Optimal Parameter Combination Research Articles

Simulation and experimental study of flexible adsorption and localization mechanism for vegetable grafting machines

Because of the lack of good seedling positioning during vegetable grafting, there are issues such as high labor costs and long grafting time. This article proposes a negative pressure suction seedling positioning method for seed leaves based on the characteristic parameters of cucumber spike wood, and designs a flexible adsorption positioning mechanism for spike wood. Firstly, the ventral surface curve trajectories of cucumber cotyledons were extracted using Matlab software, and then a shape-adaptive design was applied to the attachment surface of the flexible suction positioning mechanism, and a computational fluid dynamics model of the airflow field was established. By combining Fluent simulation analysis with orthogonal experiments, the effect of suction hole diameter, vacuum negative pressure value, suction hole quantity, and suction hole depth on the adsorption effect of the suction head was analyzed, the main and secondary factors and operational indicators that affect the adsorption effect are evaluated. The optimal parameter combination: suction hole diameter of 1.5 mm, vacuum negative pressure value of 2 kPa, suction hole quantity of 42, and suction hole depth of 2 mm, has been found. A verification experiment was conducted on a test bench, and the experimental results show that the success rate of leaf absorption using the optimal parameter combination is 97.69%, which indicates that the suction head is designed reasonably and meets the requirements of grafting.

Performance of bio-nest by modified basalt fiber carriers on the enhanced remediation of low-carbon and -nitrogen urban black-smelling water

ABSTRACT Biological contact oxidation reactors employing modified basalt fiber (MBF) were constructed to systematically investigate the impact of various hydraulic retention times (HRTs) and aeration durations on nitrogen and phosphorus removal in low carbon and polluted river water. The experimental findings underscored that configuring the HRT to 36 h and maintaining an aeration ratio of 1:2 yielded the most favorable outcomes for the removal of COD, NH4+ -N, total nitrogen (TN), and TP from synthetic low carbon, source-polluted river water. Detailed microbial sequencing elucidated the predominant bacterial phylum within the MBF reactor, identified as Proteobacteria. The dominant genera encompassed Pseudomonas, Aeromonas, and SM1A02. This microbial composition, marked by a high abundance of denitrifying genera, corroborated the robust denitrification capacity exhibited by the MBF reactors. The orchestrated combination of optimal operational parameters and the prevalence of key microbial taxa substantiate the efficiency of MBF reactors in effectively mitigating nitrogen and phosphorus in low carbon source river water.

Multiresponse optimisation and process capability analysis of chemical vapour jet machining for the acrylonitrile butadiene styrene polymer: Unveiling the morphology

Abstract The implementation of three-dimensional (3D) printing technology has culminated in a notable rise in productivity and operational effectiveness for manufacturers. Additive manufacturing (AM) is a manufacturing technology that implies an alteration from the conventional approach of material removal. The fundamental idea underlying the AM technique is the gradual buildup of layers (layer-on-layer accumulation). In conventional approaches, every component can have detrimental implications due to the direct interaction between the tool and the workpiece, leading to the loss of heat through friction. The utilisation of 3D printing as a way to surpass conventional processing methods signifies a novel development in several sectors. This method involves the utilisation of unconventional techniques for the fabrication of components. The primary objective of this research is to investigate the chemical vapour jet drilling technique specifically applied to acrylonitrile butadiene styrene (ABS) materials. The intent is to enhance the surface characteristics, or surface finish (SF), and the dimensional accuracy (DA) of ABS workpieces. An evaluation regarding the reliability, repeatability, as well as preciseness of the vapour jet drilling (VJD) process is conducted via the utilisation of experiment and data analysis. The study employed a Taguchi L9 design of experiments to carry out a series of tests aimed at analysing the implications of three independent variables: pressure, flow rate, and standoff distance. The researchers employed a multiresponse optimisation approach to attain an optimal combination of parameters that resulted in a superior SF with DA. Consequently, the overall appeal of the outcome was reached. The process’s capabilities and dependability were assessed by conducting tests on the substrates at their optimal settings. Surface roughness and circularity were measured at numerous locations on the substrates. The study determined that the process capability indices (C p and C pk) had values over 1.33 for each of the response parameters, with C pk values also exceeding 1. The analysis of histograms and capability indices demonstrates that the VJD method, when conducted under optimised conditions, may be categorised as statistically controlled for the processing of ABS materials.

