Adjustment Of Process Parameters Research Articles

Modeling of multi-phase flow in plasma transferred arc cladding of NiCrBSi/WC metal matrix composite

In this paper, a 3D multiphase flow model has been established for the plasma transferred arc (PTA) cladding of composite powder (NiCrBSi/50 wt% WC) on a 40CrNi2MoA substrate. The multiphase flow model simulates both the discrete phase (WC particles) and continuous phase in the melt pool, including the behaviors of heat transfer and particle distribution. The buoyancy, arc pressure, surface tension, and electromagnetic force are calculated. The model is then used to predict the influences of welding currents and preheating temperatures on the depth of heat-affected zone (HAZ), the temperature field, and the dimensions of the cladding layers. To validate this model, the dimensions and the HAZ depth are analyzed and compared to experimental data. The results show that the dimensions and HAZ depth obtained by the model simulation are in good agreement with the experiment. The HAZ depth, the melt pool length, and the dimensions of cladding layer would increase with the increasing welding current or preheating temperature. Driven by the inward convection in the melt pool, WC particles tend to distribute around the center line of cladding layers. This is the first work of modeling 3D multiphase flow in PTA cladding process of particle-reinforced metal matrix composite (MMCs). The work can provide theoretical guidance for adjusting process parameters to control HAZ depth and cladding quality in actual production.

Compressive behavior of thermoplastic polyurethane with an active agent foaming for 3D-printed customized comfort insoles

The primary objective of this study was to investigate the compressive behavior of 3D-printed specimens made of a thermoplastic polyurethane filament, specifically colorFabb varioShore TPU, which incorporates a foaming agent. The foaming technology makes it possible to manipulate 3D prints’ properties by adjusting process parameters that affect the level of expansion, such as printing temperature, printing speed or flow ratio. This capability opens new possibilities for 3D-printed personalized foot orthotics or for static comfort-oriented applications by tailoring the stiffness of the print based on the application requirements. Since infill density and infill pattern also affect the compressive characteristics of 3D prints, this research took these two parameters into account as independent variables, alongside the printing temperature. Thus, the specimens with printing temperatures set at 190°C, 220°C, and 240°C, gyroid and honeycomb patterns, and infill variations from 10% to 35% in 5% increments were experimentally investigated in compression testing. The evaluation was conducted to determine the influence of these factors on the stiffness of prints at 10% and 20% strains, in accordance with established standard. The findings had an important practical value as they provide data for adjusting the variable stiffness based on peak plantar pressure measurement data when developing tailored foot orthoses (insoles), which was the second objective of the research. The most influential factor affecting the compressive strength was the printing temperature, followed by infill density and pattern type. The difference in compression modulus between 10% and 20% strains did not exhibit statistical significance. Moreover, the main effects plots indicated minimal variations in compression modulus effects at 220°C and 240°C, whereas an important difference was observed at 190°C printing temperature, which was confirmed by scanning electron microscopy investigations showing foamed layers at 220°C and unfoamed layers at 190°C. Furthermore, interaction plots revealed no significant interaction effect among the studied variables. Additionally, a comparison of samples’ sizes and densities before and two months after testing indicated no observable modifications When applied to custom orthotics (five pairs of insoles were customized and tested), the experimental findings documented variations in the reduction and distribution of peak plantar pressure based on the infill variability.

