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.