A method and system for detecting the z-direction performance of a steel plate for a hydraulic support structure

Abstract

This application relates to a method and system for testing the performance of steel plates used in hydraulic support structural components. The method includes: acquiring the design parameters of the target hydraulic support structural component and constructing a digital twin simulation model based on these parameters; performing finite element analysis and welding simulation using the digital twin simulation model to identify the key steel plates of the target structural component; marking the key steel plates using a traceable marking method; controlling a sampling device to extract base material samples and component feature samples according to the marked locations; controlling testing equipment to test the samples to obtain a first performance dataset and a second performance dataset; inputting the datasets into a trained performance mapping model to obtain a comprehensive performance score; and determining the final performance level as the evaluation result based on the z-axis performance of the steel plate according to a predefined score-level mapping relationship. This application can effectively screen steel plates with insufficient z-axis performance, avoid hydraulic support structural failure, and improve equipment safety.

Description

Technical Field

[0001] This application relates to the field of mining machinery manufacturing technology, and in particular to a method for testing the performance of steel plates used in hydraulic support structural components. Background Technology

[0002] Hydraulic supports are core equipment in fully mechanized coal mining, and their load-bearing capacity and structural safety directly determine the efficiency and safety of mining operations. As coal mining progresses towards deeper and higher mining depths, hydraulic support designs are trending towards larger sizes and higher load-bearing capacities. To meet the demands for high load-bearing capacity and lightweight construction, high-strength steel plates with a thickness ≥60mm and a strength grade ≥Q690D or higher are commonly used. During the rolling process, these thick plates are prone to uneven microstructure and inclusion segregation in the thickness direction (z-direction), resulting in significantly lower z-direction toughness, especially low-temperature impact toughness, compared to the plate surface direction. This poses a potential risk of brittle cracking under low-temperature impact loads.

[0003] Currently, in engineering, the Z-axis tensile test is mainly used to evaluate the resistance of steel plates to lamellar tearing based on the GB / T 5313 standard. However, this method is difficult to effectively reflect the toughness of materials under dynamic and low-temperature loads.

[0004] Therefore, there is an urgent need for a method and system for testing the z-axis performance of steel plates used in hydraulic support structures. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, this application provides a method and system for testing the z-axis performance of steel plates for hydraulic support structural components, which solves the technical problem that the prior art cannot effectively reflect the toughness performance of materials under dynamic and low-temperature loads.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the main technical solutions adopted in this application include:

[0009] In a first aspect, embodiments of this application provide a method for testing the z-axis performance of steel plates used in hydraulic support structures, including:

[0010] S10. The control center obtains the design parameters of the target structural component of the hydraulic support, constructs a digital twin simulation model of the target structural component based on the design parameters, and performs finite element analysis and welding simulation through the digital twin simulation model to determine the key steel plate of the target structural component.

[0011] S20. The control center obtains the physical coordinate information of the key steel plate of the target structural component and marks the key steel plate using a traceable marking method; according to the marking position, the control of the sampling device extracts the sample blank and obtains the base material sample and the component feature sample.

[0012] S30. The control center receives the instruction from the sampling device to complete the sampling, controls the testing equipment to perform a low-temperature impact test and a tensile test on the substrate sample in the z-direction to obtain a first performance dataset; performs a welding thermal simulation impact test and a thickness direction fatigue test on the structural component feature sample to obtain a second performance dataset.

[0013] S40. The control center inputs the first performance data and the second performance data into the trained performance mapping model to obtain a comprehensive performance score value; according to the predefined score-level mapping relationship, it determines the final performance level corresponding to the comprehensive performance score value, and outputs the final performance level as the evaluation result of the target structural component based on the z-direction performance of the steel plate.

[0014] Optionally, in some embodiments of this application, the design parameters include:

[0015] The three-dimensional model, material properties, load spectrum, and predetermined welding process parameters of the target structure of the hydraulic support.

[0016] Optionally, in some embodiments of this application, step S10 specifically includes:

[0017] S11. Based on the three-dimensional model, material properties and design load spectrum in the design parameters, perform static finite element analysis in the digital twin simulation model to obtain the z-direction stress distribution of the target structural component under rated load, and identify the region where the z-direction stress peak exceeds the first preset threshold.

[0018] S12. Based on the predetermined welding process parameters in the design parameters, perform a thermo-mechanical coupling simulation of the welding process in the digital twin simulation model to obtain the range of the welding heat-affected zone and the welding thermal cycle curve within the range, and identify the region where the equivalent heat output is greater than the second preset threshold.

[0019] S13. Perform spatial superposition analysis on the region where the z-direction stress peak exceeds the first preset threshold and the region where the equivalent heat output is greater than the second preset threshold to obtain the key steel plate of the hydraulic support structure to be tested.

[0020] Optionally, in some embodiments of this application, step S13 specifically includes:

[0021] The control center conducts a criticality level assessment of the regions identified after spatial overlay analysis, that is, it calculates the comprehensive risk index of the region by weighted summing of the peak stress in the z-direction and the equivalent heat output of each region.