Optimized control of laser processing parameter on microstructural evolution and mechanical behavior in CoCrNi medium entropy alloy

In this study, the processing parameters of a laser powder bed fusion (LPBF)-fabricated CoCrNi alloy were systematically investigated for their impact on mechanical properties, microstructure, and fracture characteristics when the relative density exceeds 99%. The maximum relative density of 99.89% was attained at a laser power of 225 W, and scan speed of 1000 mm/s, accompanied by a hardness value of 276.83 HV. However, it is important to note that the highest relative density did not correspond to the maximum σUTS. Tensile testing revealed that increasing the laser power at the same scan speeds led to a decrease in elongation. The LPBF-fabricated CoCrNi alloy exhibited enhanced defect tolerance due to its cellular microstructure, which confined ductile dimple sizes to the nanoscale and impeded crack propagation through pinning effects. At scan speeds of 1000 mm/s and energy densities below 83 J/mm³, an increased presence of unmelted powder and pores was observed. Besides, it was confirmed that the <110> crystallographic texture displayed the strongest orientation within the sample. Theoretical calculations indicated that over 50% of the yield strength is attributed to the dislocation structure. These findings contribute to the process optimization of LPBF, providing a valuable reference for adjusting process parameters to enhance material mechanical properties, particularly when targeting relative densities above 99%.

Tailored gradient nanocrystallization in bulk metallic glass via ultrasonic vibrations

To advance materials with superior performance, the construction of gradient structures has emerged as a promising strategy. In this study, a gradient nanocrystalline-amorphous structure was induced in Zr46Cu46Al8 bulk metallic glass (BMG) through ultrasonic vibration (UV) treatment. Applying a 20 kHz ultrasonic cyclic loading in the elastic regime, controllable gradient structures with varying crystallized volume fractions can be achieved in less than 2 s by adjusting the input UV energy. In contrast to traditional methods of inducing structural gradients in BMGs, this novel approach offers distinct advantages: it is exceptionally rapid, requires minimal stress, and allows for easy tuning of the extent of structural gradients through precise adjustment of processing parameters. Nanoindentation tests reveal higher hardness near the struck surface, attributed to a greater degree of nanocrystal formation, which gradually diminishes with depth. As a result of the gradient dispersion of nanocrystals, an increased plasticity was found after UV treatment, characterized by the formation of multiple shear bands. Microstructural investigations suggest that UV-induced nanocrystallization originates from local atomic rearrangements in phase-separated Cu-rich regions with high diffusional mobility. Our study underscores the tunability of structural gradients and corresponding performance improvements in BMGs through ultrasonic energy modulation, offering valuable insights for designing advanced metallic materials with tailored mechanical properties.

Impact and torsional behavior of additive layer-manufactured biopolymer: An advancement for orthopedic applications.

Distal ulna locking bone plates (DLBPs) are commonly employed in the treatment of distal ulna fractures. However, commercially available metallic bone plates experience stress shielding and lack corrosion resistance. Poly lactic acid (PLA) is highly favored biopolymer due to its biocompatible and bioabsorbable nature with human tissues. The use of additive layer manufacturing (ALM) is gaining attention for creating customized implants with intricate structures tailored to patient autonomy. ALM-based PLA bone plates must provide high resistance against impact and torsional forces, necessitating the adjustment of printing process parameters. This study focuses on examining the influence of key printing parameters, on the impact strength and torque-withstanding capability of DLBPs. Experimental results, along with microscopic images, reveal that an increase in infill density (IF) and wall thickness imparts strong resistance to layers against crack propagation under impact and torsional loads. On the contrary, an increase in layer height and printing speed leads to delamination and early fracture of layers during impact and torsional testing. IF significantly contributes to improving the impact strength and torque-withstanding capability of DLBPs by 70.53% and 80.65%, respectively. The study highlights the potential of the ALM technique in developing DLBPs with sufficient mechanical strength for biomedical applications.

Expanded beads of polyethylene moldable at low steam pressure

In this study, we explored an innovative approach to extrude expanded polyethylene (ePE) bead foam by utilizing a blend of two types of polyethylene, specifically low-density and linear low-density polyethylene. The intention behind this mixture was to enhance the molding process’s flexibility during steam chest molding. The outcomes of our investigation reveal the adaptability of the devised method and formulation, enabling the production of a broad spectrum of foam densities ranging from 20 to 100 kg/m³. Additionally, we achieved successful molding at remarkably low steam pressures, below 0.6 bar. Our research focuses on the strategic adjustment of processing parameters, including temperature and pressure, along with optimized die geometry for the extrusion foam process. To comprehensively evaluate the foamed molded components, we conducted a comprehensive mechanical and optical analysis. The findings underscore the flexibility and cost-effectiveness of ePE produced through foam extrusion. This method emerges as a promising alternative to expandable polystyrene, especially in applications requiring flexible materials and convenient recyclability without contributing to microplastic pollution in the environment.

