Optimal Parameter Combination Research Articles

Peanut Pickup Combine Harvester Seedling Vines Crushing Mechanism Performance Enhancement and Pilot Studies

Aiming at the current peanut pickup combine harvester seedling vines crushing mechanism operation qualified seedling length rate is low, the impurity rate is high, and the seedling vines crushing mechanism is optimized design. On the basis of the material characteristics of peanut seedling vines, taking the crushing mechanism as the research carrier, the interaction mechanism of the seedling vine mechanism was analyzed. Establish the mechanical model and motion trajectory model in the crushing process, simulate the peanut seedling vines crushing process based on the discrete element method, analyze the influence of the cutter shaft rotational speed, movement speed, and bending angle on the crushing of peanut seedling vines, and establish the regression model, and the results of the research show that the influence factors in the main order of priority are: knife shaft speed > movement speed > bending angle The optimal parameter combination is knife shaft speed 2171.94 r/min, movement speed 0.79 m/s, and bending angle 45°. According to the field test conditions, it is determined that the knife shaft rotational speed of 2170 r/min, movement speed of 0.8 m/s, and bending angle of 45° can make the peanut seedling vines crushing mechanism of the work performance to achieve a qualified seedling length rate of 97.292%, the rate of impurity content of 2.746%, and the error with the predicted value is less than 2%, which indicates that this study can provide a reference for the optimal design of the seedling vines crushing mechanism of the peanut pickup combine harvester.

A microscopic oxygen transport model for ultra-high dose rate radiotherapy in vivo: The impact of physiological conditions on FLASH effect.

Ultra-high dose rate irradiation (≥40Gy/s, FLASH) has been shown to reduce normal tissue toxicity, while maintaining tumor control compared to conventional dose-rate radiotherapy. The radiolytic oxygen (O2) depletion (ROD) resulting from FLASH has been proposed to explain the normal tissue protection effect; however, in vivo experiments have not confirmed that FLASH induced global tissue hypoxia. Nonetheless, the experiments reported are based on volume-averaged measurement, which have inherent limitations in detecting microscopic phenomena, including the potential preservation of stem cells niches due to local FLASH-induced O2 depletion. Computational modeling offers a complementary approach to understand the ROD caused by FLASH at the microscopic level. We developed a comprehensive model to describe the spatial and temporal dynamics of O2 consumption and transport in response to irradiation in vivo. The change of oxygen enhancement ratio (OER) was used to quantify and investigate the FLASH effect as a function of physiological and radiation parameters at microscopic scale. We considered time-dependent O2 supply and consumption in a 3D cylindrical geometry, incorporating blood flow linking the O2 concentration ([O2]) in the capillary to that within the tissue through the Hill equation, radial and axial diffusion of O2, metabolic and zero-order radiolytic O2 consumption, and a pulsed radiation structure. Time-evolved distributions of [O2] were obtained by numerically solving perfusion-diffusion equations. The model enables the computation of dynamic O2 distribution and the relative change of OER (δROD) under various physiological and radiation conditions in vivo. Initial [O2] level and the subsequent changes during irradiation determined δROD distribution, which strongly depends on physiological parameters, i.e., intercapillary spacing, ultimately determining the tissue area with enhanced radioresistance. We observed that the δROD/FLASH effect is affected by and sensitive to the interplay effect among physiological and radiation parameters. It renders that the FLASH effect can be tissue environment dependent. The saturation of FLASH normal tissue protection upon dose and dose rate was shown. Beyond ∼60Gy/s, no significant decrease in radiosensitivity within tissue region was observed. In turn, for a given dose rate, the change of radiosensitivity became saturated after a certain dose level. Pulse structures with the same dose and instantaneous dose rate but with different delivery times were shown to have distinguishable δROD thus tissue sparing, suggesting the average dose rate could be a metric assessing the FLASH effect and demonstrating the capability of our model to support experimental findings. On a macroscopic scale, the modeling results align with the experimental findings in terms of dose and dose rate thresholds, and it also indicates that pulse structure can vary the FLASH effect. At the microscopic level, this model enables us to examine the spatially resolved FLASH effect based on physiological and irradiation parameters. Our model thus provides a complementary approach to experimental methods for understanding the underlying mechanism of FLASH radiotherapy. Our results show that physiological conditions can potentially determine the FLASH efficacy in tissue protection. The FLASH effect may be observed under optimal combination of physiological parameters, not limited to radiation conditions alone.