小麦制粉粒度检测装置供料部件设计与研究

To address the issues of poor timeliness and delayed feedback in traditional wheat milling processes for manual particle size detection, a wheat milling online particle size detection device has been designed. This paper focuses on the design optimization of the key feeding component in the device, which affects the accuracy of particle size detection. The feeding component adopts shaftless screw blades. Through theoretical analysis, the main parameter ranges affecting the throughput capacity of the shaftless screw conveyor are determined. A Box-Behnken experiment is designed to obtain the optimal parameter combination for each factor: outer diameter of screw blades 23.8 mm, inner diameter of screw blades 6.4 mm, pitch 11.2 mm, and blade rotation speed 288.9 r/min. Simulation and test stand experiments are conducted using the above parameter combination. The simulation results show that the average throughput capacity of the feeding component is 2.85 kg/h, while the average throughput capacity of the test stand experiment is 2.84 kg/h, with a coefficient of variation of uniformity of 1.33%. These results indicate that the above parameter combination meets the design requirements for the feeding component in the device.

Physical and mechanical properties of ceramics based on ZrN-ZrO2 obtained by spark plasma sintering method

Relevance. Increasing the service life of mining tools is an important task in the development of geology associated with the investigation of new ceramic materials for functional purposes. The ability to reduce wear and thermal and chemical effects of rocks on the working elements of mining equipment determines the vector of development of the use of super-hard, high-strength and refractory ceramics. At the same time, the task is set to increase the fracture toughness of the materials used to solve the problem of maintaining the operability of equipment under conditions of critical deformations. To accomplish this task, it is necessary to study the patterns of consolidation of ceramic materials and search for the optimal combination of consolidation parameters to achieve improved physical and mechanical properties. Aim. To develop a method for producing high-density durable ceramics based on commercially available zirconium nitride powders using spark plasma sintering under vacuum conditions, to study the phase composition and physical and mechanical properties of the resulting samples. Methods. X-ray phase analysis of the studied samples, nanoindentation, microscopic analysis. Results and conclusion. The authors have studied the physical and mechanical properties of zirconium nitride consolidated by spark plasma sintering at 2000℃, a pressure of 30–60 MPa and a holding time at a given temperature of 5–10 minutes. Qualitative and quantitative X-ray phase analysis was carried out, within which the content of the main phase of zirconium nitride and zirconium dioxide phase was determined. It was found that an increase in the pressure applied during consolidation and holding time contributes to better compaction and a decrease in the porosity of the samples from 8.52 to 2.72%. It was found that with a decrease in porosity, the elastic modulus increased in the range from 320 to 378 GPa, and the hardness from 7.3 to 10.4 GPa. At the same time, by extrapolating the data, it was established that non-porous zirconium nitride samples will have an elastic modulus of 394 GPa and a Martens hardness of 11.56 GPa. For the samples under study, critical stress intensity factors were determined. It was established that at a porosity of less than 5%, the critical stress intensity factor of zirconium nitride consolidated by spark plasma sintering has values of at least 4 MPa m1/2.

Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved Bilstm

In this paper, a novel neural network based on parameter optimization Variational Mode Decomposition (VMD) and improved bidirectional long short-term memory (BiLSTM) is proposed. Wear state recognition method of ball screw element action unit component based on Bilstm. Firstly, Tent chaotic map and adaptive sine cosine Algorithm were used to improve the improved Northern Goshawk Optimisation Algorithm (INGO) to verify the superiority of INGO algorithm and determine the optimal parameter combination of VMD. Secondly, INGO-VMD was used to decompose the collected vibration signals and calculate the correlation of IMF components, and the multi-feature information matrix that could characterize the wear state change of the lead screw was constructed after retaining the IMF components with large correlation. Finally, the divided feature information matrix and labels were input into the Bilstm network model of Bayesian optimization (BO) for training, and the Softmax classifier was used to classify and identify the wear state category.

Multi-Objective Optimization of Surface Integrity in the Grind-Hardening Process

Grind-hardening machining is a new integrated manufacturing technology that integrates the theory of material surface quenching and grinding machining. The surface integrity of grind-hardening directly affects the performance and reliability of the parts. Improving the grind-hardening quality has always been the focus and difficulty in this field. Based on the surface integrity theory and the characteristics of the grind-hardening process, this paper proposed four optimization criteria for grinding parameters according to the engineering application requirements of materials. Using the expectation function, the burr cross-sectional area, depth of the effective hardened layer, and surface roughness were comprehensively analyzed under each optimization criterion to obtain an optimal combination of grinding parameters. The results revealed a significant inconsistency in the optimized grinding parameters under each optimization criterion. When considering the depth of the effective hardened layer as the primary optimization parameter and ignoring the surface roughness and burr cross-sectional area, the highest overall desirability was 0.926395. In practical application, the optimization criteria should be reasonably selected according to the actual engineering requirements.

An Experimental Study in Laser-Assisted Machining of AerMet100 Steel.

To solve the problems of poor surface quality and low tool life in conventional machining (CM) of AerMet100 steel, an experimental study was conducted in laser-assisted machining (LAM) of AerMet100 steel. The effects of laser power, cutting speed, feed rate, and depth of cut on the surface roughness of AerMet100 steel were studied based on a single-factor experiment. The degree of influence of each factor on the surface roughness was evaluated by analyses of variance and range in the orthogonal experiment, and the combination of process parameters for the optimal surface roughness was obtained. The order of influence was as follows: laser power > cutting speed > depth of cut > feed rate; the optimal combination of process parameters was laser power 200 W, cutting speed 56.5 m/min, feed rate 0.018 mm/rev, and depth of cut 0.3 mm. Compared to CM, the surface morphology of the workpiece under the optimization of LAM was relatively smooth and flat, the surface roughness Ra was 0.402 μm, which was reduced by 62.11%, the flank wear was reduced from 208.69 μm to 52.17 μm, there were no tipping or notches, and the tool life was significantly improved. The study shows that the LAM of AerMet100 steel has obvious advantages in improving surface quality and reducing tool wear.

The Influence of the Distribution Law and Uniformity of a Threshed Mixture with the Working Parameters of a Soybean Threshing Device

Soybean plants cultivated using mulched drip irrigation planting technology have the following characteristics during the harvest period: green stems and leaves, and a high straw/grain ratio. Moreover, the threshing device of a soybean combine harvester is difficult to adapt to, resulting in an increase in the accumulation and unevenness of the threshed mixture. This leads to an increase in impurity content and the loss rate. We conducted a single-factor experiment on a self-developed longitudinal/axial-flow soybean threshing and separation test bench, employing drum speed, feeding rate, and threshing clearance as experimental factors. The influence of the soybean threshing and separation device’s working parameters on the distribution and uniformity of the threshed mixture in the axial and radial directions of the drum was explored through experiments. The results showed that the mass of the threshed mixture and soybean seeds showed a trend of first rapidly increasing and then slowly decreasing in the axial direction of the drum. Additionally, the mass showed a distribution feature of large values on both sides and small values in the middle in the radial direction. A lower drum speed, greater threshing clearance, and a smaller feeding rate make the radial distribution of a threshed mixture more uniform. Based on the combination of the crushing rate and unthreshed rate, the optimal working parameter combination was determined to be as follows: a drum speed of 500 r/min, a feeding rate of 6 kg/s, and a threshing clearance of 25 mm. The findings of this research offer valuable insights for the structural optimization and design enhancement of threshing and cleaning mechanisms within soybean combine harvesters.

