Bead sttape in underwater rotating arc welding was affected by several welding parameters. RVM （ relevance vector machine） was used to build a model to predict weld bead shape. The training data set of RVM eortsists...Bead sttape in underwater rotating arc welding was affected by several welding parameters. RVM （ relevance vector machine） was used to build a model to predict weld bead shape. The training data set of RVM eortsists of the welding parameters which are rotational frequency, rotational radius, height of torch and welding current and the features of the bead shape. The maximum error and mean error for prediction of width are 0. 10 mm and 0. 09 mm, respectively, and the maximum error and mean error for prediction of penetration are 0. 31 mm and 0. 12mm, respectively, which are showed that the prediction model can achieve higher prediction precision at reasonably small size of training data set.展开更多
The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles(UAVs)technology.Herein,rotor UAVs are increasingly favored by consumers due to their un...The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles(UAVs)technology.Herein,rotor UAVs are increasingly favored by consumers due to their unique advantages.The UAVs motion is altered by adjusting propeller speed,which is governed by motor speed.Consequently,motor speed is a key factor influencing flight performance that is susceptible to environmental interference.Accurate and real-time monitoring of motor speed is essential.Conventional speed sensors are bulky,reliant on external power,and challenging to integration into compact UAVs systems.They also suffer from insufficient accuracy and unstable measurements,particularly with small motors.This article introduces a self-powered digital aircraft rotational speed sensor(SDARSS)utilizing a rotating triboelectric nanogenerators(TENGs)to address current challenges.This sensor is lightweight,energy-efficient,and self-powered,weighing only 2.185 g and measuring 3.43 mm in thickness,with an accuracy exceeding 99.94%.It measures speeds up to 10,000 revolutions per minute(rpm)with exceptional precision and stability.The sensor enables real-time monitoring of UAVs motor speeds,which is crucial for enhancing flight safety.展开更多
基金The authors wish to thank the financial support for this research from National Natural Science Foundation of China （ No. 50705030） , Natural Science Foundaiion of Guangdong Province of China （No. 9151008019000008 ） and the Fundamental Research Funds for the Central Universities （No. 2009ZM0318）.
文摘Bead sttape in underwater rotating arc welding was affected by several welding parameters. RVM （ relevance vector machine） was used to build a model to predict weld bead shape. The training data set of RVM eortsists of the welding parameters which are rotational frequency, rotational radius, height of torch and welding current and the features of the bead shape. The maximum error and mean error for prediction of width are 0. 10 mm and 0. 09 mm, respectively, and the maximum error and mean error for prediction of penetration are 0. 31 mm and 0. 12mm, respectively, which are showed that the prediction model can achieve higher prediction precision at reasonably small size of training data set.
基金supported by the National Natural Science Foundation of China(No.52203324)the National Key R&D Program of China(No.2023YFB2604600)+1 种基金the National Key R&D Project from the Minister of Science and Technology(No.2021YFA1201601)Thanks to the Georgia Institute of Technology for providing software support。
文摘The low-altitude transport has demonstrated significant growth potential driven by rapid advancements in unmanned aerial vehicles(UAVs)technology.Herein,rotor UAVs are increasingly favored by consumers due to their unique advantages.The UAVs motion is altered by adjusting propeller speed,which is governed by motor speed.Consequently,motor speed is a key factor influencing flight performance that is susceptible to environmental interference.Accurate and real-time monitoring of motor speed is essential.Conventional speed sensors are bulky,reliant on external power,and challenging to integration into compact UAVs systems.They also suffer from insufficient accuracy and unstable measurements,particularly with small motors.This article introduces a self-powered digital aircraft rotational speed sensor(SDARSS)utilizing a rotating triboelectric nanogenerators(TENGs)to address current challenges.This sensor is lightweight,energy-efficient,and self-powered,weighing only 2.185 g and measuring 3.43 mm in thickness,with an accuracy exceeding 99.94%.It measures speeds up to 10,000 revolutions per minute(rpm)with exceptional precision and stability.The sensor enables real-time monitoring of UAVs motor speeds,which is crucial for enhancing flight safety.
