualidade e Excelência Organizacional, University of Minho
Bárbara Rangel Carvalho, Assistant Professor, University of Porto

This grant is renewed until August 31, 2026

Abstract

Predictive models of solidification require atomic-scale resolution of the material structure and chemistry. It is surprising then that none of the methodologies for computer-aided engineering employed in industry account for the underlying atomistic nature of the solidification process. Here we propose to develop a rigorous approach to integrate this atomic-scale information into practical solidification models that can be used for additive manufacturing and welding-related applications. Our goal is to elevate the predictive capabilities of these models such that the time-consuming process of experimental trial and error employed in the development of new metallic alloys can be greatly reduced.

MIT PIs
Rodrigo Freitas, Assistant Professor,Department of Materials Science and Engineering

PT PIs
João Pedro Oliveira, Assistant Professor Department of Materials Science Faculdade de Ciências e Tecnologia Universidade NOVA de Lisboa, Portugal

Abstract

Thanks to photovoltaic (PV) textiles, solar power can be harnessed in new ways such as in bags, cloths,
curtains, tents, sails, or construction tarps. However, the existing options for PV cells are either too expensive
or not scalable. Using inkjet printing, an affordable and widely used technique in the textile industry, we can fabricate PV cells on large textile surfaces. It would contribute to modernize Portuguese textile manufacturing and promote sustainability, or even eco-tourism. In this proposal, we outline the steps involved in our solution and how we plan to collaborate with academia and industry in Portugal to make this a reality. Ultimately, this project can participate in making Portugal a hub for high-tech innovation and revolutionize human-computer interfaces such as self powered smart sensing gloves for Augmented/Virtual Reality (AR / VR).

MIT PIs
Joseph Paradiso, Professor,Program in Media Arts and Sciences

PT PIs
Ana Baptista, FCT NOVA

Abstract

In order to reach a carbon neutral building stock, building heating systems have to be electrified. However, system selection often happens later in the design process when key decisions have already been locked in. To address this issue, this research aims to develop new workflows to effectively integrate the selection of  heating ventilation and air- conditioning (HVAC) systems earlier into the design process. We will design and test a series of tools that enable architects to compare a variety of commercial HVAC systems and implement them into their projects during that phase. These new workflows will be tested in architectural education and practice alike.

MIT PIs
Christoph Reinhart, Professor, Department of Architecture

PT PIs
Paulo Ferrão, Professor of Instituto Superior Técnico – University of Lisbon, President of IN+, Center for Innovation, Technology and Policy Research

Abstract

Semantic Simultaneous localization and mapping (SLAM) refers to the ability of a robot to build object-based models of the environment, accounting for uncertainty. To do so, a robot must combine continuous geometric information about its trajectory and object locations with discrete semantic information about object classes.  We are investigation several gaps in existing capabilities, including: (1) the ability to robustly estimate both the shape and the pose of objects, (2) the ability to transfer vision techniques. from terrestrial scenes to underwater scenes, and (3) the ability to create self-improving perception system for robots using semi-supervised machine learning, with location information from SLAM as a supervisory signal.

• (1) the ability to robustly estimate both the shape and the pose of objects, 
• (2) the ability to transfer vision techniques. from terrestrial scenes to underwater scenes, and 
• (3) the ability to create self-improving perception system for robots using semi-supervised machine learning, with location information from SLAM as a supervisory signal.

MIT PIs
John Leonard, Professor, Department of Mechanical Engineering

PT PIs
Nuno Alexandre Cruz, Senior researcher at the Ocean Systems Group, Lecturer at the Dept. of Electrical and Computer Engineering, FEUP, Porto, Portugal

This grant is renewed until August 31, 2026

Abstract

Hart’s research supported by the MIT-Portugal Program focuses on new computational tools and experimental techniques to explore the coupled design-material-economic capabilities of additive manufacturing (AM). In the past two years, his team has developed a physics-based throughput and cost modeling framework for AM, with application to laser-based techniques used widely in industry. Also, the team has invented a high-throughput workflow to predict the mechanical properties and processability of aluminum alloys for use in laser-based AM, and has demonstrated a new alloy composition that leverages metastable phases to retain superior strength than established aluminum alloys. In addition to these topics, the team has formed collaborations with researchers in Portugal focused on quality management in digital manufacturing, applications of AM to architecture and construction, and high-speed extrusion AM of polymer materials.

MIT PIs
John Hart, Professor, Department of Mechanical Engineering

PT PIs
Paulo Sampaio, Professor from University of Minho