Lunar rock investigation and tri-aspect characterization of lunar farside regolith by a digital twin.

Yutu-2 rover conducted an exciting expedition on the 41st lunar day to investigate a fin-shaped rock at Longji site (45.44°S, 177.56°E) by extending its locomotion margin on perilous peaks. The varied locomotion encountered, especially multi-form wheel slippage, during the journey to the target rock, established unique conditions for a fin-grained lunar regolith analysis regarding bearing, shear and lateral properties based on terramechanics. Here, we show a tri-aspect characterization of lunar regolith and infer the rock's origin using a digital twin. We estimate internal friction angle within 21.5°-42.0° and associated cohesion of 520-3154 Pa in the Chang'E-4 operational site. These findings suggest shear characteristics similar to Apollo 12 mission samples but notably higher cohesion compared to regolith investigated on most nearside lunar missions. We estimate external friction angle in lateral properties to be within 8.3°-16.5°, which fills the gaps of the lateral property estimation of the lunar farside regolith and serves as a foundational parameter for subsequent engineering verifications. Our in-situ spectral investigations of the target rock unveil its composition of iron/magnesium-rich low-calcium pyroxene, linking it to the Zhinyu crater (45.34°S, 176.15°E) ejecta. Our results indicate that the combination of in-situ measurements with robotics technology in planetary exploration reveal the possibility of additional source regions contributing to the local materials at the Chang'E-4 site, implying a more complicated geological history in the vicinity.

Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method.

Resistance spot welding (RSW) ultrasonic testing signal contains nugget size and internal defect information which can reflect the mechanical property of spot-welded joint. The mechanical property of spot-welded joint is the most direct indicator for evaluation of spot welding quality. In this paper, 100 samples of different quality spot-welded joints are detected by ultrasonic detection technology, then ultrasonic signals are processed by fast Fourier transform (FFT) and wavelet packet transform (WPT). After that, mathematical statistical methods are used to feature extraction for ultrasonic detection signals in time domain, frequency domain, and wavelet domain based on WPT. 100 samples are subjected to tensile-shear tests to obtain the maximum tensile-shear strength (MTSS) that is used as the classification identifier here. Finally, back-propagation (BP) neural network classifier and particle swarm optimization support vector machine (PSO-SVM) classifier are used to classify the MTSS of spot-welded joints and comparing the accuracy of the two classifiers with different number of features. The results show that the PSO-SVM classifier with all 9 features has a good accuracy, which verifies the feasibility and correctness of the spot welding quality classification method proposed in this paper.