In an analysis of the penetration resistance and tillage depth of post-tillage soil, four surface-layer discrimination methods, specifically, three machine learning algorithms—Kmeans, DBSCAN, and GMM—and a curve-fitting method, were used to analyze data collected from the cultivated and uncultivated layers. Among them, the three machine learning algorithms found the boundary between the tilled and untilled layers by analyzing which data points belonged to which layer to determine the depth of the soil in the tilled layer. The curve-fitting method interpreted the intersection among data from the fitted curves of the ploughed layer and the un-ploughed layer as the tillage depth. The three machine learning algorithms were used to process a standard data set for model evaluation. DBSCAN’s discrimination accuracy of this data set reached 0.9890 and its F1 score reached 0.9934, which were superior to those of the other two algorithms. Under standard experimental conditions, the ability of DBSCAN clustering to determine the soil depth was the best among the four discrimination methods, and the discrimination accuracy reached 90.63% when the error was 15 mm. During field-test verification, the discriminative effect of DBSCAN clustering was still the best among the four methods. However, the soil blocks encountered in the field test affected the test data, resulting in large errors in the processing results. Therefore, the combined RANSCA robust regression and DBSCAN clustering algorithm, which can eliminate interference from soil blocks in the cultivated layer and can solve the problem of large depth errors caused by soil blocks in the field, was used to process the data. After testing, when the RANSCA and DBSCAN combined method was used to process all samples in the field and the error was less than 20mm, the accuracy rate reached 82.69%. This combined method improves the applicability of discrimination methods and provides a new method of determining soil depth.

The cleaning unit is a key functional component of grain combine harvesters, yet its operating parameters are still predominantly adjusted according to operator experience, resulting in limited adaptability to fluctuating working conditions. To enhance the intelligence and stability of the cleaning process, this study develops a fuzzy control approach supported by data-driven performance modeling. Based on multi-condition bench experiments, feeding rate, fan speed, cleaning sieve vibration frequency, and sieve opening were selected as input variables. Gaussian Process Regression (GPR) models were established to describe the nonlinear relationships between operating parameters and cleaning loss rate and impurity rate, and impurity rate was inferred online to compensate for the absence of a reliable sensor. Taking feeding rate variation as the primary disturbance, a dual-input fuzzy control strategy was designed using loss rate monitoring and model-predicted impurity rate as feedback signals. Simulation and bench test results show that, under small and moderate load disturbances (±20% and ±35%), the proposed method reduces either impurity rate or cleaning loss rate through coordinated parameter adjustment. Under large disturbances (±50%), performance deterioration cannot be fully eliminated, but its extent is alleviated compared with open-loop conditions.

To determine the optimal operating parameters of the single-longitudinal-axial-flow threshing device for wheat harvesters under high feed rate conditions and lay a theoretical and methodological foundation for the subsequent development of a specialist system for threshing device parameter optimization, this study constructed a discrete element model (DEM) of wheat plants at harvest stage in EDEM software based on the Hertz–Mindlin with bonding contact model, combined with the discrete element parameters of wheat plants calibrated by a texturometer. Simulation experiments were designed and conducted to optimize the operating parameters of the threshing device, and a methodology that employs simulation experiments to obtain optimal parameter combinations under different operating environments was proposed, which was applied to the development of a specialist system for threshing device parameter optimization. Taking cylinder speed, cylinder inclination and threshing gap as experimental factors, and unthreshed rate, separation loss rate and breakage rate as evaluation indexes, single-factor and Box–Behnken simulation experiments were carried out. Mathematical models were established to reveal the correlation between evaluation indexes and influencing parameters, and then the optimal parameter combination was solved. The results showed that the threshing device achieved the optimal comprehensive operating performance at a cylinder speed of 850 r/min, a cylinder inclination of 6° and a threshing gap of 20 mm, with an unthreshed rate of 1.575%, a separation loss rate of 0.158% and a breakage rate of 0.509%. Bench validation tests indicated that the relative errors between simulated and measured indexes were all less than 5%, verifying the reliability of the established DEM. Compared with bench tests, the simulation experiment method in this study ensured high accuracy while significantly reducing the time and labor costs of tests. The obtained optimal operating parameters and the proposed simulation optimization methodology provide core data support and technical reference for the development of a specialist system for parameter optimization of the threshing device of wheat harvesters.

