Optimal Parameter Combination Research Articles

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.

Experimental Investigation on in-situ Laser-Assisted Mechanical Ruling of Single Crystal Silicon

Abstract In this study, 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 0.25mm/s, 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%.

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.

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 =47°, impeller outer diameter D2=140mm and impeller inlet width b2=34mm. 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.

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.

Discrete element modelling and mechanical properties and cutting experiments of Caragana korshinskii Kom. stems

The forage crop Caragana korshinskii Kom. is of high quality, and the biomechanical properties of its plant system are of great significance for the development of harvesting equipment and the comprehensive utilisation of crop resources. However, the extant research on the biomechanical properties of Caragana korshinskii Kom. is inadequate to enhance and refine the theoretical techniques for mechanised harvesting. In this study, we established a discrete element model of CKS based on the Hertz-Mindlin bonding contact model. By combining physical experiments and numerical simulations, we calibrated and validated the intrinsic and contact parameters. The Plackett-Burman design test was employed to identify the significant factors influencing bending force, and the optimal parameter combination for these factors was determined through response surface analysis. When the shear stiffness per unit area was 3.56×109 Pa, the bonded disk scale was 0.93 mm, the normal stiffness per unit area was 9.68×109 Pa, the normal strength was 5.62×107 Pa, the shear strength was 4.27×107 Pa, the discrete element numerical simulation results for three-point bending, radial compression, axial tension, and shear fracture exhibited a maximum failure force error of 3.32%, 4.37%, 4.87% and 3.74% in comparison to the physical experiments. In the cutting experiments, a smaller radial angle between the tool edge and the stem resulted in less damage to the cutting section, which was beneficial for the smoothness of the stubble after harvesting and the subsequent growth of the stem. The discrepancy in cutting force between the physical and numerical simulations was 3.89%, and the F-x (force versus displacement) trend was consistent. The multi-angle experimental validation demonstrated that the discrete element model of CKS is an accurate representation of the real biomechanical properties of CKS. The findings offer valuable insights into the mechanisms underlying crop-machine interactions.

Optimization Study of the Structural Parameters of an Artificial Percolation Intake Riverbed Based on a Numerical Orthogonal Test

Research on water abstraction techniques in sandy rivers is crucial for addressing water scarcity issues in northern China. This study introduces an artificial percolation water intake riverbed structural model designed to prevent sand intake and facilitate the extraction of clear water from sandy rivers, the main function of which is to artificially construct a helical flow field to realize the separation of water and sand and, at the same time, percolate the water. In this paper, we use a numerical orthogonal test method to study the influence of structural parameters and their combinations on water extraction by percolation, and the preferred combination of structural parameters. The main conclusions of this paper are as follows: Under the condition of certain structural parameters of the artificial riverbed, the single-width flow rate has an important effect on the mean circumferential flow rate of the vortex tube and the vortex tube seepage volume. The effect of different structural parameters on the mean circumferential flow rate of the vortex tube and the vortex tube seepage varies in magnitude. The longitudinal slope of artificial riverbed i had the greatest and most significant effect on the mean circumferential flow rate of the vortex tube, and each structural parameter had some effect on the vortex tube seepage Q but not to a significant level. When the sand content of the water flow is 15 kg/m3 and the maximum sand grain size of the suspended mass is 2 mm, the optimal structural parameter combinations of the artificial riverbed under different working conditions are as follows: Combination 20 is the optimal structural parameter combination for q = 0.3 m2/s; Combination 24 is the optimal structural parameter combination for q = 0.6 m2/s; and Combination 24 is the optimal structural parameter combination for q = 0.9 m2/s. The present study is of great significance for understanding the influence of each structural parameter and its combination on the effect of the water intake and sand discharge of an artificial percolation intake riverbed, and for optimizing the combination of the structural parameters of an artificial percolation intake riverbed.

Assessing the Tribological Impact of 3D Printed Carbon-Reinforced ABS Composite Cylindrical Gears

The tribological performance of carbon-reinforced acrylonitrile butadiene styrene (ABS) composites is very important in determining their suitability for advanced engineering applications. This study employs response surface methodology (RSM) to evaluate the effects of printing temperature and post-processing annealing on the wear resistance and frictional properties of these composites. A central composite design is used to systematically explore the interaction between these two factors, enabling the development of predictive models for key tribological parameters. The results reveal that both the coefficient of friction (COF) and wear are affected by printing and annealing temperatures, although in a non-linear manner. Moderate printing temperatures and lower annealing temperatures were found to reduce friction and wear, with annealing temperature having a more pronounced effect on wear. To further optimize these responses, the desirability approach was applied for predicting the optimal conditions. The optimal combination of input parameters for minimizing both COF and wear was found to be a printing temperature of 256 °C and an annealing temperature of 126 °C. This research provides valuable insights for optimizing additive manufacturing processes of carbon-reinforced ABS composites, contributing to enhanced material durability in practical applications.

