Rapid Damage Identification to Reduce Remanufacturing Costs
The objective of this project is to develop and validate a remanufacturability assessment method that will support decision making about the viability of remanufacturing a component. The proposed method is based on development of machine learning (ML) techniques for recognizing different types of component damage, embedding developed ML algorithms in low-cost, damage-identification hardware for use in-process at the remanufacturing factory floor, and using this in-process technique to develop a real- time estimate of remanufacturing costs for a component. Although most high-value, metal-alloy components can be remanufactured, sufficiently accurate and rapid decision making support tools are needed to significantly reduce remanufacturing costs and increase the throughput and volume of remanufactured components.
Project Team:
Iowa State University, John Deere & Company
19-01-RM-05