基于 EDEM 离散元方法的盐碱土壤参数标定与测试

In this study, saline soil parameters were calibrated based on EDEM discrete element method, soil density, soil elastic modulus, soil shear modulus, soil particle size distribution and Poisson's ratio were determined. The Hertz-Mindlin with Johnson-Kendall-Roberts (JKR) model was used to simulate the characteristics of soil stress and strain of particles under external forces. The JKR contact parameter between saline soil particles was used as a test factor, and the soil accumulation angle was used as a test index to carry out the saline soil contact parameter calibration test. It was finally determined that an elastic recovery coefficient of 0.262, a static friction coefficient of 0.263, a dynamic friction coefficient of 0.234, and a JRK surface energy of 10.084 were the optimal combinations of contact parameters for saline soils.

Fabricating diamond bit by selective laser melting and its drilling performance evaluation

Diamond abrasive tools play an important role in efficient and precision machining of difficult-to-cut materials. As the traditional tool manufacturing process is difficult to meet the integrated molding preparation of complex structures, the use of selective laser melting (SLM) technology to manufacture diamond tools with functions and structures has become a research hotspot in recent years. In this paper, the feasibility and wear characteristics of diamond bits prepared by SLM using AlSi7Mg as metal matrix are investigated. The optimal parameter combination of diamond/AlSi7Mg mixture was obtained through orthogonal experiments, in which the powder layer thickness was 30 μm, the laser power was 300 W, the scanning speed was 3000 mm/s, and the scanning spacing was 120 μm. This parameter combination realized the improvement of the mechanical properties and the decrease of the porosity. Two types of bits with and without groove-structure (Labeled as Bit-G and Bit-N) were fabricated and their drilling performance were compared by processing BK7 optical glass. The results show that the Bit-G has more excellent machining performance, including lower drilling force, better hole quality and longer service life. According to the simulation results, the self-sharpening cycle of the Bit-G’s wear characteristics can be attributed to the groove structure that allows the coolant to be fully utilized in the bit/workpiece contacting area. This paper demonstrates the feasibility of the SLM process for the preparation of diamond tools and comprehensively analyzes the wear process, which provides reference value for further research on this process for the manufacturing of diamond abrasive tools.

Experimental optimization of the FSW parameters to weld the dissimilar Al- and Cu-alloys by using additives at their joints

Friction stir welding (FSW) stands as a promising technique for joining materials, offering advantages over conventional methods. This study investigates the influence of tool tilt angle, rotational speed and profile on welded joint characteristics using a design of experiment (DOE) approach. The research objective aims to determine optimal parameter combinations for improved mechanical properties. Methodology entails the systematic variation of tilt angle, rotational speed and tool profile in a DOE setup, with subsequent FSW of samples. Mechanical testing, including tensile, bend and hardness tests, is conducted on 24 samples, with scanning electron microscope (SEM) and microstructure analysis on two specimens. Results reveal that higher tilt angles, rotational speeds and threaded profiles yield increased tensile strength but decreased ductility. Similarly, bend tests show higher fracture loads but reduced deflection before fracture under these conditions. Hardness tests indicate elevated hardness in welded areas, with variations based on input parameters. Main findings highlight the importance of parameter optimization for desired mechanical properties. Lower tilt angles and rotational speeds promote smoother material flow and uniform heating, resulting in consistent microstructure and hardness distribution. In conclusion, this study provides valuable insights for optimizing FSW parameters to achieve desired mechanical properties in welded joints. The combination of a 2° tilt angle, threaded tool profile and 1500 rpm rotational speed demonstrated the highest tensile strength, while a 1° tilt angle, cylindrical tool profile and 1000 rpm rotational speed yielded highest ductility.

