Join us for an in-depth look at CART - the Cost Analysis for Reman Tool. This webinar will walk through a representative design for remanufacturing scenario to illustrate how CART can aid in data-driven decision-making process. The session will feature both a methodology-focused presentation and a live demonstration of the tool.
Our primary objectives are to:
- Build trust in the analytical accuracy of the tool.
- Highlight the tool's user-friendly and dynamic interface.
Next, industry-based case studies will be shared to validate CART's applicability in remanufacturing contexts. The webinar will conclude with a discussion of current limitations and future directions for CART development. We aim to spend 30 minutes on the presentation and 15 minutes for a Q&A style discussion.
Guest Speakers
Mohammad Mundiwala joined the Reliability Engineering and Informatics Laboratory (REIL) at the University of Connecticut, as Ph.D. student after graduating summa cum laude with honors in Mechanical Engineering. His research, advised by Professor Chao Hu, focuses on data-driven methods for sustainable design and design-for-remanufacturing cost modeling, as well as uncertainty quantification in machine learning-based diagnostics and prognostics for predictive maintenance.
Aidan Lawlor is a Ph.D. student in the Reliability Engineering and Informatics Laboratory (REIL) at the University of Connecticut. His research, supervised by Professor Chao Hu, focuses on machine learning and data-driven diagnostics for battery health and reliability assessment. Through predictive modeling and data driven methods, he aims to support smarter design choices and more sustainable engineering solutions.
