Forest fruit harvesting is carried out through two ways: manual harvesting and machine harvesting. Mechanical vibration will become the future development trend because of the high harvesting efficiency and short picking cycle of this method. However, appropriate vibration frequency, vibration amplitude, and action time are required to improve the fruit harvesting rate. At the same time, it is ensured that the vibration type forest fruit harvesting machine is in the harvesting process to minimize the damage to the fruit trees. The vibration frequency and amplitude of the existing vibration harvesting machine are relatively single, and the result of energy transfer loss is extensive fruit tree damage. Therefore, this study proposes a branch-stalk-fruit of apple as a base system. The article investigates the vibration mechanism and separation deformation law of apple tree branches through dynamic tests and simulations. Through experiments, it was found that when the vibration frequency is 3 Hz and the vibration amplitude is 20 mm, the maximum separation force experienced by the apple is 18.71N, greater than the binding force between the stem and the branch, and can achieve fruit shedding. This study provides some theoretical insights into the efficient mechanical vibration harvesting of apples.

The key technology of automated apple harvesting is detecting apples quickly and accurately. The traditional detection methods of apple detection are often slow and inaccurate in unstructured orchards. Therefore, this article proposes an improved YOLOv5s-GBR model for orchard apple detection under complex natural conditions. First, the researchers collected photos of apples in their natural environments from different angles; then, we enhanced the dataset by changing the brightness, rotating the images, and adding noise. In the YOLOv5s network, the following modules were introduced to improve its performance: First, the YOLOv5s model’s backbone network was swapped out for the GhostNetV2 module. The goal of this improvement was to lessen the computational burden on the YOLOv5s algorithm while increasing the detection speed. Second, the bi-level routing spatial attention module (BRSAM), which combines spatial attention (SA) with bi-level routing attention (BRA), was used in this study. By strengthening the model’s capacity to extract important characteristics from the target, its generality and robustness were enhanced. Lastly, this research replaced the original bounding box loss function with a repulsion loss function to detect overlapping targets. This model performs better in detection, especially in situations involving occluded and overlapping targets. According to the test results, the YOLOv5s-GBR model improved the average precision by 4.1% and recall by 4.0% compared to those of the original YOLOv5s model, with an impressive detection accuracy of 98.20% at a frame rate of only 101.2 fps. The improved algorithm increases the recognition accuracy by 12.7%, 10.6%, 5.9%, 2.7%, 1.9%, 0.8%, 2.6%, and 5.3% compared to those of YOLOv5-lite-s, YOLOv5-lite-e, yolov4-tiny, YOLOv5m, YOLOv5l, YOLOv8s, Faster R-CNN, and SSD, respectively, and the YOLOv5s-GBR model can be used to accurately recognize overlapping or occluded apples, which can be subsequently deployed in picked robots to meet the realistic demand of real-time apple detection.

Bone fracture is an extremely dangerous health risk to human. Actually, cortical bone is often subjected to the complicated loading patterns. The mechanical properties and deformation mechanism under the complicated loading pattern could provide a more precise understanding for the bone fracture. For this purpose, the mechanical response and multi-scale deformation mechanism of cortical bone material were investigated by in-situ experimental research using the compression-torsion coupling loads as an example. It was found that the torsion strength and shear modulus all decreased under the compression-torsion coupling loads than single torsion load. This indicated bone would suffer greater risk of fracture under the compression-torsion coupling loads. Based on in-situ observation, it was found that the rapid reduction of the anisotropy of bone material under the compression load was the potential influencing factor. Because of the redistribution of the principal strain and the variations of cracks propagation, the comprehensive fracture pattern containing both transverse and longitudinal fracture was shown under the coupling loads, and finally resulted in the reduction of the torsion properties. This research could provide new references for researches on mechanical properties of cortical bone material under complicated loading patterns.

The wind turbine blade is the core component of the wind turbine. However, wind turbine blades will suffer fatigue and internal crack defects, which seriously affect the safety service performance in harsh environments. The current detection mode is mainly traditional manual detection and shutdown maintenance, with high risk and low efficiency. Therefore, this research proposed a method to conduct a non-stop online inspection of the wind turbine blade’s internal defects using ultrasonic inspection mounted unmanned aerial vehicle. As the vital component of this inspection system, the optimized design of the negative pressure adsorption car was carried out. The structural characteristics of the negative pressure adsorption car were determined experimentally, followed by multivariate analysis and an integrated development and performance test study of the test system. Based on the test results, the detection rate of this test system for internal wind power blade faults is 100%. This research offers novel concepts of and theoretical insights into the intelligent and security identification of internal flaws in wind turbine blades.

Vibrating screens have wide applications in various industries, ranging from agriculture to coal mining. In recent years, improvement in production have put forward higher requirements on the efficiency of vibrating screens. However, these large screens are prone to fatigue damage, particularly crack formation, over time. The emergence of substantial dynamic inertial forces can be attributed to the considerable body mass of a large vibrating screen. This study analyzed the structural strength of the HZXZ200x300 large vibrating screen, employing finite element simulation to identify the maximum equivalent stress and primary stress distribution. The main frame model of the vibrating screen box was constructed by integrating the equivalent static load and submodule methods. The model transitioned from dynamic response to static optimization under equivalent static and dynamic loads, significantly reducing calculation scale and enhancing optimization efficiency. Equivalent static sub-models were employed for topology optimization, determining the optimal structure for material performance distribution. This process yielded an optimal conceptual model for reconstructing the actual model. The structural strength was further improved by comparing three-dimensional and dynamic local stress relationships post-topological optimization, followed by reinforcing the vibrating screen structure and introducing reinforced bars for increased stability. The study showed that lightweight topology optimization significantly decreased the stress levels and improved the fatigue durability of beams. Local strengthening, accomplished via topology optimization, effectively reduced the maximum equivalent stress to 76.487 MPa, a decrease of 46.8%. The vibrating screen mass also decreased by 225 kg, a reduction of 14.9%. In summary, this study employed structural analysis, topology optimization, and local reinforcement to mitigate stress levels, enhance the fatigue life of a vibrating screen, and reduce its weight. This study offers an effective solution to the dynamic optimization challenges of complex structures.

Objective service load is the load pattern of cortical bone in practical conditions. The objective service load conditions of cortical bone are complicated, usually including two or more load patterns. The mechanical behavior and deformation mechanism of cortical bone material under coupling load pattern and single load pattern are diametrically different. However, nowadays, researches on the mechanical response of cortical bone have been heavily focused on the single load pattern, which couldn’t reveal the potential deformation mechanism accurately. For the purpose of obtaining the objective mechanical properties under complicated loading patterns, the mechanical response and deformation mechanism of bone material under compression-bending coupling load were investigated by in-situ test. The research shows that bending strength increased under the compression-bending coupling load than the single bending load. By in-situ observation, the variations of surface strain distribution and cracks directions were the potential reasons for the increase of the bending strength. It was found that the cracks changed from transverse fracture to integrated patterns with transverse fracture and longitudinal fracture. Larger fracture range and tortuous crack propagation increased the fracture energy dissipation, which led to an enlarged bending strength under the compression-bending coupling load. Through theoretical analysis and numerical calculation, the impeded effect to the increasing of bending deflection was dominant before the final fracture with the adding of the compression load. The numerical calculation result was consistent with the result of the experiment. This present work would provide new references to further studies on the mechanical behavior of cortical bone under complicated loading patterns.