Optimization Design of Redundant Parallel Posture Adjustment Mechanism for Solar Wing Docking Based on Response Surface Methodology

The parallel mechanism exhibits high stiffness and excellent dynamic response, making it ideal for high-precision applications. In our early work, a novel 6-DOF redundant parallel posture mechanism with four limbs for solar wing docking has been proposed; each limb consists of three links and four joints. This paper primarily focuses on optimization design of the mechanism. The calculation of workspace volume reveals that factors influencing the range of posture adjustment include dynamic platform parameters, static platform parameters, the drive trajectory of each kinematic pair, and the angles between each kinematic pair. A sensitivity analysis was conducted to examine the impact of each parameter on the range of posture adjustment. To reduce computational complexity and improve analysis efficiency, a combined approach of single-factor analysis and response surface methodology (RSM) is used in the paper. Single-factor analysis is utilized to evaluate the effect of each parameter on the posture adjustment range. Based on these results, RSM is used to establish a regression model for parameters; thereby, the optimal parameter combination for the mechanism is determined. The regression coefficient R2 = 0.9374 attests to the validity of the proposed model. Finally, a comparison of the posture adjustment range before and after optimization is presented, providing a foundation for the practical application of the redundant parallel mechanism. This paper introduces a novel structural design concept aimed at resolving the conflict between heavy loads and compact sizes in redundant parallel mechanisms while providing valuable insights for miniaturized design.

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain–computer interface to improve the effect of node displacement

Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated.

Optimization of Wood-Based Birch Plywood CO2 Laser Engraving Process Parameters with Taguchi Method

In this study, the optimization of the parameters used in the engraving process of wooden birch plywood material with a CO2 laser machine was investigated using the Taguchi method. An industrial laser machine with a 150W glass tube was used during the experimental examination process. The basic parameters for engraving wooden surfaces include laser power (P), engraving speed (S) and laser head height (F). The engraving depth (D) and engraving width formed on the surface during the engraving process are the main factors that determine the aesthetics of the product. Taguchi L25 orthogonal array was used in the experiments to determine and optimize the highly important parameters. The optimum combination of parameters in the laser engraving process was then evaluated. The research results showed that the effect of P and S factors played a leading role, and the F parameter had a small effect on the depth of wood scraping. Optimization results found that F:5, S:100 and P:30 gave the best engraving depth optimization, while F:6, S:300 and P:10 gave the lowest engraving depth optimization result.

Research on mine water source classifications based on BO-CatBoost.

Mine water surge is one of the main safety risks in coal mines. This research offers a novel mine water source identification model (BO-CatBoost) to successfully avoid and control mine sudden water catastrophes by properly identifying the sources of mine water. First, the classification model is trained and built using the Categorical Boosting (CatBoost) algorithm. The Gaussian process Bayesian optimization (BO) algorithm is used to optimize parameters, and the optimal parameter combination is integrated into the CatBoost algorithm to build the BO-CatBoost mine water source identification model, which further improves the accuracy of mine water source identification. The model was also applied to the Pingdingshan mine to verify the practicality of the model. Then, 29 groups of unknown water sources in Pingdingshan were selected as validation samples for the model and compared with the conventional CatBoost, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (Xgboost) models. The comparison results demonstrate that the accuracy of LightGBM, Xgboost, CatBoost, and BO-CatBoost models can reach 69%, 79.3%, 79.3%, and 100% respectively, and the RMSE is 0.947, 0.643, 0.719, and 0.0 respectively. The comprehensive analysis shows that, when it comes to mine water source detection, the BO-CatBoost model performs noticeably better than other models in terms of discriminative accuracy and generalization capacity. Lastly, the multi-output prediction and decision-making process of the BO-CatBoost water source identification model is visualized by the interpretability analysis performed with the SHAP approach. The research demonstrates that the BO-CatBoost model can more precisely and impartially identify mine water sources, offering fresh concepts for mine water source detection.

