Optimal Parameter Combination Research Articles

Optimization of working parameters of high-pressure roller mill based on entropy weight method and response surface method.

In order to improve the crushing efficiency of high-pressure roller mill and reduce energy consumption, the optimal parameter combination of high-pressure roller mill is sought, GM160-140 high-pressure roller mill is taken as the research object, and the discrete element simulation calculation model of high-pressure roller mill is established to simulate the crushing process. Taking the roll surface pressure, productivity, and the proportion of discharge particle diameter as the performance evaluation indexes, the simulation data are used to analyze the influence of each working condition parameter of the high-pressure roller mill on its performance law. Through the entropy weighting method to determine the weights of high-pressure roller mill productivity, roll surface pressure, the proportion of discharge particle diameter, and through the extreme difference analysis to get the optimal parameter combination. Minitab software is used to carry out the response surface method (RSM) experimental design, the optimal parameter combinations are derived from the optimization using the response surface apparatus, the optimal parameter combinations derived from the two optimization methods are compared and analyzed, and ultimately it is concluded that the optimization of the response surface is the global optimal, and the optimal parameter combinations are as follows: the roller speed is 2.50rad/s, the roller gap is 28.87mm, and the feed size is 31.97mm, Under the above optimal parameters, the roll surface pressure is 150450.00N, the productivity is 1268.00t/h, the percentage of the material less than 6mm is 58.14%, so as to achieve the purpose of optimizing the parameters of the high-pressure roller mill, It is of significance to save resources and respond to the national green development and intelligent development.

Multi-objective optimization in EDM of functionally graded Fe-Al using grey relational analysis

Abstract Functionally Graded Materials (FGMs) are multipurpose materials with a specific goal of managing changes in structural, thermal, or functional qualities. They feature a spatial variation in microstructure and/or composition. The current work prepared three layers of sample with different chemical compositions of Fe-Al FGM (50 Al-50 Fe, 45 Al-55 Fe, and 40 Al-60 Fe) at. % produced by powder metallurgy, then studied the machining behavior of these samples. The literature on the machining behavior of FGM was reviewed, and the influence of electrical discharge machine (EDM) process parameters such as voltage (V), current (I), pulse-on time, and pulse-off time on performance characteristics (material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra)) was investigated. The optimal machining settings for Fe-Al FGM samples will be ascertained by use of Grey Relational Analysis (GRA), which is based on the Taguchi approach, after an experimental investigation of the L18 orthogonal array design of trials. The GRA findings verify that V 140 volts, Ip10 A, Ton 100 μs, and Toff 75 μs is the optimal combination of process parameters. It has been found that the voltage is more significantly affected than the rest of the input parameters to obtain greater material removal rate (MRR) and lower tool wear rate (EWR) and surface roughness (Ra) through the response table. According to the study, choosing the right process parameters can improve the multi-performance feature.

Experimental investigation on in-situ laser-assisted mechanical ruling of single crystal silicon

Abstract In this paper, response surface methodology (RSM) was employed as a robust and convenient predictive tool to establish the correlation between process parameters of in situ laser-assisted mechanical ruling and the ductile-to-brittle transition of single-crystal silicon. The interaction effects among three factors laser power, ruling speed, and negative rake angle on the ductile-to-brittle transition of single-crystal silicon were investigated. The optimal combination of process parameters was determined to be a laser power of 30W, a ruling speed of 0.25 mm s−1, and a negative rake angle of 58°. Utilizing a self-assembled setup and the optimal process parameters, multiple processing experiments were conducted. The average error between the experimental and predicted values was found to be 2.8%.

Experimental Study on the Pelleting and Coating Performance of Red Clover Seeds

This study aimed to optimize the pelleting and coating process for red clover seeds, addressing the issue of low pelleting success rates. Through theoretical analysis and experimental research, coating pan fill rate, powder supply quantity, and pelleting time were identified as key factors influencing the pelleting success rate. Single-factor experiments were conducted to investigate the effects of these parameters on the quality of red clover seed pelleting and coating. Based on these results, orthogonal trials were carried out, and response surface analysis was employed to reveal the influence patterns and interactions of each factor. The research results indicate that the factors affecting the pelleting success rate, ranked in order of importance, are coating pan fill rate, pelleting time, and powder supply quantity. Through mathematical model optimization, the optimal combination of process parameters was determined to be coating pan fill rate of 35.9%, powder supply quantity of 160.2 g, and pelleting time of 6.9 s. Under these conditions, a pelleting success rate of 94.3% was achieved in validation experiments. This study provides a theoretical foundation and practical guidance for optimizing the pelleting and coating process of red clover seeds, which is significant for improving seed coating quality and promoting red clover cultivation.

