The Department of Defense (DOD) is seeking proposals for a Small Business Innovation Research (SBIR) program focused on the topic of "Corrosion Modeling Analytics and Machine Learning to Promote Corrosion-Informed Design to Reduce Ship Maintenance". The objective of this program is to develop protocols and algorithms to transform raw data into information-rich features for machine learning (ML), as well as software and modeling tools for ML that can automatically detect patterns in data and predict optimal materials selection and corrosion control measures to reduce Navy ship maintenance.

The challenge addressed by this program is the integration of data to make informed decisions about lifecycle issues in digital engineering. The Phase I of the program involves defining and developing a concept for feature engineering tools to extract critical information related to corrosion from multiple formats. Phase II focuses on developing and validating a materials database for supervised and unsupervised learning algorithms to be used for corrosion control and life prediction.

The Phase III of the program aims to transition the developed technology for commercialization in ML utilization through original equipment manufacturers (OEMs) or other partnering agreements. The technology has potential dual-use applications in aircraft, land vehicles, and materials processing entities. Commercialization of this technology may be realized by predicting materials service life in marine and modified marine environments in ship systems.

The project duration and funding specifics are not mentioned in the document. For more information and to submit proposals, interested parties can visit the SBIR topic link provided: SBIR Topic Link.