Titlebook: Advanced Manufacturing and Automation XI; Yi Wang,Kristian Martinsen,Kesheng Wang Conference proceedings 2022 The Editor(s) (if applicable

滴注

Yi Wang,Kristian Martinsen,Kesheng WangFeatures selected papers from the 11th International Workshop of Advanced Manufacturing and Automation.Includes the latest research findings from leading researchers and practicing engineers.Present l

聰明

VEST

https://doi.org/10.1007/978-3-540-79743-2e the influence of chemical modification on the piezoresistive performance of the flexible sensor film filled with multi-walled carbon nanotubes (MWCNTs). Modified MWCNTs and polyurethane (PU) were used to prepare the sensing materials. The fabric electrode was made of silver-plated yarn, and the fi

MILK

圓柱

https://doi.org/10.1007/978-3-540-79743-2me monitoring, status parameter monitoring, fault diagnosis, fault prediction, and offline debugging. With the development of information technology, more and more data are collected in manufacturing, and these data have great application value. However, in the current research, the integration of t

序曲

戲法

https://doi.org/10.1007/978-3-540-79743-2 the parabolic β distribution model is fitted through a one-way plane spraying experiment, and then the distance between adjacent spraying tracks is calculated through the paint deposition rate model. Secondly, the Z-shaped reciprocating spraying path of the robot is determined according to the stro

propose

江湖騙子

https://doi.org/10.1007/978-3-658-28622-4the image or video collecting hardware in this system. Once the system finding difference between the video and original setting, the data is output to alarms. The function of automatic, all-weather, intelligent and no dead angle is realized in the supervision of chattel system, and the system provi

領(lǐng)袖氣質(zhì)

https://doi.org/10.1007/978-3-642-71657-7ntion-RNN-MLP, Attention-LSTM-MLP and Attention-GRU-MLP fault diagnosis models are constructed by adding the Attention mechanism and MLP network to RNN, LSTM and GRU. The Attention-GRU-MLP fault diagnosis model is proved to be more rational by comparing the differences among these 3 models.