Study on Electrical and Mechanical Properties of Double-End Supported Elastic Substrate Prepared by Wet Etching Process.

Preparing elastic substrates as a carrier for dual-end supported nickel chromium thin film strain sensors is crucial. Wet etching is a vital microfabrication process widely used in producing microelectronic components for various applications. This article combines lithography and wet etching methods to microprocess the external dimensions and rectangular grooves of 304 stainless steel substrates. The single-factor variable method was used to explore the influence mechanism of FeCl3, HCl, HNO3, and temperature on the etching rate, etching factor, and etching surface roughness. The optimal etching parameter combination was summarized: an FeCl3 concentration of 350 g/L, HCl concentration of 150 mL/L, HNO3 concentration of 100 mL/L, and temperature of 40 °C. In addition, by comparing the surface morphology, microstructure, and chemical and mechanical properties of a 304 stainless steel substrate before and after etching treatment, it can be seen that the height difference of the substrate surface before and after etching is between 160 μm and -70 μm, which is basically consistent with the initial design of 0.2 mm. The results of an XPS analysis and Raman spectroscopy analysis both indicate that the surface C content increases after etching, and the corrosion resistance of the surface after etching decreases. The nano-hardness after etching increased by 26.4% compared to before, and the ζ value decreased by 7%. The combined XPS and Raman results indicate that the changes in surface mechanical properties of 304 stainless steel substrates after etching are mainly caused by the formation of micro-nanostructures, grain boundary density, and dislocations after wet etching. Compared with the initial rectangular substrate, the strain of the I-shaped substrate after wet etching increased by 3.5-4 times. The results of this study provide the preliminary process parameters for the wet etching of a 304 stainless steel substrate of a strain measuring force sensor and have certain guiding significance for the realization of simple steps and low cost of 304 stainless steel substrate micro-nano-processing.

Simulation and verification of discrete element parameter calibration of pulverized coal particles

ABSTRACT To enhance the simulation efficiency of the EDEM discrete element numerical simulation software for coal powder mineral particles, experimental data on the rest angle of coal powder particles were collected as the response variable. Five parameters related to coal powder particles within the Hertz-Mindlin no-slip contact model were selected for investigation. Initially, the Plackett-Burman experiment identified three parameters: collision restitution coefficient, static friction coefficient, and rolling friction coefficient, significantly influencing the resting angle. Subsequently, the steepest ascent experiment determined the significant parameter ranges as follows: the collision restitution coefficient ranged from 0.20 to 0.40, the particle-particle static friction coefficient ranged from 0.30 to 0.50, and the particle-particle dynamic friction coefficient ranged from 0.06 to 0.10. Finally, the second-order regression model for the angle of repose and the saliency parameters was developed and optimized using the Box-Behnken experimental design. The following parameter combination was obtained: the collision recovery coefficient was 0.30, the particle-particle static friction coefficient was 0.49, and the particle-particle dynamic friction coefficient was 0.09. This study aimed to compare the angle of repose between the simulation experiment and the actual experiment. The relative error was only 1.02%, indicating that the simulation experiment achieved the optimal parameter combination.