Prediction of Floor Failure Depth in Coal Mines: A Case Study of Xutuan Mine, China

Accurately predicting the failure depth of coal seam floors is crucial for preventing water damage, ensuring the safe and efficient mining of coal seams, and protecting the ecological environment of mining areas. In order to improve the prediction accuracy of the coal seam floor failure depth, an improved support vector regression (SVR) model is proposed to predict the floor failure depth by taking the 3234 working face in Xutuan Mine as an example. This improved model incorporates principal component analysis (PCA) and slime mould algorithm (SMA) optimization techniques. First, based on the measured data of seam floor failure depth in several mining areas, a prediction index system of floor failure depth was constructed. Subsequently, the PCA method was used to reduce the dimension of the measured data of the coal seam floor failure depth, and the input structure of the SVR model was optimized. Then, the SMA was used to optimize the key parameters, namely the penalty factor (C) and kernel function parameter (g), in the SVR model, achieving automatic parameter selection and obtaining the optimal parameter combination. This process led to the establishment of a coal seam floor failure depth prediction model based on PCA-SMA-SVR. The predictive performance of the PCA-SMA-SVR model, SMA-SVR model, and SVR model was quantitatively evaluated and compared using four quantitative indicators, and the results showed that the PCA-SMA-SVR model had the smallest MAE, RMSE, MRE, and TIC values, which were 1.0470 m, 1.2928 m, 0.0628, and 0.0374, respectively. Finally, the PCA-SMA-SVR model was used to predict that the floor failure depth of the 3234 working face in Xutun Mine was 17.09 m, and the predicted result was compared and analyzed with the results of four commonly used empirical formulas (16.03–21.74 m). The results show that the model is close to the results of four commonly used empirical formulas, indicating that the model has high predictive performance and good practicality. This study is of great significance for the safety, green mining, and ecological environment protection of coal mines.

Parameter Optimization Method for Centrifugal Feed Disc Discharging Based on Numerical Simulation and Response Surface

In this study, a centrifugal feeding disc device is proposed. To investigate the influence of the process parameters on the discharging efficiency and the lifting of the discharging efficiency, the centrifugal feeding disc device was dynamically simulated based on the discrete element method (DEM), and the simulation results were experimentally verified. Based on the quadratic regression orthogonal test method, a significant lossless regression model of process parameters and discharging efficiency was established, and the response surface of the interaction of process parameters was obtained. The results indicated that the order of influence of the process parameters on the discharging speed of the centrifugal feeding disc was as follows: outer turntable speed > inner turntable speed > inner turntable tilt angle > conical turntable angle. The interaction of the conical turntable angle and the inner turntable tilt angle had the greatest influence on the centrifugal feed disc discharge efficiency. The response surface method (RSM) was used to optimize the process parameters, and the optimal combination of process parameters included an outer turntable speed of 135 r/min, an inner turntable speed of 64 r/min, an inner turntable tilt angle of 7°, and a conical angle of 15°. The discharged efficiency of the optimized centrifugal feeding disc device was increased by 31.9%.