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction.

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

Effect of coil and chamber structure on plasma radial uniformity in radio frequency inductively coupled plasma

Enhancing plasma uniformity can be achieved by modifying coil and chamber structures in radio frequency inductively coupled plasma (ICP) to meet the demand for large-area and uniformly distributed plasma in industrial manufacturing. This study utilized a two-dimensional self-consistent fluid model to investigate how different coil configurations and chamber aspect ratios affect the radial uniformity of plasma in radio frequency ICP. The findings indicate that optimizing the radial spacing of the coil enhances plasma uniformity but with a reduction in electron density. Furthermore, optimizing the coil within the ICP reactor, using the interior point method in the Interior Point Optimizer significantly enhances plasma uniformity, elevating it from 56% to 96% within the range of the model sizes. Additionally, when the chamber aspect ratio k changes from 2.8 to 4.7, the plasma distribution changes from a center-high to a saddle-shaped distribution. Moreover, the plasma uniformity becomes worse. Finally, adjusting process parameters, such as increasing source power and gas pressure, can enhance plasma uniformity. These findings contribute to optimizing the etching process by improving plasma radial uniformity.

Achievement of a Parameter Window for the Selective Laser Melting Formation of a GH3625 Alloy.

In the selective laser melting (SLM) process, adjusting process parameters contributes to achieving the desired molten pool morphology, thereby enhancing the mechanical properties and dimensional accuracy of manufactured components. The parameter window characterizing the relationship between molten pool morphology and process parameters serves as an effective tool to improve SLM's forming quality. This work established a mesoscale model of the SLM process for a GH3625 alloy based on the discrete element method (DEM) and computational fluid dynamics (CFD) to simulate the forming process of a single molten track. Subsequently, the formation mechanism and evolution process of the molten pool were revealed. The effects of laser power and scanning speed on the molten pool size and molten track morphology were analyzed. Finally, a parameter window was established from the simulation results. The results indicated that reducing the scanning speed and increasing the laser power would lead to an increase in molten pool depth and width, resulting in the formation of an uneven width in the molten track. Moreover, accelerating the scanning speed and decreasing the laser power cause a reduction in molten pool depth and width, causing narrow and discontinuous molten tracks. The accuracy of the simulation was validated by comparing experimental and simulated molten pool sizes.

Ultra-strong and conductive graphene fiber via sacrificial layer technology

Microfluidic spinning technology (MST) is an emerging method specialized in the construction of multi-component and multi-structure fibers. In this study, we designed an ultra-strong coaxial graphene oxide (GO) fiber by employing sodium alginate (SA) as sheath flow via MST. The diameter of the fiber and the arrangement of GO molecules could be controlled by adjusting process parameters. More importantly, the outer SA layer could be removed during a subsequent reduction process of GO, which served as a sacrificial layer to GO fiber formation and enhanced the fiber strength. Ultimately, this could achieve a conductive graphene fiber with a maximum strength of 365.2 MPa and electrical conductivity of 52 Ω/cm via a common wet spinning mechanism.