基于蚯蚓运动特征的仿生筛清选性能试验研究

As the feeding mass of harvester continues to be increased, the accumulation of the maize mixture on the screen more serious, leading to a decrease in screening efficiency of the cleaning screen. The maize mixture on the screen was quickly transported backward through a bionic screen based on the motion characteristics of earthworms to reduce accumulation. However, the impact of the main operating parameters of this bionic screen on the cleaning performance was not yet clear. Therefore, the main operating parameters that affect the loss percentage and impurity percentage of maize grain in the cleaning device of bionic screen were simulated using CFD-DEM. Analysis of variance, regression and response surface were carried out on the test data, and the optimal parameter combination was obtained through parameter optimization: the concave depth of the screen surface was 25 mm, the installation angle of the screen surface was –5.5°, and the airflow velocity at the inlet of the cleaning device was 12.3 m/s. At this time, the lost percentage of maize grain was 1.56%, and the impurity percentage of maize grain was 1.85%, which met the requirements of national standards. The experiment provides reference and basis for the follow-up exploration of the screening mechanism of bionic screen.

Optimization of friction stir processing parameters to improve mechanical properties and microstructure of Al5083 aluminum alloy reinforced with AlCoCrFeNiSi high-entropy alloy

Abstract This study explores integrating AlCoCrFeNiSi high-entropy alloy (HEA) particles into the Al5083 aluminum alloy matrix via Friction Stir Processing (FSP) to enhance mechanical characteristics. Microstructural analysis reveals a homogeneous distribution and size reduction of HEA particles, contributing to improved structural strength. X-ray diffraction (XRD) examination confirms the formation of solid solution phases in the HEA particles, validating their role in enhancing material properties. Through the utilization of Design of Experiments (DOE) and Response Surface Methodology (RSM), FSP parameters are systematically optimized, enabling precise predictions of mechanical behavior. Multi-response optimization identifies the optimal combination of FSP parameters, resulting in significant enhancements in Ultimate Tensile Strength (UTS) and Hardness, reaching 314 MPa, 42% elongation, and 75 HV, respectively. Scanning Electron Microscopy (SEM) analysis of tensile test specimens elucidates the impact of varied FSP parameters on microstructural features, emphasizing the importance of optimal mixing for improving interfacial bonding and mechanical properties. This study underscores the effectiveness of integrating HEA particles and optimizing FSP parameters to elevate the mechanical properties of Al5083 aluminum alloy, paving the way for tailored composite materials with enhanced performance for specific applications.

Optimization of process parameters for Trifolium pratense L. seed granulation coating using GA-BP neural network

Regarding the issue of low granulation qualification rates during the granulation coating of red clover seeds, this study theoretically analyzed the force conditions of seeds and powder particles under the action of liquid to obtain the main factors affecting seed coating quality. During the seed granulation coating process, an intermittent powder supply method combined with continuous liquid supply was utilized to control the ratio of powder to liquid. Using the granulation qualification rate as the evaluation index, single-factor experiments were conducted to investigate the effects of coating pan fill ratio, single powder supply amount, powder supply interval, and liquid supply amount on the quality of red clover seed granulation coating. Based on the results of the single-factor experiments, orthogonal experiments were conducted, revealing that the interaction of factors would influence the experimental results. To further optimize the quality of seed granulation coating, the mechanisms of powder and liquid in the adhesion process on granulation coating were explored. Orthogonal experiments were conducted on the process parameters of the granulation coating machine, and the GA-BP model was employed for optimization and solution. The optimal process parameter combination obtained was a coating pan fill ratio of 33.78%, a single powder supply amount of 5.17 g, a powder supply interval of 7.7 s, and a liquid supply amount of 0.42 mL/s. Under this optimal parameter combination, granulation coating experiments with red clover seeds were performed, and the seed granulation coating quality was relatively high, with a granulation qualification rate of 97.7%. The research results can provide a reference for optimization experiments on coating irregular seeds.

