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1. AI-driven value assessment for intelligent remanufacturing; Advances in remanufacturing 2024: Proceedings of VIII International Workshop on Autonomous Remanufacturing (Lecture Notes in Mechanical Engineering)

   Esmaeilian, Behzad; Behdad, Sara (September 2025, Springer)

   Rickli, Jeremy (Ed.)

   This paper aims to introduce an Artificial Intelligence (AI) guided computational framework for the automatic identification, inspection, assessment, and remanufacturing of end-of-use products. The proposed framework consists of three main steps: (1) developing computer vision and image processing algorithms for analyzing product teardown images, (2) quantifying the economic and environmental value of remanufacturing from product images, and (3) developing recommender algorithms to identify the best recovery decision for each device. The paper discusses the importance of advancing object detection, image segmentation, and machine learning algorithms to automatically compute the value embedded in discarded items and developing recommendation systems to determine remanufacturing operations from product configurations. The main focus of the paper is on the value assessment and remanufacturing of electronic waste (e-waste). The paper emphasizes the need for developing object detection for identifying small objects (e.g., screws, bolts, snaps) and overlapped components (e.g., cables, printed circuit boards) standard in the design of consumer electronics by incorporating product shapes and features. The proposed value assessment framework has applications beyond remanufacturing and can be used in take-back programs and other business models that benefit from product serialization and assessment of individual devices. 

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2. Data-driven remanufacturing planning with parameter uncertainty

   https://doi.org/10.1016/j.ejor.2023.01.031  

   Zhu, Zhicheng; Xiang, Yisha; Zhao, Ming; Shi, Yue (August 2023, European Journal of Operational Research)
3. AI‑Driven Value Assessment for Intelligent Remanufacturing.

   Esmaeilian, Behzad; Behdad, Sara (September 2025, Advances in Remanufacturing 2024: Proceedings of VIII International Workshop on Autonomous Remanufacturing (Lecture Notes in Mechanical Engineering). Springer, 2024.)

   Rickli, Jeremy (Ed.)
4. AI-Driven value assessment for intelligent remanufacturing

   Esmaeilian, Behzad; Behdad, Sara (August 2025, Advances in Remanufacturing 2024: Proceedings of VIII International Workshop on Autonomous Remanufacturing (Lecture Notes in Mechanical Engineering, pp. xx–xx). Springer Cham.)

   Rickli, J (Ed.)
5. A Polymer Degradation and Remanufacturing Experiment in the High School Classroom

   https://doi.org/10.1021/acs.jchemed.3c00692  

   Lim, Y. Justin; Wong, Brandon; Macfee, Katie; Cueva, Alexa; Martinez, E. Aaron; Paxton, Cameron; Barnes, Robin; Kleinsasser, Eric; Williams, Travis J. (December 2023, Journal of Chemical Education)

   Most students enter college without any exposure to polymer science, which leads to the poor understanding and slow implementation of plastics recycling programs in the United States. To address the knowledge gap in chemical recycling, we introduce a 2-part laboratory experiment that was conducted in multiple high schools and public outreach events to demonstrate the depolymerization of PET via aminolysis and the remanufacturing of cleaved PET fragments into a new aramid polymer. Student experiences were evaluated with two post-lab assignments. 

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