Results 161 to 170 of about 73,182 (264)
Modular, Textile‐Based Soft Robotic Grippers for Agricultural Produce Handling
This article introduces textile‐based pneumatic grippers that transform simple textiles into robust bending actuators. Detailed experiments uncover how cut geometry and fabric selection shape performance. Successful handling of fragile agricultural items showcases the potential of textile robotics for safe, scalable automation in food processing and ...
Zeyu Hou +4 more
wiley +1 more source
Tool wear prediction based on XGBoost feature selection combined with PSO-BP network. [PDF]
Lin Z +6 more
europepmc +1 more source
Bioinspired Soft‐Skeleton Robotics With Cooperative Shape Transformation and Stiffness Adaptation
A bioinspired soft‐skeleton robot that integrates variable‐stiffness chain skeletons into a soft textile skin is developed. The chain skeletons are highly flexible initially but can stiffen upon pneumatic actuation, enabling large deformation and enhanced load‐bearing capacity.
Mengyuan Niu +5 more
wiley +1 more source
Deep Learning Tool Wear State Identification Method Based on Cutting Force Signal. [PDF]
Li S, Li M, Gao Y.
europepmc +1 more source
This work presents the MicroRoboScope, a highly integrated, compact, and portable microrobotic experimentation platform combining electromagnetic and acoustic actuation with real‐time visual feedback into a single, end‐to‐end device. The system enables closed‐loop control and tracking algorithm experimentation within an accessible and unified hardware ...
Max Sokolich +4 more
wiley +1 more source
A multi-feature dataset of coated end milling cutter tool wear whole life cycle. [PDF]
Li N +5 more
europepmc +1 more source
DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano +3 more
wiley +1 more source
Tool Wear Prediction in Machining of Aluminum Matrix Composites with the Use of Machine Learning Models. [PDF]
Hamrol A +3 more
europepmc +1 more source
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Influence of Machining Parameters on the Surface Roughness and Tool Wear During Slot Milling of a Polyurethane Block. [PDF]
Szadkowska K +8 more
europepmc +1 more source