Experimental study of NiTi alloy cardiovascular stent formed via SLM

Selective laser melting (SLM) has garnered significant attention in the manufacturing of cardiovascular stents due to its potential for fabricating customized stents with intricate geometries that meet clinical requirements. This study aims to optimize the SLM parameters for NiTi stents to achieve minimal porosity and surface roughness, and investigate the biocompatibility and compression tests of SLMed stents. Firstly, the single-factor experiments of laser power, scanning speed and hatch spacing are conducted, respectively. Subsequently, a Box-Benhnken experimental was employed to establish regression prediction models for porosity and surface roughness using response surface methodology (RSM). The results show that scanning speed is the most influential factor on porosity, while laser power significantly affects surface roughness. The optimized parameter combination with minimum porosity of 0.165% and surface roughness of 6.17 μm as follows: laser power of 156W, scanning speed of 712mm/s, and hatch spacing of 0.9mm. The relative errors of them are 0.19% and 0.8%, respectively, indicating that the regression model possesses good prediction ability. Additionally, the corrosion rate and nickel ion release gradually increase with the immersion time, reaching the highest corrosion rate of 2.46g/m² and nickel ion release of 1.08μg after 42 days. Besides, the SLMed stent demonstrated good recovery ability in the compression test. The findings promote the utilization of SLM in the manufacturing of NiTi cardiovascular stents for medical implant applications.

Microstructure phase analysis and process optimization of ZL205A metal mold casting

In this paper, we take an elastomeric transmission structure part made of ZL205A alloy as the research object, and use the metal type gravity casting technology for casting trial production, analyze the quality and mechanical properties of the molded parts of ZL205A alloy parts under different process parameters, optimize the casting process parameters, get the optimal combination of process parameters, and obtain large ZL205A castings with less internal quality defects and high mechanical properties.

Operation parameters study on the performance of PEMFC based orthogonal test method

By selecting five key operational parameters—inlet temperature, operating temperature, anode humidity, cathode humidity, and operating pressure—and using PEMFC power density, membrane temperature difference, and membrane water content as performance indicators, simulations were conducted using 3D CFD modeling and orthogonal experiments to identify the most significant factors influencing PEMFC performance and determine the optimal operational parameter combination. The results show that working pressure has the most significant impact on PEMFC performance. Pressure variations cause significant changes in diffusion and heat conduction within the PEMFC. When the working pressure increases from 1.0 MPa to 3.0 MPa, the power density increases by approximately 11.2 %, but the temperature difference within the membrane increases by about 30.5 %. The difference between operating temperature and inlet temperature directly leads to changes in the temperature difference within the membrane. Moreover, changes in operating temperature greatly affect the water content within the proton exchange membrane, which in turn impacts the PEMFC's power density. When the operating temperature increases from 60 °C to 100 °C, the water content within the membrane decreases by approximately 50.5 %, and the power density decreases by 60.4 %. By optimizing the combination of operating parameters using a comprehensive scoring method, the temperature difference within the membrane can be controlled at 27.78 °C while ensuring the power density is increased to 0.92 W/cm2.

Optimization of Laser Cladding Parameters for High-Entropy Alloy-Reinforced 316L Stainless-Steel via Grey Relational Analysis

Laser cladding technology serves as a pivotal technique in industrial production, especially in the realms of additive manufacturing, surface enhancement, coating preparation, and the repair of part surfaces. This study investigates the influence of metal powder composition and processing parameters on laser cladding coatings utilizing the Taguchi orthogonal experimental design method. To optimize the laser cladding parameters, multi-response grey relational analysis (GRA) was employed, aiming to improve both the microhardness and the overall quality of the coatings. The optimal parameter combinations identified through GRA were subsequently validated through experimental tests. The results reveal that the microhardness and quality of the coatings are substantially influenced by several critical factors, including the powder feed rate, laser power, high-entropy alloy (HEA) addition rate, scanning speed, and substrate tilt angle. Specifically, the powder feed rate exerts the most significant effect on the microhardness, dilution rate, and average contact angle. In contrast, laser power primarily impacts the mean contact angle difference. The HEA addition rate notably affects the mean contact angle difference, while the scanning speed affects the microhardness and the substrate tilt angle influences the average contact angle. The results of the validation experiment showed a deviation of only 0.95% from the predicted values, underscoring the efficacy of the grey relational analysis (GRA) in optimizing the laser cladding process parameters. The methodology presented in this paper can be applied to determine the ideal processing parameters for multi-response laser cladding processes, encompassing applications such as surface peening and surface repair.