This study investigates the tribological behavior of the attachment force between bio-inspired claw–spines and rough surfaces and conducts theoretical and model-based analyses of the corresponding attachment mechanism. Based on critical interfacial tribological theory, a frictional mechanics model for a single claw–spine interacting with an arbitrary rough surface was developed. Furthermore, a stiffness-matrix-based mechanical model of bio-inspired claw–spine attachment to arbitrary surfaces was established, together with a contact interaction model between the claw–spine and the contact surface. The force distribution during the contact and attachment of the claw–spine to arbitrary surfaces was analyzed in detail. Criteria for determining stable claw–spine attachment were formulated, and the safe range of frame displacement variation under the corresponding conditions was identified. Based on these analytical results, a test platform for the bio-inspired claw–spine attachment structure was designed. In the experiments, the developed attachment-force measurement system was used to measure and analyze the frictional attachment forces generated by the claw–spine foot on different rough surfaces. After the claw–spine entered the stable attachment stage, the maximum claw–spine attachment forces measured on 60-grit, 80-grit, and 120-grit sandpaper surfaces were 0.62 N, 0.54 N, and 0.61 N, respectively. The corresponding maximum attachment forces measured on horizontal and vertical brick surfaces were 0.92 and 0.99 N, respectively. The results provide a basis for the mechanical analysis and force sensing of bio-inspired claw–spine attachment states and establish theoretical and experimental foundations for subsequent research on attachment-state recognition and motion control based on attachment-force feedback.

To solve the problems of high kernel breakage and low threshing efficiency in the threshing operation of high-moisture maize, this study designs an adaptive threshing device based on the coupled working principle of vortex airflow driving and flexible collision. The adaptive threshing mode enables maize ears to move spirally upward under vortex airflow and make compliant contact with flexible components. By adopting repeated mild collisions instead of the rigid violent impact used in traditional devices, low-damage maize threshing is achieved. Preliminary experiments verify that the layout density of flexible threshing units, tangential airflow velocity, and feeding speed are the key factors affecting threshing performance. A regression orthogonal rotational combination test is conducted to systematically explore the single-factor and interactive effects on threshing efficiency, and the optimal parameter configuration is obtained. The test results show that, under the conditions of circumferential angular spacing of 21.5°, tangential velocity of 45.9 m/s and feed rate of 0.65 kg/s, the maize threshing rate reaches 96.1% while the grain breakage rate is controlled below 0.1%, which is significantly superior to conventional rigid threshing methods. This research provides a new technical scheme and experimental data reference for the low-damage threshing study of high-moisture maize.

At present, the measurement of tillage depth is mainly based on manual measurement, but the manual raking method results in low measurement accuracy and high labor intensity. Due to the complexity of soil, theoretical research on tillage depth is relatively scarce. In order to provide a new research direction and research idea for soil stratification, topsoil was taken as the research object of this paper. The correlation between penetration resistance and penetration depth of a probe in a cultivated soil was studied, and a mathematical model was established. There is a certain similarity between the process of spherical cavity expansion and the process of probe penetration, so we introduced the theory of spherical cavity expansion into the modeling of penetration resistance of the cultivated soil. In this paper, the spherical cavity expansion theory of unsaturated soil was used as the basis for solving the penetration resistance. And the unified strength criterion was employed as a yield condition of the soil to set a stress solution and a displacement solutionin into of the probe penetrating into the elasto-plastic zone of the cultivated soil to determine the model of expansion force. We have carried out indoor tests to revise the expansion force model. Firstly, according to the range of soil density and water content in the field, the soil densities were classified into 1.1×10³kg/m³, 1.2×10³kg/m³ and 1.3×10³kg/m³, and the water contents were divided into 10%, 15% and 20%. In addition, the orthogonal tests were performed at different levels. The soil was put into the barrel, and the probe was inserted into the soil in the barrel at the speed of 8mm/s to determine the test values of the change of the probe penetration resistance with depth. Finally, the expansion force model was fitted with the results of the indoor test, and coefficient B was introduced to express the influence degree of density and water content on the resistance. Coefficient B was substituted into the expansion force model to obtain the penetration resistance model of the cultivated soil. Through the goodness of fit analysis of the penetration resistance model, the results show that the overall average goodness of fit of the penetration resistance modelat was up to 0.871 at different water contents and densities, which was a good fit and could present novel insights into the study relating to soil stratification theory.