Roundtables on Data Science Applications in Manufacturing:  From Design through Distribution                          
Hosted by the MIT Master of Engineering (M.Eng) in
Advanced Manufacturing and Design     E: mengprojects@mit.ed
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Friday, July 23 or 30, 2021                                     12:00 pm-1:30 pm  via Zoom
These roundtable sessions are an invitation to share your advice and guidance on the development of the pilot program in data science in manufacturing and to share project ideas that will be positively impactful to you and your company.

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, Prof. 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. Zoom link will follow registration.

Your Name: *
Company: *
Title:
Address:
Which session will you be attending: *
How did you hear about this event? *
If you have a potential project at your company you wish to share in the seminar, please provide a brief description here, including context, goals and metric for success. (Alternatively, please send your project description to mengprojects@mit.edu) You may find an example project description below.
Example Data Science Project :
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, 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 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 concluding by  December 15, 2022.

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