A COMPARATIVE MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN THE END MILLING OF Al 3003 SUBJECTED TO NON-EQUAL CHANNEL ANGULAR PRESSING (NECAP)

This paper highlights the surface roughness optimization of a specific material, Al 3003, which has been subjected to the non-equal channel angular pressing (NECAP) process. Considering spindle speed, feed rate, and depth of cut as input variables and surface roughness as an output variable, experiments have been conducted based on the L27 orthogonal array of the Taguchi method. Four prediction models, namely exponential and response surface methodology (RSM) as mathematical models, and artificial neural networks (ANNs) prediction models with different training algorithms (Bayesian Regularization (BR) and Levenberg–Marquardt (LM)), are proposed. Applying effectiveness and performance criteria, the prediction accuracy of the exponential model (90.35%), RSM (93.07%), BR (97.83%), and LM (97.54%) shows that all proposed prediction models are efficient enough. The ANN model trained with BR is found to be the best fit for predicting surface roughness. In order to optimize surface roughness, a newly introduced optimization method called the Intelligible-in-time Logics Algorithm (ILA) is employed. High spindle speed (1000[Formula: see text]rev/min), low feed rate (100[Formula: see text]mm/min) and depth of cut (0.5[Formula: see text]mm) have been the optimum cutting parameter combinations to obtain minimum surface roughness (0.4956[Formula: see text][Formula: see text]m). The results have been verified by confirmation tests and Particle Swarm Optimization (PSO) method. ILA and PSO predict the same optimum parameter combinations and minimum surface roughness, while ILA performs optimization in less time (114.4[Formula: see text]s), about 3.5 times faster than PSO. The paper’s findings strongly advocate the application of ILA in machining data optimization.

Empirical Study on Real Estate Mass Appraisal Based on Dynamic Neural Networks

Real estate mass appraisal is increasingly gaining popularity as a critical issue, reflecting its growing importance and widespread adoption in economic spheres. And data-driven machine learning methods have made new contributions to enhancing the accuracy and intelligence level of mass appraisal. This study employs python web scraping technology to collect raw data on second-hand house transactions spanning from January 2015 to June 2023 in China. Through a series of data processing procedures, including feature indicator acquisition, the removal of irrelevant sample cases, feature indicator quantification, the handling of missing and outlier values, and normalization, a dataset suitable for direct use by mass appraisal models is constructed. A dynamic neural network model composed of three cascaded sub-models is designed, and the optimal parameter combination for model training is identified using grid searching. The appraisal results demonstrate the reliability of the dynamic neural network model proposed in this study, which is applicable to real estate mass appraisal. A comparison with the common methods indicates that the proposed model exhibits a superior performance in real estate mass appraisal.

Determination of melon seed physical parameters and calibration of discrete element simulation parameters.

To improve the accuracy of the Hami melon discrete element model, the parameters of the Hami melon seed discrete element model were calibrated by combining practical experiments and simulation tests. The basic physical parameters of Hami melon seeds were obtained through physical experiments, including triaxial size, 100-grain mass, moisture content, density, Poisson's ratio, Young's modulus, shear modulus, angle of repose, suspension speed and various contact parameters. Taking the repose angle of seed simulation as an index, the parameters of each simulation model were significantly screened by the Plackett-Burman test. The results showed that the recovery coefficient, static friction coefficient and rolling friction coefficient of Hami melon seeds had significant effects on repose angle. Based on the steepest climbing test and quadratic regression orthogonal rotation combination test, it was determined that the significant order of the influence of various contact parameters on the angle of repose was static friction coefficient, collision recovery coefficient, and rolling friction coefficient. The optimal parameter combination was obtained through the mathematical regression model between the angle of repose and various contact parameters, namely, the collision recovery coefficient of Hami melon seeds was 0.518, the static friction coefficient of Hami melon seeds was 0.585 and the rolling friction coefficient of Hami melon seeds was 0.337. Under this condition, three static seed-dropping experiments and dynamic rolling accumulation experiments were carried out. The average simulated angle of repose was 31.93°, and the relative error with the actual value was only 1.71%. The average simulated rolling accumulation angle was 51.98°, and the relative error with the actual value was only 1.92%.

Optimizing UAV spray parameters to improve precise control of tobacco pests at different growth stages.