Design and Experimental Analysis of an Air-Suction Wheat Precision Hill-Seed Metering Device

The uniformity of the wheat distribution within and between rows has a significant effect on crop population structure, leading to decreased yield as nonuniformity increases. Traditional drills are influenced by soil porosity and flatness in the field, making accurate control of sowing depth and amount challenging and resulting in an uneven spatial distribution of gramineous seedlings. Precision cave-sowing technology effectively enhances wheat population distribution uniformity. However, owing to limitations in existing mechanical precision cave planters, their operational speed is lower than that of drill planters. To address these issues, this study designed an air-suction precision wheat seed dispenser, described its basic structure and working principle, and developed a seed mechanical model. A theoretical analysis was conducted on the working process and key components of the seed feeder. A suitable mould hole diameter was determined to be 1.6~2.0 mm, and the rotation speed range for the seed plate was found to be 65~85 r·min−1. Fluent simulations were used to determine the influence of orifice type on gas chamber flow fields; DEM-CFD-coupled simulation identified an appropriate negative pressure range of 2.6~3.4 kPa for optimal performance during seeding operations. Orthogonal experiments were carried out with mould hole diameter, negative pressure size, and seed plate speed as test factors alongside a qualification index, multiple sowing index, and missed sowing index as response indicators—leading to regression equation establishment, which yielded the optimal parameter combination: mould hole diameter at 1.8 mm; gas chamber negative pressure at 3.2 kPa; and a seed plate speed of 74 r·min−1, with the corresponding forwards speed of the machine being 7 km·h−1—resulting in a qualification index of 91.66%, multiple sowing index of 5.98%, and missed sowing index of 2.36%. This pneumatic suction type wheat precision seeder achieves equivalent operational speeds as traditional drills while enabling precision seeding.

Optimization of Magnetic Field-Assisted Laser Cladding Based on Hierarchical Analysis and Gray Correlation Method

Process parameters directly affect the quality of laser cladding. In this study, magnetic field-assisted laser cladding experiments were carried out on the surface of 300 M ultra-high-strength steel by setting laser energy density, magnetic field strength, and frequency as processing parameters. The optimization of laser cladding process parameters was investigated based on evaluating the quality of the laser cladding layer through hierarchical analysis and gray correlation analysis. Based on orthogonal test data, the correlation coefficients of the process parameters with the single objective function and the correlation degree of the multi-objective function were calculated by using the gray theory. Then the comprehensive objective optimization was carried out according to the gray correlation degree. The optimization problem with multiple process objectives was transformed into a single gray correlation degree optimization method to realize the optimization of process objectives and obtain the optimal combination of process parameters. The validation experiments indicate that the quality of the laser cladding layer can be greatly improved by employing optimal process parameters. The optimized laser cladding layer shows a reduced microstructure size and enhanced wear resistance, indicating the effectiveness of the optimization approach.

A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network

To solve the problems of difficult-to-model parameter selections, useful-signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.

Improvement of hydrogen reciprocating compressor efficiency: A novel capacity control system and its multi-objective optimization

To enhance the operational efficiency of hydrogen compressors and reduce the costs associated with the storage and transportation of hydrogen, this paper has developed a novel capacity control system (CCS). This system ensures that the compressor accurately compresses hydrogen according to actual demand, driven by an electromagnetic actuator, which simplifies the system design and enhances operational precision. To optimize the system's performance, a multi-objective optimization was conducted. Initially, a hydrogen compressor backflow working model and a comprehensive evaluation index system were established. Subsequently, a three-tier computational multi-objective optimization framework was proposed based on the working model, ultimately determining the optimal combination of key system parameters. A prototype was manufactured and subjected to validation testing and a full life cycle analysis. The results indicate that the developed system can accurately regulate the amount of compressed hydrogen within a range of 0%–100% load. Compared to traditional systems, for the same compressor group, carbon emissions and costs were reduced by 41.93% and 36.49%, respectively. The optimized design of the internal and external resistance angles and the limiter plate thickness are 11°, 16°, and 1 mm, respectively. Compared to the baseline state, the specific energy consumption of the hydrogen compressor per unit of exhaust volume has been reduced by 28.22%. Additionally, during the ejection and retraction processes of the actuator, the peak vibration impact on the hydrogen compressor has been reduced by 77.6% and 73.3%, respectively, achieving optimal comprehensive performance.

Study of thermal stress deformation in hybrid 3D printing and milling process of PEEK material

Hybrid manufacturing can enable rapid production of personalized artificial bones that are made of Polyether-ether-ketone (PEEK) by integrating 3D printing and milling. A main challenge, however, is the thermal stress deformation that arises from the fused deposition process, leading to warping and shrinkage. As a result, the fabrication accuracy is significantly diminished. This work aims to address this challenge by investigating the thermal stress deformation behavior of PEEK in hybrid manufacturing. The main contributions of this work, therefore, include the development of a model for this thermal deformation behavior, and the identification of key parameters such as cross-section length ( Ls), printed layer thickness ( Lh), and current workpiece height ( He). Further, experiments are conducted based on response surface methodology (RSM) to explore the relationships between temperature variation rate (Δ T), end surface height difference (Δ S), and length shrinkage rate (Δ L) with critical hybrid processing parameters. Optimal parameter combinations are then identified. Our experiments demonstrate that the proposed methods effectively reduce the effects of warping and shrinkage.