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1. 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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2. 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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3. Automatic Screw Detection and Tool Recommendation System for Robotic Disassembly

   https://doi.org/10.1115/MSEC2022-85403  

   Zhang, Xinyao; Eltouny, Kareem; Liang, Xiao; Behdad, Sara (June 2022, the ASME Manufacturing Science and Engineering Conference (MSEC))

   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 achieve robotic disassembly of screws, the location information and the required tools need to be predicted. This paper proposes a framework to automatically detect screws and recommend related tools for disassembly. First, the YOLOv4 algorithm is used to detect screw targets in EOL electronic devices, and then 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 is used to evaluate the performance of the proposed framework. 

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4. Flat Ring Flexure Test for Full-Culm Bamboo

   https://doi.org/10.21741/9781945291838-32  

   J. Virgo, R. Moran (October 2018, Materials Research Proceedings)

   The development of a new simple test method suitable for assessing the tension strength perpendicular to the fibres (Mode I) of a bamboo culm – the flat ring flexure test – is presented. The proposed test places a short section of bamboo culm in through-cross section flexure, causing circumferential stresses at failure. The modulus of rupture at the failed section is a measure of the transverse tensile strength of the culm. The test is compression-based and uses a simple apparatus and specimen. The full culm specimen is symmetric and requires very little preparation. The study first investigated test parameters affecting results, thereby arriving at an appropriate and repeatable standard test method. The resulting test method is documented in a format consistent with ISO 22157. 

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5. The Structure‐Mechanics Relationship of Bamboo‐Epidermis and Inspired Composite Design by Artificial Intelligence

   https://doi.org/10.1002/adma.202414970  

   Qin, Zhao; Destree, Aymeric_Pierre (December 2024, Advanced Materials)

   Abstract Bamboo culm has been widely used in engineering for its high strength, lightweight, and low cost. Its outermost epidermis is a smooth and dense layer that contains cellulose, silica particles, and stomata and acts as a water and mechanical barrier. Recent experimental studies have shown that the layer has a higher mechanical strength than other inside regions. Still, the mechanism is unclear, especially for how the low silica concentration (<10%) can effectively reinforce the layer and prevent the inner fibers from splitting. Here, theoretical analysis is combined with experimental imaging and 3D printing to investigate the effect of the distribution of silica particles on composite mechanics. The anisotropic partial distribution function of silica particles in bamboo skin yields higher toughness (>10%) than randomly distributed particles. A generative artificial intelligence (AI) model inspired by bamboo epidermis is developed to generate particle‐reinforced composites. Besides the visual similarity, it is found that the samples by the generative model show failure processes and fracture toughness identical to the actual bamboo epidermis. This work reveals the micromechanics of the bamboo epidermis. It illustrates that generative AI can help design bio‐inspired composites of a complex structure that cannot be uniformly represented by a simple building block or optimized around local boundaries. It expands the design space of particle‐reinforced composites for enhanced toughness modulus, offering advantages in industries where mechanical reliability is critical. 

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