The fault diagnosis of rolling bearings is of utmost importance in industrial applications to ensure mechanical systems' reliability, safety, and economic viability. However, conventional data-driven fault diagnosis techniques mainly depend on a pre-existing dataset with complete failure modes and knowledge to serve as the training data, which may not be available or accessible in some crucial industrial scenarios. This can limit the practicality of these methodologies in real-world industrial applications. This article addresses this issue by developing a novel digital twin-enabled domain adversarial graph network (DT-DAGN). The main contributions of this article are as follows: 1) the development of a comprehensive and accurate digital twin model for rolling bearings that includes a dynamic simulation of the bearing's operational status using only its structural parameters and failure severity/size to obtain the system's vibration response, and 2) the development of a novel graph convolutional network-based transfer learning framework to transfer knowledge from simulated datasets to measured datasets, enabling effective fault diagnostics of bearings with limited knowledge. A series of experiments are applied to validate the efficacy of the developed methodology.
The surface integrity of a large screw in whirlwind milling was vital for the fatigue life. Firstly, the comprehensive evaluation of surface integrity (CESI) was modeled by the Analytic Hierarchy Process (AHP). The weights of selected important factors, such as surface roughness (Ra), surface residue stress (SRS), and full width at half maximum (FWHM), were calculated. Then aimed at minimizing Ra, SRS −200 MPa and maximizing FWHM, the single-factor improvements were achieved under different parameter combination, respectively. Lastly based on single-factor improvements, the CESI was markedly improved by 64% compared to factory processing. Thus, the proposed approach and methodology can be notably adequate for evaluating and improving the surface integrity comprehensively.
Abstract. Amphibious species of frogs are notable candidates to
mimic for amphibious robotic design, as their swimming and sprawling locomotion is generated by the united propulsive mechanisms in which the hindlimbs play the dominant role. Although the propulsive system of frogs is not as complex as other amphibians, it is still difficult to employ the
propulsive mechanism in robotic design due to the numerous degrees of freedom (DoF). This paper proposes a novel united propulsive mechanism to acquire the amphibious function inspired by the frog's hindlimb. The mechanism is a hybrid design combining a planar six-bar linkage, which is designed based on homotopy continuation and a spatial four-bar linkage. The DoF of the hindlimb-like mechanism are dramatically decreased to 2, with 1 each in the two sub-chains. Forward analysis is conducted to find the pose of the foot when two actuations are input. The improved analysis based on the geometrical features overcomes the multiplicity from the
numerical computation. The inverse kinematic analysis is conducted to find the rotation of the input with a given pose of the foot. The aquatic function of the united propulsive mechanism is demonstrated based on the blade element theory, where the rotational speed and the projected area of the foot are fully active. The terrestrial function of the propulsive mechanism is validated with a specific gait.
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