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John Deere

Data-Driven Design Decision Support for Remanufacturing of High-Value Components in Industrial and Agricultural Equipment

Data-Driven Design Decision Support for Remanufacturing of High-Value Components in Industrial and Agricultural Equipment

This project aims to develop and validate a new tool package (D4Reman) for design decision analysis to improve the reuse rates of high-value components at end-of-life. The project is a continuation of exploratory project 18-02-DE-06. Upon completion, this project will create a software tool package (D4Reman) for data-driven design decision support consisting of a cloud-based software application along with an Excel plugin. It will utilize field reliability data and reman reuse data to identify design improvement decisions and quantitatively assess their influences on the initial cost, life-cycle warranty cost (LCWC), and energy and emissions. This project will reduce primary feedstock by 0.55 million metric tons (MMT) of steel and aluminum, 7 PJ energy reduction, 0.42 MMCO2e. Assumptions based on preliminary results of exploratory project.

Project Team:
Iowa State University (ISU), University of Illinois at Urbana-Champaign (UIUC), Mississippi State University, John Deere, Automotive Parts Remanufacturers Association (APRA)

21-01-DE-5071

Rapid Damage Identification to Reduce Remanufacturing Costs

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

In-situ Nondestructive Evaluation of In-flight Particle Dynamics and Intrinsic Properties for Thermal Spray Repairs

In-situ Nondestructive Evaluation of In-flight Particle Dynamics and Intrinsic Properties for Thermal Spray Repairs

The quality of coated surfaces from thermal spray repairs is determined by the particles impacting the surface. A better understanding of in-flight particle dynamics will enable improved success rates for repairs in the remanufacturing industry.

Project Team:
Iowa State University, John Deere

18-01-RM-09

Low Heat Repair of Cast Iron

Low Heat Repair of Cast Iron

The objective of this project is to develop a robust weld repair process that does not require pre-heat temperatures greater than 315°C and shortens cool down periods to less than eight hours. In addition, the process will be able to consistently create a weld with minimal regions of high hardness and no cracks in or around the weld.

Project Team:
Rochester Institute of Technology, John Deere Reman

19-01-RM-04

Data-Driven Design Decision Support for Re-X of High-Value Components in Industrial and Agricultural Equipment

Data-Driven Design Decision Support for Re-X of High-Value Components in Industrial and Agricultural Equipment

This project will create a tool to evaluate and recommend the optimal designs of components in industrial and agricultural equipment. By designing components with optimum material utilization and end-of-life in mind, there is a 60% reduction in carbon emissions.

The novelty of this tool lies in its ability to incorporate real-world load/component health data that has been acquired by condition monitoring systems in the field into early-stage design assessment using random variable models. This approach enables data-informed design for Re-X.

Project Team:
Iowa State University, John Deere

18-02-DE-06

 

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