Photovoltaic Integrated Shading Devices in the Retrofitting of Existing Buildings on Chinese Campuses Within a Regional Context

Retrofitting existing buildings to be more energy-efficient is a tremendous contribution to the sustainability of society. The application of photovoltaic integrated shading devices (PVSDs) accords with this ambition by blocking out unwanted radiant heat gain and generating clean electricity. The deployment of PVSDs needs sensible design strategies to optimize the production of renewable energy while retaining the aesthetic quality of the built-up environment, especially in historic campuses. The concept was tested in a case study of buildings in South China University of Technology (SCUT) using Ladybug 1.4.0 and PVsyst 7.2, utilizing the existing “Xia’s shading” design method in historical environments and optimizing the design from the perspective of photovoltaic performance. Firstly, the photovoltaic (PV) panels were integrated as architectural components, and the parameters were incorporated into a mathematical equation based on “Xia’s shading” design method. This was followed by the assessment of the solar energy harvesting potential based on simulated annual solar irradiation values. Lastly, the PV panels’ solar irradiation potential under these different parameters was shown in figures to identify the optimum parameters combination for PVSD applications. The proposed methodology could evolve as a design tool and thus further assist in promoting the large-scale adoption of PVSDs in retrofit projects.

Optimization of Tensile Strength and Cost-Effectiveness of Polyethylene Terephthalate Glycol in Fused Deposition Modeling Using the Taguchi Method and Analysis of Variance

This paper investigates the optimization of tensile strength, tensile strength per unit weight, and tensile strength per unit time of polyethylene terephthalate glycol (PETG) material in fused deposition modeling (FDM) technology using the Taguchi method and analysis of variance (ANOVA). Unlike previous studies that typically focused on optimizing a single mechanical property, our research offers a multi-dimensional evaluation by simultaneously optimizing three critical quality characteristics: tensile strength, tensile strength per unit weight, and tensile strength per unit time. This comprehensive approach provides a broader perspective on both the mechanical performance and production efficiency, contributing new insights into the optimization of PETG in FDM. The Taguchi method (L16 45) was designed and executed, with the layer height, infill density, print temperature, print speed, and infill line direction as the control factors. Sixteen tensile tests were conducted, and ANOVA was employed to identify the main influencing factors for each quality characteristic. For the tensile strength, the infill density was found to have the greatest impact (48.45%), while the print temperature had the least impact (0.78%). The optimal parameter combination reduced the quality loss to 31.28% and standard deviation to 55.93%. For tensile strength per unit weight, the infill line direction had the greatest impact (87.22%), whereas the print temperature had the least impact (0.77%). The optimal parameter combination reduced the quality loss to 54.09% and standard deviation to 73.54%. Regarding the tensile strength per unit time, the layer height had the greatest impact (82.12%), while the print temperature had the least impact (0.08%). The optimal parameter combination reduced the quality loss to 10.81% and standard deviation to 32.87%.

Validation through internal flow physics of response surface methodology optimized mixed flow pump as turbine

In order to determine the optimal working efficiency of mixed flow pumps as turbine (MF-PAT) under a design condition of 10m3kw, this study takes the number of blades, blade wrap angle, impeller outer diameter, and impeller inlet width as design variables. Based on the center combination design method, experimental scheme design is carried out, and the head, shaft power, and efficiency of the turbine are used as evaluation indicators. A response surface model is constructed for optimization analysis, and the optimal geometric parameter combination of the impeller for MF-PAT is determined. For MF-PAT with forward-curved blade impeller in this paper, the optimal parameter combination is recommended as blade number Z = 6, blade wrap angle α = 47°, impeller outer diameter D2 = 140 mm and impeller inlet width b2 = 34 mm. The results show that compared with the original scheme, its efficiency has increased by 7.8 %. The established response surface model can reflect the relationship between evaluation indicators and design variables, and can be used for optimizing the geometric parameters of MF-PAT impellers. It can effectively enhance the blade's constraint ability on liquid flow, reduce hydraulic losses, and improve the performance of MF-PAT. Apply the ns-ds methodology for this and future mixed flow optimized pumps as turbines.

Detection of hidden drawings using multi-wavelength dynamic speckle, tuneable algorithms, and unsupervised learning

Abstract We implemented an experiment to reveal hidden drawings on papyrus, utilizing an optical technique based on the speckle phenomenon. The goal is to optimize the detection of hidden objects. Our approach proposes using multiple wavelengths for illumination and tuneable algorithms to process the dynamic speckle images. By implementing the suggested method, we generated various results with varying quality, contingent upon the tuneable algorithm parameters. It's feasible to identify the optimal parameter combination to achieve the most effective visualization of the recovered image. To streamline the selection of tuneable algorithms and mitigate reliance on subjective visual judgment, we employed unsupervised machine learning techniques to determine the conditions necessary to achieve optimal results. This approach simplifies the selection procedure and offers an objective and non-invasive method. Furthermore, the proposed procedure holds promise for extending its application to uncover hidden paintings, subsurface archaeological artefacts, and other dynamic speckle experiments.