Unmanned aerial vehicles (UAVs) for pesticide application show promising potential in tobacco pest management. However, the impact of flight parameters on spray efficacy requires further investigation. Three field experiments were conducted from the rosette to the maturation stage of tobacco to systematically assess spray efficacy under varying flight heights, speeds, and application volumes. Using a multi-index weight analysis method, optimal operational parameter combinations for different tobacco growth stages were evaluated and compared with backpack electric sprayers. For the rosette stage, the recommended parameter is a flight speed of 5 m s-1, a flight height of 2 m, and a liquid application volume of 30 L hm-2; during the vigorous growth stage, the suggested parameter includes a flight speed of 3 m s-1, a flight height of 2 m, and a liquid application volume of 22.5 L hm-2. In the maturing stage, optimal parameter consists of a flight speed of 3 m s-1, a flight height of 3.5 m, and a liquid application volume of 30 L hm-2. Furthermore, UAV spraying achieves higher droplet deposition on both sides of tobacco leaves compared to traditional electric backpack sprayers. Adjusting UAV spraying parameters for different tobacco growth stages is crucial. These results can provide the methods for the precise control technology of tobacco pests at different growth stages. © 2024 Society of Chemical Industry.

Optimization of Flexural Performance of PETG Samples Produced by Fused Filament Fabrication with Response Surface Method.

Additive manufacturing (AM), particularly fused filament fabrication (FFF), has gained significant attention for its design flexibility and cost-effectiveness. This study focuses on optimizing FFF parameters that employ response surface methodology (RSM) to enhance the flexural performance of polyethylene terephthalate glycol (PETG) parts. Three essential parameters-layer height, print speed, and nozzle temperature-were varied, and their effects on flexural strength, flexural modulus, flexural toughness for ultimate strength, flexural toughness at 5% strain, and strain at ultimate strength were evaluated. Based on a Box-Behnken design, the experiments revealed significant effects of these parameters on the mechanical responses. The analysis of variance (ANOVA) indicates that layer height predominantly affects flexural modulus and toughness, while nozzle temperature significantly impacts flexural strength. The RSM models exhibited high accuracy, with R2 values exceeding 99%. Optimal parameter combinations yield remarkable improvements: flexural strength reached 39.55 MPa, flexural modulus peaked at 1344.60 MPa, flexural toughness for ultimate strength reached 218.22 J/mm3, flexural toughness at 5% strain reached 381.47 J/mm3, and strain at ultimate strength reached 3.50%. Validation experiments confirm the effectiveness of the optimization, with errors below 3.17%.

Optimal design of an inclined barrier for performance improvement of an oscillating water column device with an artificial neural network model

An oscillating water column (OWC) device with a surface-piercing inclined barrier has been studied in irregular waves. This study is to determine the optimal shape of an inclined barrier utilizing an Artificial Neural Network (ANN) model for the performance enhancement of the conventional OWC system. We employed the dual boundary element method (DBEM) as our numerical approach, which resolves two separate boundary integral equations (singular and hyper-singular) formulated at the coincident source points situated on either side of the degenerate barrier boundary. The design parameters affecting the performance of the OWC system have been optimized to maximize the power extraction using the trained ANN model. The training dataset was prepared using the DBEM, which is then used to train and validate the ANN model. Subsequently, the trained model was employed to make predictions using a broader dataset to identify the optimal combination of design parameters that maximize the conversion efficiency. The findings reveal that the well-trained ANN model can learn complex nonlinear relationships, make good predictions with high accuracy using a larger dataset of input conditions, and offer the optimal values of an inclined barrier appropriate for the wave conditions at the sea installation site.

Effects of transcranial direct current stimulation on different cognitive domains in Alzheimer's disease: a meta-study.

