A quadrotor is a rotorcraft capable of hover, forward flight, and VTOL and is emerging as a fundamental research and application platform at present with flexibility, adaptability, and ease of construction. Since a quadrotor is basically considered an unstable system with the characteristics of dynamics such as being intensively nonlinear, multivariable, strongly coupled, and underactuated, a precise and practical model is critical to control the vehicle which seems to be simple to operate. As a rotorcraft, the dynamics of a quadrotor is mainly dominated by the complicated aerodynamic effects of the rotors. This paper gives a tutorial of the platform configuration, methodology of modeling, comprehensive nonlinear model, the aerodynamic effects, and model identification for a quadrotor.

No abstract

Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.

Boron nitride nanosheets (BNNSs) were synthesized by the classical chemical vapor deposition (CVD) method and modified by tannic acid (TA) (named as M-BNNSs). M-BNNSs have greatly improved the thermal conductivity (0.45 W/(m·K)) and mechanical tensile strength (15.10 MPa) of epoxy resin (EP). More importantly, the EP composite coating with an appropriate amount of M-BNNS not only has an excellent anticorrosion effect (the I corr of 10%M-BNNS@EP is 8.053 × 10–7 A/cm2 after immersed in 3.5 wt % NaCl solution for 30 min) on the metal substrates but also has a good anticorrosion stability. The impedance modulus (|Z|0.01) of the 10%M-BNNS@EP coating is higher than that of the neat EP coating and other composite coating systems during 120 h of immersion. Polarization curve (Tafel) and electrochemical impedance spectroscopy (EIS) tests reveal that the 10%M-BNNS@EP coating not only has an excellent anticorrosion effect on the metal substrates but also has a good anticorrosion stability after being immersed in 3.5 wt % NaCl aqueous solution for 120 h.