Experimental Investigation of Tensile, Shear, and Compression Behavior of Additional Plate Damping Structures with Entangled Metallic Wire Material

To fulfill the vibration damping requirements of plate structures under complex conditions, additional damping structures with entangled metallic wire material (EMWM) are proposed based on the excellent physical properties of EMWM. A batch of specimens with different filament diameters, densities, and thicknesses are prepared. The stiffness and loss factors are taken as the evaluation indexes, and orthogonal tests are conducted to obtain the tensile, shear, and compression properties. The results show that the optimal parameter combinations can be obtained through orthogonal tests. For the specimens with optimal parameter combinations, the mechanical tests under different loading rates and loading displacements are carried out. With the increase in loading rate, the tensile and shear forces appear to display fracture failure in advance, and the compression performance is stable without significant changes. The change rule under each mechanical test is explored using the stiffness and loss factor evaluation index. It provides a reference for the analysis of the preparation parameters of subsequent additional damping structures with EMWM.

Improvement of the functional layouts of multifunctional soil-cultivating sowing units

BACKGROUND: With the technology evolution, the use of combined machines and units has become more in demand. From the numerous studies that were carried out during the operation of machines of this type, it can be seen that the cost of raw materials and financial costs have been significantly reduced, and the number of other advantages has been identified as well. AIM: Improvement of the functional layouts of multifunctional tillage sowing units for a more optimal combination of structural layouts and parameters of its use. METHODS: Engineers use the method of changing working bodies designed as layouts aiming to turn the machines into the modules capable of performing various types of operations based on working conditions. Thanks to this method, sets of individual working bodies and combinations are prepared for any kind of conditions (soil, climate, etc.). In addition to the advantages, this system has a number of disadvantages. One of these is that at the factory-built modules that are part of the original basis of the unit cannot be fully adapted to environmental conditions and economic factors of production. In this regard, combination of structural layouts and selection of the necessary parameters become problem causing. It is difficult to choose necessary parameters that are capable of ensuring minimal losses and adjusting the unit to the necessary operating mode. The objects of study are the devices and production processes, the main task of which is performing tillage and sowing operations. The practical value of this study presented in the form of: building the algorithm capable of structuring the analysis and synthesis of the structural layout of the tillage-sowing unit to find the working links for various types of unit functions; finding the methods and improved combinations of working bodies, comparison of similar mechanical analogues with the original properties of systems. RESULTS: When studying the studies of Wilde, who gave a description of the combined units, as well as the studies of Runchev, who determined the generalization of the combined machines, the relevant typology of agricultural machinery was developed. In this typology, one of the most important characteristics is multifunctionality, which has developed from the need for the versatility of units and the compilation of combinations of several units. The developed technology contains a number of characteristics that are fundamental for the choice of agricultural machinery. CONCLUSION: As a result of the study, it was determined at which velocity of the unit the system is balanced.

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman's rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. The proposed AsterRF model achieved a validation accuracy of 92.2<!PCT!>, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

Design and Test of a Traction Side-Pull Square Straw Bale Pick-Up-and-Stack Truck

To address the issues of limited one-time loading capacity, single functionality, and low automation level in existing square straw bale pickers, a large automated square straw bale pick-up-and-stack truck that integrates picking, stacking, transporting, and bundling functions has been developed, combining the technical advantages of one-time field removal and storage of bales. We innovatively designed a side-pulling traction mechanism that can realize the rapid transition between the transporting state and working state of the machine; a picking device that can complete the continuous action of forking, lifting, turning positioning, and de-forking; and a bundling device that can realize the adjustment of the attitude of square straw bales. Response surface tests were conducted on the prototype to determine the key structural and operational parameters, using the bundle completion rate and regular bale rate as evaluation indicators. Regression and significance tests were performed on the machine’s forward speed, chassis frame offset, and the ground clearance of the fork tine to determine the influence and priority of these factors on the evaluation indicators. Through multi-objective function optimization of the regression model, the optimal parameter combination was found to be a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, resulting in a bundle completion rate of 98.85% and a regular bale rate of 96.96%. Subsequent field tests with the optimized parameters showed that at a machine forward speed of 15.5 km/h, a chassis frame offset of 2126 mm, and a fork tine ground clearance of 225 mm, the bundle completion rate was 98.37% and the regular bale rate was 95.83%, meeting the relevant design requirements. This study can provide a reference for the design and development of straw collection and storage machinery.

