Manufacturing

Prof. Jesús del Alamo speaks with Ann Fisher of WOSU’s All Sides with Ann Fisher about the importance of supporting domestic chip manufacturing in the U.S., and the need to help encourage students to pursue careers in the semiconductor industry. “Universities and colleges train over 50% of the semiconductor workforce,” says del Alamo, “and so investing in education, investing in the infrastructure, both human but also physical infrastructure that supports education and research, is really critical in the long run.” 

Infinite Cooling, an MIT startup, is developing a new system that can capture water from cooling tower plumes and could significantly reduce water consumption in evaporative cooling tower systems, reports Sonal Patel for Power Magazine. “The technology that is developed could lead to significant water savings and improve water quality with minimal energy cost,” explained members of Prof. Kripa Varanasi’s lab.

Jake Guglin MBA ’19, Jasper Lienhard PhD ’22, Prof. Chris Schuh and University of California Irvine Prof. Tim Rupert have founded Foundation Alloy, a vertically integrated metal part production platform specializing in manufacturing high performing metal parts, reports Ariyana Griffin for Forbes. “By creating stronger metals, we can make lighter parts for planes, cars [which] will make those existing products greener and more efficient,” says Guglin.

Boston Metal, an MIT spinout, has created a new manufacturing method that could help engineers reshape the way in which alloy is made, reports Marcello Rossi for The Atlantic. The process is “called ‘molten oxide electrolysis,’ in which a current moves through a cell containing iron ore,” explains Rossi.

Forbes reporter Trond Arne Undheim spotlights the “Manufacturing at MIT Symposium: 2022 and Beyond” conference. “MIT appears to be renewing its manufacturing research and innovation efforts at a pivotal time, with a four-fold focus on technology, workforce development, policy efforts and innovation,” writes Undheim.

MIT scientists have created a new tool that can improve robotic wearables, reports Danica D’Souza for Mashable. “The tool provides a pipeline for digital creating pneumatic actuators – devices that power motion with compressed air in many wearables and robotics,” writes D’Souza.

CSAIL researchers have developed a robotic glove that utilizes pneumatic actuation to serve as an assistive wearable, reports Brian Heater for TechCrunch. “Soft pneumatic actuators are intrinsically compliant and flexible, and combined with intelligent materials, have become the backbone of many robots and assistive technologies – and rapid fabrication with our design tool can hopefully increase ease and ubiquity,” says graduate student Yiyue Luo.

Prof. Jessika Trancik speaks with Wall Street Journal reporter Nidhi Subbaraman about the dramatic drops in costs to manufacture and sell renewable technologies. Subbaraman notes that Trancik’s research shows that “the steep drop in solar and lithium-ion battery technology was enabled by market expansion policies as well as investment in research and development by governments and the private sector.”

The MIT AI Hardware Program seeks to bring together researchers from academia and industry to “examine each step of designing and manufacturing the hardware behind AI-powered technologies,” reports Emily Bamforth for EdScoop. “This program is about accelerating the development of new hardware to implement AI algorithms so we can do justice to the capabilities that computer scientists are developing,” explains Prof. Jesús del Alamo.

MIT researchers have developed reconfigurable, self-assembling robotic cubes embedded with electromagnets that allow the robots to easily change shape, reports John Koetsier for Forbes. “If each of those cubes can pivot with respect to their neighbors you can actually reconfigure your first 3D structure into any other arbitrary 3D structure,” explains graduate student Martin Nisser.

The MIT AI Hardware Program is aimed at bringing together academia and industry to develop energy-optimized machine-learning and quantum-computing systems, reports Katyanna Quach for The Register. “As progress in algorithms and data sets continues at a brisk pace, hardware must keep up or the promise of AI will not be realized,” explains Professor Jesús del Alamo. “That is why it is critically important that research takes place on AI hardware."

MIT researchers have utilized a new reinforcement learning technique to successfully train their mini cheetah robot into hitting its fastest speed ever, reports Matt Simon for Wired. “Rather than a human prescribing exactly how the robot should walk, the robot learns from a simulator and experience to essentially achieve the ability to run both forward and backward, and turn – very, very quickly,” says PhD student Gabriel Margolis.

MIT researchers have created a new computer algorithm that has allowed the mini cheetah to maximize its speed across varying types of terrain, reports Shi En Kim for Popular Science. “What we are interested in is, given the robotic hardware, how fast can [a robot] go?” says Prof. Pulkit Agrawal. “We didn’t want to constrain the robot in arbitrary ways.”

MIT researchers have used a new reinforcement learning system to teach robots how to acclimate to complex landscapes at high speeds, reports Emmett Smith for Mashable. “After hours of simulation training, MIT’s mini-cheetah robot broke a record with its fastest run yet,” writes Smith.

CSAIL researchers developed a new machine learning system to teach the MIT mini cheetah to run, reports James Vincent for The Verge. “Using reinforcement learning, they were able to achieve a new top-speed for the robot of 3.9m/s, or roughly 8.7mph,” writes Vincent.