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- W4385762300 abstract "Abstract Innovative soft robotic grippers, such as granular grippers, enable the automated handling of a wide spectrum of different geometries, increasing the flexibility and robustness of industrial production systems. Granular grippers vary in their design as well as the configuration, which affects the specific characteristics and capabilities regarding grippable objects. Relevant aspects are the selection of granulates and membranes, as they affect the deformability. This results in influences on the achievable gripping forces, which vary with the gripped objects geometry. The present research investigates the influences of different configurations on the achievable gripping forces for an innovative vacuum-based granular gripper concept. Specifically, the focus lies on design as well as configuration parameters, which could influence the achievable gripping force. Influencing parameters are determined based on a literature review of similar gripping concepts. Various adjustment possibilities are identified, such as materials of granulates or membranes. The possible configuration options are experimentally analyzed with a one-factor-at-a-time approach. The possibility of modelling their interrelations is examined with approaches for linear models and compared to interpolations based on Machine Learning. Especially the granulate filling level and the membrane configuration exhibit the largest influences, which were best predicted with the approach based on artificial neural networks. A selection of an optimized gripper configuration for a specified object set as well as possible further developments such as a continuous expandability of the approaches and integrations with simulations are discussed. As a result of these analyses, this research provides methodologies for an optimized selection of a gripper configuration for an improved object-specific achievable gripping force and allows for more efficient handling processes with the examined type of vacuum-based granular gripper." @default.
- W4385762300 created "2023-08-12" @default.
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- W4385762300 date "2023-08-11" @default.
- W4385762300 modified "2023-09-28" @default.
- W4385762300 title "Experimental assessment and prediction of design parameter influences on vacuum-based granular grippers" @default.
- W4385762300 doi "https://doi.org/10.21203/rs.3.rs-3227382/v1" @default.
- W4385762300 hasPublicationYear "2023" @default.
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