Influence of spatial position of sound source on dual-frequency ultrasound-assisted preparation of magnesium alloy

In this work, a dual-frequency ultrasonic field was employed to improve the quality and mechanical properties of magnesium alloy ZW61 ingot. The effect of the spatial position of the sound source on the cavitation volume, microstructure size, and morphology was discussed in detail combined with numerical simulations and experiments. The mechanical properties were analyzed elaborately and the relationship between yield strength (YS), ultimate tensile strength (UTS), and average grain size (d) was established. The results showed that the dual-frequency ultrasonic field (DUF) had a larger cavitation range and superior grain refinement ability than the single-frequency ultrasonic field (SUF) at the same energy consumption, with the grain refining from 578 μm (untreated) and 395-433 μm (SUF) to 55.2 μm. The grain refining effect of DUF weakened with increasing sound source spacing, with d increasing from 55.2 μm to 163 μm. The radiating angle had little effect on the grain size. Too shallow or too deep inserting depth of the ultrasonic rods could seriously weaken the grain refinement. In this case, 30mm source spacing, 60° radiating angle, and 30mm inserting depth were the optimal process parameter combinations, and the ingot strength was increased from YS of 83MPa and UTS of 151MPa to 138MPa and 241MPa, respectively. YS and d for all conditions conformed to the Hall-Petch relationship, with σ0 and k values of 71.3MPa and 503.9MPa μm-1/2. The relationship between UTS and d could be expressed as UTS = 126.7 + 907.0 d-1/2.

Optimization of multiple responses in the laser cladding process parameters

ABSTRACT An orthogonal test method was used to optimize parameters for Ni60A-25% tungsten carbide (WC) laser cladding layers. Meanwhile, the tensile properties of the optimized laser cladding layer are explored. A multi-factor analysis of variance (ANOVA) was conducted to examine the effect of factors (laser power-A, scanning speed-B, powdering rate-C, and spot diameter-D) and interactions on responding variable. The results showed that the interaction of scanning speed and powdering rate with spot diameter has a more pronounced impact on the dilution rate compared to a single factor. Based on the ANOVA of the dilution rate, the optimal combination of process parameters was A3C2B1D3. Furthermore, the optimal combination of process parameters was C3B2D3A3 for the effective area. The results showed that the yield strength of the specimens was 512.3 MPa and 509.7 MPa, and the tensile strength was 646.3 MPa and 605.7 MPa for process parameters I and II, respectively.

Residual Stress and Warping Analysis of the Nano-Silver Pressureless Sintering Process in SiC Power Device Packaging

Chip bonding, an essential process in power semiconductor device packaging, commonly includes welding and nano-silver sintering. Currently, most of the research on chip bonding technology focuses on the thermal stress analysis of tin–lead solder and nano-silver pressure-assisted sintering, whereas research on the thermal stress analysis of the nano-silver pressureless sintering process is more limited. In this study, the pressureless sintering process of nano-silver was studied using finite element software, with nano-silver as an interconnect material. Using the control variable method, we analyzed the influences of sintering temperature, cooling rate, solder paste thickness, and solder paste area on the residual stress and warping deformation of power devices. In addition, orthogonal experiments were designed to optimize the parameters and determine the optimal combination of the process parameters. The results showed that the maximum residual stress of the module appeared on the connection surface between the power chip and the nano-silver solder paste layer. The module warping deformation was convex warping. The residual stress of the solder layer increased with the increase in sintering temperature and cooling rate. It decreased with the increase in coating thickness. With the increase in the coating area, it showed a wave change. Each parameter influenced the stress of the solder layer in this descending order: sintering temperature, cooling rate, solder paste area, and solder paste thickness. The residual stress of the nano-silver layer was 24.83 MPa under the optimal combination of the process parameters and was reduced by 29.38% compared with the original value of 35.162 MPa.