Exploring multi-deformation mechanism and control of arc thin-walled structures during supercritical CO2 assisted micro milling

Titanium alloys are extensively utilized due to their exceptional corrosion resistance, high specific strength, temperature resilience, and biocompatibility. However, the challenges such as poor thermal conductivity, pronounced micro-machining size effects, and the sensitivity of micro-thin wall structures to residual stress complicate the machining of titanium alloy micro-thin walls. This paper investigates the use of supercritical CO2 to assist in arc micro-thin wall milling experiments of titanium alloys, aiming to elucidate the influence of various process parameters on micro-milling performance. The mesoscale prediction model developed in this study shows that the time-varying and static deflection deformations of micro-thin walls cooled by supercritical CO2 are approximately half of those observed under dry cutting conditions. To compare and optimize micro-milling performance metrics, an RVEA-entropy weight TOPSIS optimization scheme was developed, and combined with several high-dimensional multi-objective optimization algorithms. Integrating this with micro-milling finite element model, an iterative optimization and reverification strategy was proposed. The optimized parameters combination achieved through this method reduced micro-milling force, top deformation, and side deformation by 32.5 %, 24.6 %, and 24.3 %, respectively. The research approach and optimization strategy presented in this paper offer valuable insights for enhancing the machining precision of mesoscale titanium alloy micro-thin-wall structures.

Optimization of hydrogel extrusion printing process parameters based on numerical simulation

The printing quality of biological scaffold is not only affected by the fluidity of bio-ink but also by the printing process parameters, such as the size of the needle, printing height, extrusion speed, and printing speed. Therefore, optimizing the printing process parameters can further improve the molding quality of the biological scaffold. In this study, the printing and deposition process of sodium alginate hydrogel was modeled and analyzed based on the Herschel–Bulkley model by the finite element simulation method. The orthogonal experiment method, control variable method, and response surface method were used to design experiments, and the influences of different printing process parameters on the hydrogel deposition process were investigated. Finally, the optimal combination of printing process parameters was obtained by taking the molding degree and offset of the hydrogel line as optimization objectives. The results show that the strength relationship of the factors affecting the molding degree of the hydrogel line is as follows: printing height > needle diameter > printing speed > extrusion speed, and the strength relationship of the factors affecting the printing offset is as follows: printing height > needle diameter > extrusion speed > printing speed. The optimal combination of printing process parameters is d = 0.34 mm, H = 0.51 mm, v1 = 10 mm/s, and v2 = 7.91 mm/s. Compared with the printing experiment results of the hydrogel line molding degree under the optimal process parameters, the error range is within −11.55%–1.27%, which further demonstrates the reliability of the optimization method of hydrogel extrusion printing process parameters based on numerical simulation and response surface method.

DFT-based accurate and efficient bandgap prediction of CsSnI3-xBrx and parameter optimization for enhanced perovskite solar cell performance

This article analyzes the bandgap (Eg) of tin-based metal halide perovskites CsSnI3-xBrx (x = 0, 1, 2, 3) using density functional theory (DFT). For the first time, the study evaluates various exchange-correlation (XC) families—LDA, GGA, MGGA, and HSE—along with key DFT parameters such as pseudopotentials (PP), K-point densities, and basis sets. The investigation identifies the optimal combination of parameters that most accurately matches experimental Eg values for different mole fractions. The study reports average deviations from experimental Eg values of 6.48% for LDA, 8.08% for GGA, 3.44% for MGGA, and 3.07% for HSE. While HSE shows the smallest deviation, MGGA demonstrates better consistency across mole fractions with lower computational costs. Additionally, the band structure and Eg trends for each mole fraction are examined, providing valuable insights for future computational studies on perovskite materials.

