The objective of this study was to improve the surface properties, hardness, wear resistance and electrochemical corrosion resistance of #45 steel. To this end, Ni–P–ZrO₂–CeO₂ composite coatings were prepared on the surface of #45 steel using the jet-electrodeposition technique by varying the current density from 20 to 60 A/dm². The effect of current density on the performance of the composite coatings was evaluated. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) were applied to explore the surface topography, elemental composition, hardness and electrochemical corrosion resistance of the composite coatings. The results showed that with the increase in the current density, the hardness, wear resistance, and electrochemical corrosion resistance tends to increase first and then decrease. At a current density of 40 A/dm², the hardness reached a maximum of 688.9 HV_0.1, the corrosion current reached a minimum of 8.2501 × 10⁻⁵ A·cm⁻², and the corrosion potential reached a maximum of −0.45957 V. At these values, the performance of the composite coatings was optimal.

The detection of the impurity rate in machine-picked seed cotton is crucial for precision agriculture. This study proposes a novel Cotton-YOLO-Seg cotton-impurity instance segmentation algorithm based on the you only look once version 8 small segmentation model (Yolov8s-Seg). The algorithm achieves precise pixel-level segmentation of cotton and impurities in seed cotton images and establishes a detection model for the impurity rate, enabling accurate detection of the impurity rate in machine-picked cotton. The proposed algorithm removes the Pyramid 4 (P4) feature layer and incorporates Multi-Scale Convolutional Block Attention (MSCBCA) that integrates the Convolutional Block Attention Module (CBAM) and Multi-Scale Convolutional Attention (MSCA) into the Faster Implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module of the feature extraction network, forming a novel C2f_MSCBCA module. The SlimNeck structure is introduced in the feature fusion network by replacing the P4 feature layer with the small-target detection layer Pyramid 2 (P2). Additionally, transfer learning is employed using the Common Objects in Context (COCO) instance segmentation dataset. The analysis of 100 groups of cotton image samples shows that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) for impurity rate detection are 0.29%, 0.33%, and 3.70%, respectively, which are reduced by 52.46%, 48.44%, and 53.75% compared to the Yolov8s-seg model. The Precision (P), Recall (R), and mean Average Precision at an intersection over union of 0.5 (mAP@0.5) are 85.4%, 78.4%, and 80.8%, respectively, which are improved by 4.2%, 6.2%, and 6.4% compared to Yolov8s-seg model, significantly enhancing the segmentation performance of minor impurities. The Cotton-YOLO-Seg model demonstrates practical significance for precisely detecting the impurity rate in machine-picked seed cotton.

This study aimed to investigate the wear failure changes of spindle hook teeth and the reasons for such failure during field work. Spindle samples were obtained from a fixed position of the spindle bar under different field picking area conditions and combined with the spatial distribution characteristics of cotton bolls in Xinjiang. After cutting a spindle sample, a scanning electron microscope and an energy spectrum analyzer were used to characterize the micromorphology and element composition of the hook tooth surface and cross section under different working area conditions. The wear parameters of the hook teeth were then extracted. The results showed that the thickness of the coating on the surface of the hook tooth used in this study was between 66.1 µm and 74.4 µm. The major chemical element was chromium, with a small amount of nickel. During the field picking process, failure of the coating on the surface of the hook teeth initially appeared on the tooth tip and tooth edge, and then spread to the entire hook tooth surface. The wear failure of the hook teeth resulted from abrasive wear, oxidative wear, and fatigue peeling. As the picking area increased, the wear area of the hook teeth increased exponentially, while the wear width increased linearly. When the field picking area reached 533.33 ha, the maximum change rate of the wear area was 2.33 × 10³ µm²/ha, and the wear width was 1.84 µm/ha. During field work, the thickness of the coating decreased from the cutting surface to the tooth edge, and the wear rate gradually increased. The wear rate at Position 1 was the slowest, at 0.01 µm/ha, and the wear rate at Position 5 was the fastest, at 0.25 µm/ha.

As a key core component of cotton pickers, the spindle is prone to wear of the hook teeth. This research explores the wear characteristics of the spindle hook teeth of cotton pickers under different working areas. The sampled spindles were cut, the element composition and hardness of the spindle hook tooth coating and substrate were determined, the surface morphology of the spindle hook tooth was characterized, and its wear area and coating thickness were extracted. Results show that the main constituent elements of the coating and substrate are Cr and Fe, respectively, the hardness of the coating is about 1020 HV_0.1, and the hardness of the substrate is 470~840 HV_0.1. During the field operation, scratches appeared on the surface of the coating, the coating thickness layer gradually decreased, and the coating peeled off as the operating area increased. Afterward, scratches and oxidized particles appeared on the surface of the substrate, the wear rate accelerated, and the wear area gradually increased. The wear of the spindle hook teeth started to appear from the front and rear tooth tips up to the tooth edge, the back of the tooth, and the doffering edge, hence forming a long boot-shaped wear area.

Seed cotton compression molding solves the inconvenience of seed cotton transportation and storage after mechanical harvesting. Stress relaxation is closely related to the performance of the compressed seed cotton. In this study, an electronic universal testing machine with a homemade compression device was used to study the stress relaxation characteristics of machine-harvested seed cotton. The stress relaxation model of machine-harvested seed cotton was established, the influence of test factors on the response indexes was analyzed and, finally, stress relaxation characteristics of machine-harvested seed cotton were simulated. Results show that machine-harvested seed cotton stress relaxation characteristics can be described by the five-element Maxwell model. The equilibrium elastic modulus is negatively correlated with moisture content and cross-section dimensions, and the equilibrium elastic modulus is positively correlated with trash content and compression density. The rapid decay time and the residual stress ratio are negatively correlated with moisture content and compression density, but the influence of trash content and cross-section dimensions are limited. The stress relaxation process of machine-harvested seed cotton was simulated using virtual prototype technology, and the maximum error between the experimental and simulated values was obtained as 4.96%. The feasibility of the virtual prototype technique for the viscoelastic simulation of biomaterials was demonstrated.

This study aimed to improve the surface hardness and wear resistance of spindle hook teeth with special shapes to reduce the cost of replacing the spindle on cotton pickers. For this goal, a Ni-Co-β-SiC composite coating with different concentrations of β-SiC nanoparticles (0, 1, 2, 3, and 4 g/L) was electrodeposited on the surface of spindle hook teeth. The hardness, elemental composition, and micromorphology of the spindle hook teeth were characterized by microhardness tests, an energy spectrum analyzer, and a scanning electron microscope after cutting with the spindles. The actual wear process of the coating was determined by wear simulation and scratch wear tests, and the effect of the concentration of β-SiC nanoparticles on the properties of the coating was investigated. The results show that Ni-Co-β-SiC composite coating has a typical cellular structure. The hardness first increases and then decreases, and the wear resistance (including friction coefficient, scratch area, and shape of wear area) first decreases and then increases, mainly due to the pinning role and agglomeration of β-SiC nanoparticles. When the concentration of β-SiC was 1 g/L, the hardness reached a maximum of 506.2 HV0.1, the coefficient of friction reached a minimum of 0.13, and the wear area and wear micromorphology reached the most suitable values. Therefore, this Ni-Co-β-SiC composite coating had the best microhardness and wear resistance.