Data-Driven Approach for Minimizing Springback in Roll Forming: An Adaptive Process Parameter Control Strategy

Springback is the most common quality defect in roll forming. It is difficult to control springback efficiently because the forming process is very complex and difficult to analyze. This paper proposes a data-driven springback control method that can adaptively adjust process parameters to minimize springback. Firstly, a digital twin model was established for efficiently collecting equipment data, formed part data, and remote equipment control. Then, based on gathered springback data considering different sheet widths, materials, uphill volumes, and roll gaps, a high-precision springback prediction model was constructed using the SAPSO-SVR algorithm. Lastly, building upon the prediction model, optimization models were developed using bat algorithm, enabling the system to automatically adjust process parameters based on optimization results when forming different sheets.The experimental results show that after optimization, the springback at the two corners of the hat-shaped part is reduced by 36.8% and 14.3% respectively. Experimental findings initially demonstrate the efficacy of this approach in controlling springback across sheets of diverse materials and varying widths. The proposed adaptive springback control method significantly enhances production efficiency and forming quality, driving the intelligent advancement of sheet metal roll forming technology.

Control Parameters Design of Spraying Robots Based on Dynamic Feedforward

The positioning and velocity accuracy of spraying robots determine the quality of the coating, and the influence of the robotic dynamic characteristics on control precision is significant. This paper presents a method of linearizing dynamic characteristics into feedforward coefficients and designs a dual-loop control system consisting of an inner velocity loop and an outer position loop. The system is divided into three sections: a cascaded section, a feedback section, and a feedforward section. The cascaded section eliminates the nonlinear characteristics of the system; the feedback section ensures the stability of the system; the feedforward section compensates for the internal errors of the system. The main innovation of this paper lies in proposing an offline parameter tuning method, which avoids online parameter adjustments and significantly enhances the real-time performance of the control system. Additionally, this method does not require specific physical information of the system, thus avoiding the cumbersome process of parameter adjustment. The experimental results demonstrate that when facing different high-speed trajectories, the proposed control system exhibits a significant improvement in control accuracy compared to other advanced control schemes.

Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

Atlantic Meridional Overturning Circulation (AMOC) plays a central role in long-term climate variations through its heat and freshwater transports, which can collapse under a rapid increase of greenhouse gas forcing in climate models. Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations. In this work, with a low-resolution earth system model, the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse. Through a new optimization strategy, the extra freshwater flux (FWF) parameter is determined to be the dominant one affecting the AMOC's variability. The traditional ensemble optimal interpolation (EnOI) data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO2 forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC. The results show that, under an abrupt 4×CO2 forcing in millennial simulations, the AMOC will first collapse and then re-establish by the default FWF parameter slowly. However, during the parameter adjustment process, the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC, according to their physical relationship with FWF on the interdecadal timescale.

Application of laser surface detection technology in blast furnace gas flow control and optimization

Abstract The gas flow distribution inside a blast furnace affects its smelting. Laser surface detection technology can control the gas flow distribution and promote the stable and smooth running of blast furnace by observing and adjusting the surface condition in the blast furnace in real-time. The laser surface inspection technology in blast furnace gas flow control and optimization was introduced, with Masteel 4,000 m3 blast furnace as an example. The results showed that the 30 W high-power laser had good penetration performance and could obtain clear scanning images of the material surface. In the process of strengthening smelting, the laser detection system determined that the central airflow of blast furnace was insufficient, resulting in a decrease in furnace condition stability, and promptly took measures to adjust the angle of the mineral coke fabric equipment and lifting line. After adjusting the material line, the central airflow was properly suppressed and stabilized, the angle of edge platform increased, the airflow developed properly, and two suitable gas flows were formed, which finally stabilized the furnace condition. Meanwhile, the small duct airflow other than the central airflow was found in time through the laser detection system, which provided conditions for operators to take measures to effectively eliminate the duct airflow, thereby avoiding big impacts on the blast furnace operation and technical and economic indexes. Therefore, the laser surface detection technology could effectively judge the gas flow distribution in the furnace, which contributed to timely adjustment of process parameters and optimization of blast furnace operation.

