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1. Automatic Screw Detection and Tool Recommendation System for Robotic Disassembly

   https://doi.org/10.1115/1.4056074  

   Zhang, Xinyao; Eltouny, Kareem; Liang, Xiao; Behdad, Sara (March 2023, Journal of Manufacturing Science and Engineering)

   Abstract Disassembly is an essential process for the recovery of end-of-life (EOL) electronics in remanufacturing sites. Nevertheless, the process remains labor-intensive due to EOL electronics’ high degree of uncertainty and complexity. The robotic technology can assist in improving disassembly efficiency; however, the characteristics of EOL electronics pose difficulties for robot operation, such as removing small components. For such tasks, detecting small objects is critical for robotic disassembly systems. Screws are widely used as fasteners in ordinary electronic products while having small sizes and varying shapes in a scene. To enable robotic systems to disassemble screws, the location information and the required tools need to be predicted. This paper proposes a computer vision framework for detecting screws and recommending related tools for disassembly. First, a YOLOv4 algorithm is used to detect screw targets in EOL electronic devices and a screw image extraction mechanism is executed based on the position coordinates predicted by YOLOv4. Second, after obtaining the screw images, the EfficientNetv2 algorithm is applied for screw shape classification. In addition to proposing a framework for automatic small-object detection, we explore how to modify the object detection algorithm to improve its performance and discuss the sensitivity of tool recommendations to the detection predictions. A case study of three different types of screws in EOL electronics is used to evaluate the performance of the proposed framework. 

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2. 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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3. Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human–Robot Collaboration

   https://doi.org/10.1115/1.4055901  

   Liao, Hao-yu; Chen, Yuhao; Hu, Boyi; Behdad, Sara (February 2023, Journal of Mechanical Design)

   Abstract Disassembly is an essential step for remanufacturing end-of-life (EOL) products. Optimization of disassembly sequences and the utilization of robotic technology could alleviate the labor-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human–robot collaboration. The proposed framework combines three attributes: disassembly cost, safety, and complexity of disassembly, namely disassembleability, to identify the optimal disassembly path and allocate operations between human and robot. A multi-attribute utility function is used to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly which is assumed to be an uncertain parameter with a Beta distribution; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the protection of human workers in the work environment. An example of dismantling a desktop computer is used to show the application. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations among human and robot. A sensitivity analysis is conducted to show the model's performance when changing the disassembly cost for the robot. 

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4. Screw connections to full-culm bamboo

   Morrill, P.; Rogers, C.; Flower, C.; Akinbade, Y.; Harries, K.A. (January 2019, 18th International Conference on Non-Conventional Materials and Technologies (18NOCMAT))

   The use of screw connections in full-culm bamboo is often assumed to be limited, primarily due to the propensity for splitting of the culm. This study demonstrates that small diameter screws can be used effectively in full-culm bamboo. The study explores the withdrawal capacity of candidate screw types in order to identify those that may be used to achieve a high capacity while mitigating splitting failures. Twelve screw types of three standard sizes, ranging from hardwood screws, self-tapping wood screws and concrete anchors, are tested in conditions of both pre-drilled and self-tapping installation procedures. All tests are conducted on samples of P. edulis (Moso) having culm wall thickness on the order of 7 mm. The results of this study are intended to inform the applications for which screw connection to bamboo are viable. 

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5. Screw connections to full-culm bamboo

   Morrill, P.; Rogers, C.; Flower, C.; Akinbade, Y.; Harries, K.A.. (January 2019, 18th International Conference on Non-Conventional Materials and Technologies (18NOCMAT))

   The use of screw connections in full-culm bamboo is often assumed to be limited, primarily due to the propensity for splitting of the culm. This study demonstrates that small diameter screws can be used effectively in full-culm bamboo. The study explores the withdrawal capacity of candidate screw types in order to identify those that may be used to achieve a high capacity while mitigating splitting failures. Twelve screw types of three standard sizes, ranging from hardwood screws, self-tapping wood screws and concrete anchors, are tested in conditions of both pre-drilled and self-tapping installation procedures. All tests are conducted on samples of P. edulis (Moso) having culm wall thickness on the order of 7 mm. The results of this study are intended to inform the applications for which screw connection to bamboo are viable. 

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