Optimal design of a high-performance heat exchanger for modular thermoelectric generator towards low-grade thermal energy recovery

Thermoelectric generator (TEG) has been identified as a promising method for low-grade thermal energy recovery owing to their lack of moving parts, scalability, and compatibility with other devices generating waste heat. Heat exchanger, as one of the most important components of the modular TEG, plays a crucial role in improving the overall performance of the TEG. Nevertheless, flow maldistribution inside the heat exchanger results in uneven surface temperature field of the heat exchanger, which will ultimately limit the output capacity of the TEG. Achieving a homogeneous flow distribution within the heat exchanger while minimizing flow resistance is essential. To address this, optimization of a plate-shaped heat exchanger for the modular TEG is conducted using CFD analysis and the Taguchi method to identify the optimal combination of parameters. The optimized heat exchanger demonstrates a flow maldistribution intensity (ζ) of only 4.75 % and a low flow resistance of 1.16 kPa. Furthermore, a unit of the modular TEG is constructed using two optimized heat exchangers and commercial thermoelectric modules (TEMs), and its performance is analyzed via an analytical model. The results indicate that the power per module, net power density, and conversion efficiency reached 1.2 W, 51.4 kW/m3, and 1.92 %, respectively, at a temperature difference of 70 °C. These findings suggest that the optimized heat exchanger could provide high output performance compared with other literature, offering significant potential for low-grade heat energy recovery.

Optimisation of parameters of a dual-axis soil remediation device based on response surface methodology and machine learning algorithm

To accelerate the recycling of black soil, it is necessary to develop a new type of soil remediation equipment to improve its working efficiency. The one-way test was used to determine the mean level value of the steepest climb test, and the combined equilibrium method was used to determine the upper and lower interval levels of the response surface test for parameter optimisation. Based on the results of the response surface indices, machine learning was performed and the optimal model was determined. The results show that the predictive ability and stability of the decision tree model for the two indicators are better than that of random forest and support vector machine. The optimal parameter combinations determined using the decision tree model are: speed 73 rpm, homogenisation pitch 183 mm, homogenisation time 1 s, descent speed 0.06 m/s. The error between the optimal value of the machine learning prediction model and the actual simulation is 1.1% and 5.72%, respectively. The results of the study show that the effect of optimising the parameters through machine learning achieves a satisfactory prediction accuracy.

Study on the Process of Soil Clod Removal and Potato Damage in the Front Harvesting Device of Potato Combine Harvester

To improve soil clod removal and reduce potato damage in potato combine harvesters, this study investigates the processes involved in soil clod removal and potato collisions within the bar-lift chain separation device of the harvester. It outlines the structure and working principles of the machine, theoretically analyzes the key dimensions of the digging device and potato–soil separation components, and derives specific structural parameters. A dynamic mathematical model of the bar-lift chain is established, from which the dynamic equations are formulated. The analysis identifies factors that influence the dynamic characteristics of the bar-lift chain. This study examines the working principles and separation performance of the potato–soil separation device, with a focus on the collision characteristics between potatoes and both the screen surface and the bars. Key factors such as the separation screen’s line speed, the harvester’s forward speed, and the tilt angle of the separation screen are considered. Simulations are performed using a coupling method based on the Discrete Element Method (DEM) and Multi-Body Dynamics (MBD). Through simulation experiments, the optimal parameter combinations for the potato–soil separation device are determined. The optimal working parameters are identified as a separation screen line speed of 1.25 m/s, a forward speed of 0.83 m/s, and a tilt angle of 25°. Field harvesting experiments indicate a potato loss rate of 1.8%, a damage rate of 1.2%, an impurity rate of 1.9%, a skin breakage rate of 2.1%, and a yield of 0.15–0.21 ha/h. All results meet national and industry standards. The findings of this research provide valuable theoretical references for simulating potato–soil separation in combine harvesters and optimizing the parameters of these devices. Future potential research will consider the automatic regulation of the excavation volume of the potato–soil mixture, aiming to achieve intelligent control of the potato–soil separation operation.