This upcoming 2021-2022 academic year, we are undertaking a new pilot program with our students in the MIT Master of Engineering (M.Eng.) in Advanced Manufacturing and Design Program, implementing data science projects in manufacturing. These one-year-long pilot projects will be launched through the Department of Mechanical Engineering at MIT in the Fall of 2021 and continue through the Fall of 2022.
This collaborative program is designed to develop a cohort of graduates versed in the language and skills of data science, complementing their rigorous preparation in design, manufacturing, and supply chain, while tackling significant challenges and opportunities in the host companies. These small teams, composed of three students, will be supervised collaboratively by MIT faculty advisors and professional mentors in the host companies.
We look forward to seeing you at these sessions. If you have any questions, please do not hesitate to contact us via email at firstname.lastname@example.org.
Join our Roundtable Discussions with Industry
Today we are inviting you to attend two online roundtables at the end of July. These sessions will consist of two parts: the first half will set the context as we share information about the research and education on campus and outline the information about the projects. In the second, we invite you to share your advice and guidance on the development of the program. We also invite you and your company to be part of the inaugural cohort of host companies for these M.Eng. data science projects and to share potential project ideas beneficial for you and your company. A project description example is shared below for your reference.
A Roundtable on Data Science Applications in Manufacturing: From Design through Distribution
A two-part series
Fridays, July 23 & 30, 2021
12-1:30pm EDT via Zoom
On Fridays July 23 and 30, 2021, join us to explore the impact of data science on every aspect of manufacturing and to learn about opportunities for joint pilot projects with the M.Eng. in the MIT Department of Mechanical Engineering. These roundtable sessions are an invitation to share your advice and guidance on the development of the pilot program. We know your participation knowledge and experience in industry will be pivotal to the success of this pilot.
Dr. Brian Anthony will open the session and frame the conversation by sharing some research in data science and its application with partner manufacturing companies. Then, Professor David Hardt and Jose Pacheco will briefly discuss how we are teaching students in the M.Eng. in Advanced Manufacturing Design to become fluent with these new techniques.
These conversations will serve as a jumping off point to facilitate a conversation regarding your insights, business, and industry needs. Space is limited. Please let us know if you want to invite any colleagues from your company. Zoom link will follow registration.
Example Project Description
Proposed Data Science in Manufacturing Pilot Project at XYZ Company
New Engine Technology - Scale up
A new engine technology is highly sensitive to tolerance variation of the assembled product in mass production. Initial efforts to mass produce an advanced engine technology has resulted in critical to market failure modes.
A collaborative project would focus on potential failure modes within the manufacturing process.
The goal is to prevent defect outflow to the vehicle level assembly plants by predicting potential failure as early as possible in the manufacturing process.
The initial suggested approach will start by characterizing the manufacturing process then proceed in developing a predictive model using existing data (incoming component data, assembly process data and quality confirmation data) to identify units that have a high potential to fail and prevent them from leaving the manufacturing facility.
The proposed scope and action plan will be validated in collaboration with our director of engine production, the MIT faculty advisor and the student team (three students). Success will result in a measurable reduction in time to market and scrap and waste, with mutually developed quantified goals. The scope of the project may include some design or supply chain considerations.
The project will be presented on November 2021, begin in January 2022, and conclude by December 15, 2022.
Keywords: predictive maintenance, predictive quality, safety analytics, warranty analytics, plant facilities monitoring, computer vision, sales forecasting, assisted design.