INTELLIGENT MONITORING SYSTEMS IN MANUFACTURING: CURRENT STATE AND FUTURE PERSPECTIVES

Intelligent Monitoring Systems (IMS) have emerged as indispensable tools in modern manufacturing, offering real-time insights into production processes, equipment performance, and quality control. This review provides an overview of the current state and future prospects of IMS in manufacturing environments. The current state of IMS in manufacturing involves the integration of advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics. These systems collect vast amounts of data from sensors, machines, and production lines, enabling real-time monitoring and analysis of various parameters. By employing AI and ML algorithms, IMS can identify patterns, predict anomalies, and optimize production processes, leading to increased efficiency, reduced downtime, and improved product quality. IMS play a crucial role in predictive maintenance, where they can anticipate equipment failures before they occur, thereby minimizing unplanned downtime and maintenance costs. Moreover, IMS facilitate condition-based monitoring, allowing manufacturers to monitor the health and performance of machinery in real-time and schedule maintenance activities accordingly, optimizing resource allocation and prolonging equipment lifespan. Furthermore, IMS contribute to quality control by continuously monitoring production processes and detecting deviations from desired specifications. By leveraging AI-driven algorithms, IMS can automatically adjust process parameters to maintain product quality standards and minimize defects, thereby enhancing overall product reliability and customer satisfaction. Looking ahead, the future perspectives of IMS in manufacturing are promising, with advancements in areas such as edge computing, robotics, and augmented reality poised to revolutionize manufacturing operations further. Edge computing enables data processing and analysis to occur closer to the data source, reducing latency and enhancing real-time decision-making capabilities. Robotics integration with IMS facilitates autonomous manufacturing processes, while augmented reality technologies provide intuitive interfaces for operators to interact with IMS data in real-time. IMS represent a transformative technology in manufacturing, offering unprecedented levels of visibility, control, and optimization. As technology continues to evolve, IMS are poised to play an increasingly vital role in shaping the future of manufacturing, driving efficiency, productivity, and innovation..&#x0D; Keywords: Monitoring, System, Intelligent, Manufacturing, Review, Perspectives.

Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis.

The initial melting quality of a high-speed laser cladding layer has an important impact on its post-treatment and practical application. In this study, based on the repair of hydraulic support columns of coal mining machines, the influence of high-speed laser cladding process parameters on the quality of Fe-Cr-Ni alloy coatings was investigated to realize the accurate prediction of coating quality. The Taguchi orthogonal method was used to design the L25(56) test. The prediction models of the relationship between the cladding process and the coating quality were established using the Random Forest (RF) and AdaBoost (Adaptive Boosting, AB) algorithms, respectively. Then, the prediction accuracy of the two models was compared, and the process parameter features were screened for importance evaluation. The results show that the AB prediction model is more accurate than the RF prediction model and more sensitive to abnormal data. The importance evaluation based on the AdaBoost model shows that the scanning speed has a great influence on the height and surface roughness of the coating. On the other hand, the overlap rate is the most important factor in controlling the dilution ratio and near-surface grain size of high-speed laser melting coatings. In addition, the micro-hardness of the coating and the thermal effect of the substrate can be effectively enhanced by adjusting the laser power and scanning speed. Finally, it was verified that the AB prediction model could accurately estimate the quality indexes of the coating with a prediction error less than 6%. The results show that it is feasible to predict the quality of high-speed laser cladding with the AB algorithm. It provides a basis for the adjustment of process parameters in the subsequent quality control process of cladding.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

High accuracy prediction of the post-combustion carbon capture process parameters using the Decision Forest approach

This paper investigates the relationships among the important process parameters that impact Post-combustion Carbon Capture (PCC) carbon dioxide (CO2) separation. A better understanding of the complex relationships among those parameters can support optimization and performance enhancement of the separation process. Being able to precisely predict the process parameters will enable the operator to determine the current state of the process, forecast any potential changes or events, and adjust process parameters to enhance the plant's performance. With the objective of studying the process parameters' correlations in the amine-based PCC process, we modeled the multi-year historical production data of the Clean Energy Technologies Research Institute (CETRi) (formerly known as the International Test Center for PCC or ITC) in Regina, Saskatchewan, Canada, using a Decision Forest approach. The model validation process revealed that the Decision Forest model produced higher predictive accuracy than previous efforts. The Decision Forest models we developed also represent knowledge about the importance of parameters involved in the capture process, and such knowledge is useful for further optimization of the capture process in the future.