Numerous studies have investigated the potential effects of transcranial direct current stimulation (tDCS) on improving symptoms related to Alzheimer's disease (AD). However, these studies have produced inconsistent results, leading to a need for further investigation. A comprehensive search was conducted, including articles published from the initial availability date to 5 April 2024. The extracted study data were analyzed using STATA 12.0 software. The standard mean difference (SMD) and a 95% confidence interval (CI) were calculated to assess the effects of tDCS. A total of 18 studies assessing the effects of tDCS on AD were included in the study. The study revealed that tDCS has an immediate positive impact on general cognitive, executive, language, and visuospatial function. However, the study did not observe any other significant effect of tDCS treatment on improvements in brain function, including long-term effects on general cognitive, attention, language, and memory function, as well as immediate effects on attention and memory function. In conclusion, the study suggests that tDCS may be a promising intervention for improving the cognitive function of patients with AD. However, given the complex and multifactorial nature of AD, further well-designed studies with larger sample sizes are necessary to clarify the effectiveness of tDCS and determine the optimal combination of tDCS parameters.

Investigation of continuous wave/coaxial waterjet-assisted laser combined machining of micro holes in GH3536

A two-step combined processing technique, combining a continuous-wave laser with a waterjet-assisted nanosecond laser, has been introduced to enhance the quality and efficiency of laser micro-hole processing for nickel-based superalloys. Initially, a through-hole was created using a continuous-wave laser, followed by hole refinement with a coaxial waterjet-assisted nanosecond laser. In the first step, orthogonal testing methods were employed to investigate the impact of various parameters, including power, duty ratio, frequency, defocusing amount, and processing time, on the entrance and exit apertures, as well as the hole taper. Through the analysis of variance (ANOVA) method, the optimal parameter combination A2B1C4D3E1 was identified, achieving the minimal hole taper. This combination comprised a power level of 80 %, a duty ratio of 15 %, a frequency of 400 Hz, a defocusing amount of 2 mm, and a processing time of 10 ms. In the second step, a comparison was made between scanning and trepan drilling machining methods. The results unequivocally demonstrated that scanning drilling achieves superior entrance and exit morphologies. Furthermore, the effect of water jet velocity on the morphology of micropores was thoroughly analyzed. The voltage range of the water pump was adjusted from 10 V to 24 V, revealing that a voltage of 20 V produced the smallest hole taper. The scanning filling diameter was accurately preset according to the preformed hole aperture as well as the thickness of the recast layer. Finally, after coaxial waterjet-assisted laser precision machining, the inner wall of the hole is free of cracks, and the recast layer and oxide layer are effectively minimized. The initial hole prefabrication with the continuous-wave laser took merely 0.01 s, and the subsequent fine-tuning with the coaxial waterjet-assisted laser took 2 min and 25 s.

Numerical Simulation and Bench Test of Crawler-Type Cotton Time-Delay Hole-Forming Device Based on RecurDyn-EDEM Coupling

In view of the challenges faced by cotton dibbler in Xinjiang under high-speed operation, a novel crawler-type delayed hole-forming device has been designed to address the seed throwing and floating issues in high-speed cotton dibbling in Xinjiang, enhancing the duck bill’s performance. This mechanism increases the sowing speed to 6 km/h by extending the duck bill horizontally. Utilizing agronomic principles, the mechanism’s layout and key components were optimized for efficient hole-forming. DEM and multi-body dynamics simulations were employed to analyze the motion, focusing on the fixed the tilt angle of the duck bill (A), the depth of the duck bill hole-forming into the soil (B), and the angle of rotation of the moving duck bill (C) as factors affecting hole dimensions (longitudinal length of hole Y1 and hole-forming depth of cotton seed hole Y2). Quadratic regression test using RecurDyn-EDEM coupling identified optimal parameter settings for maximum hole-forming performance. When A was 2.4°, B was 42.4 mm, and C was 30.5°, the performance of the hole-forming was the best. Under the optimal parameter combination, the bench verification test was carried out. The error between the bench verification results and the simulation results is small, indicating that the model has high accuracy. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill (0.11 s). It meets the requirements of high-speed cotton planting in China and facilitating advancements high-speed planter technology.