Advancing scaffold porosity through a machine learning framework in extrusion based 3D bioprinting

Three Dimensional (3D) bioprinting holds great promise for tissue and organ regeneration due to its inherent capability to deposit biocompatible materials containing live cells in precise locations. Extrusion-based 3D bioprinting (EBP) method stands out for its ability to achieve a higher cell release rate, ensuring both external and internal scaffold structures. The systematic adjustment of key process parameters of EBP, including nozzle diameter, printing speed, print distance, extrusion pressure, material fraction, and viscosity allows for precise control over filament dimensions, ultimately shaping the desired scaffold porosity as per user specifications. However, managing these factors with all possible interactions simultaneously to achieve the desired filament width can be intricate and resource intensive. This study presents a novel framework designed to construct a predictive model for the filament width of 3D bioprinted scaffolds for various process parameters. A total of 157 experiments have been conducted under various combinations of process parameters and biomaterial’s weight fraction for this study purpose. A regression-based machine learning approach is employed to develop the predictive model utilizing Adj. R2, Mallow’s Cp, and Bayesian Information Criterion (BIC). Following model development, rigorous experimental validations are conducted to assess the accuracy and reliability of the model. Based on the cross-validation of randomly split test data, Adj. R2 model emerges as the highest performing machine learning model (Mean Squared Error, MSE = 0.0816) compared to Mallow’s Cp and BIC (MSE = 0.0841 and 0.0877, respectively) models. The comparative analysis results between the experimental and model’s data demonstrate that our predictive model achieves an accuracy of approximately 85% in filament width prediction. This framework presents a significant advancement in the precise control and optimization of 3D bioprinted scaffold fabrication, offering valuable insights for the advancement of tissue engineering and regenerative medicine applications.

Effect of microstructural inhomogeneity on mechanical anisotropy of Al–Cu–Li alloy plate

In order to reveal the relationship between microstructure inhomogeneity and mechanical anisotropy, the Al–Cu–Li alloy 100 mm thick plate for aerospace was evaluated. The grain structure, crystallographic texture, phase structure and fractography of different thickness positions and directions were observed by optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The in-plane and through-thickness anisotropy were characterized by tensile properties, fracture toughness and hardness tests. The results demonstrate a greater in-plane anisotropy in yield strength at the same thickness position of the plate. The Brass texture component increases successively at T, T/4, and T/2 positions, while the Cu texture component exhibits minimal variation. The in-plane anisotropy mainly arises from the Brass texture. Through-thickness anisotropy in the same direction is primarily influenced by the diameter, number density, and volume fraction of the T1 strengthening phase. The average diameter and number density of the primary strengthening phase T1 within grains are highest at the T/2 position and lowest at the T position. The plate contains unrecrystallized, elongated pancake-like grains along the rolling direction. Fracture toughness tests conducted in the L-T and T-L directions predominantly reveal dimple-type transgranular shear fractures, while the S-L direction exhibits intergranular fracture as the primary characteristic. The crack divider delamination toughening mechanism is the main contributor to higher fracture toughness values in the L-T and T-L directions compared to those in the S-L direction. The results reveal the corresponding relationship between the microstructure inhomogeneity and the mechanical anisotropy of Al–Li alloy ultra-thick plates, and provide ideas for the adjustment of process parameters during processing deformation and the regulation of microstructure during heat treatment of large-scale Al–Li alloy thick plates, which has engineering application reference value.