[0022] The area where the comprehensive risk index is higher than the third preset threshold is identified as the key steel plate of the target structural component.

[0023] Optionally, in some embodiments of this application, in S20,

[0024] The substrate specimens include standard z-axis impact specimens and z-axis tensile specimens;

[0025] The component feature specimens include welding thermal simulation specimens and thickness direction fatigue specimens;

[0026] The welding thermal simulation specimen is a round bar specimen with a V-shaped notch in the middle section; the thickness fatigue specimen is a plate-shaped specimen with the thickness direction in the z direction and a round or elliptical hole at the end of the specimen.

[0027] Optionally, in some embodiments of this application, in step S30, the testing equipment includes: a low-temperature impact testing machine, a tensile testing machine, a welding thermal simulation testing machine, and a fatigue testing machine;

[0028] S30 specifically includes:

[0029] A low-temperature impact testing machine tests a standard z-axis impact specimen in the substrate sample at -20℃±2℃ to obtain the z-axis impact energy; a tensile testing machine tests a z-axis tensile specimen in the substrate sample until fracture and obtains the z-axis reduction of area; a welding thermal simulation testing machine performs thermal simulation treatment on the welding thermal simulation specimen according to the thermal cycle curve in the predetermined welding process parameters, and then performs an impact test at room temperature to obtain the welding thermal simulation impact energy; a fatigue testing machine tests the thickness direction fatigue specimen with an axial cyclic load of stress ratio R=0.1 and frequency 10Hz to obtain the logarithm of fatigue life.

[0030] The z-axis impact energy and z-axis reduction of area are the first performance dataset; the welding thermal simulation impact energy and fatigue life logarithm are the second performance dataset.

[0031] Optionally, in some embodiments of this application, in step S40, the performance mapping model is a regression model based on the random forest algorithm; the performance mapping model includes:

[0032] The input and normalization layer is used to receive and process the first performance dataset and the second performance dataset, and assemble the processed data into an initial feature vector.

[0033] The core feature engineering and random forest decision layer are used to generate derived features. The derived features are merged with the initial feature vector to generate an extended feature vector. The extended feature vector is then input into a random forest composed of decision trees. Each decision tree makes an independent splitting decision based on Gini impurity to generate a preliminary score prediction value.

[0034] The derived feature is the ratio of the z-axis reduction of area to the welding thermal simulation impact energy;

[0035] The integration and scoring output layer is used to integrate and calculate the preliminary score prediction values ​​to obtain the comprehensive performance score value.

[0036] Optionally, in some embodiments of this application, in step S20, identifying the key area using a traceable marking method includes:

[0037] The control center generates marking information containing the structural component serial number, key area ID, and timestamp, and drives the laser marking equipment to engrave a QR code carrying the marking information on the non-working surface of the steel plate at the physical coordinates of the key area; the QR code adopts the QR code format with an error correction level of not less than H.

[0038] Optionally, in some embodiments of this application, the thickness of the key steel plate is ≥60mm and the strength grade is ≥Q690D.

[0039] Secondly, embodiments of this application provide a steel plate z-axis performance testing system for hydraulic support structural components, including: a control center, a sampling device, testing equipment, and an industrial Internet of Things platform;

[0040] The control center is connected to the sampling device and testing equipment through an industrial Internet of Things platform.

[0041] The control center performs the steel plate z-axis performance testing method for hydraulic support structural components as described above.

[0042] (III) Beneficial Effects

[0043] The beneficial effects of this application are as follows: The method and system for testing the z-axis performance of steel plates for hydraulic support structural components of this application, by employing digital twin simulation and finite element analysis to accurately locate key steel plates, combining traceable markings to ensure sample representativeness, and conducting comprehensive evaluation based on multi-dimensional experimental data and performance mapping models, can achieve a shift from experience-based judgment to precise quantitative analysis compared to existing technologies. This improves the pertinence and comprehensiveness of the testing, and achieves the effect of scientifically predicting and evaluating the z-axis performance of key parts before and during the manufacturing process of structural components, thereby effectively ensuring the safety and reliability of hydraulic supports under complex working conditions. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of a method for testing the z-axis performance of a steel plate for a hydraulic support structure according to an embodiment of this application;

[0045] Figure 2 This is a flowchart illustrating the process of determining the key steel plate of a target structural component using a method for testing the z-axis performance of steel plates for hydraulic support structural components according to an embodiment of this application.

[0046] Figure 3 This is a schematic diagram of a steel plate z-axis performance testing system for hydraulic support structural members according to an embodiment of this application. Detailed Implementation

[0047] To better explain and facilitate understanding of this application, the following detailed description of the application is provided in conjunction with the accompanying drawings and specific embodiments.

[0048] Currently, the procurement and acceptance of steel plates for hydraulic supports mainly rely on GB / T 1591-2018 "Low Alloy High Strength Structural Steel" or GB / T 16270-2009 "Hot-Rolled Steel Plates and Strips for High Strength Structures" and the GB / T25974 series hydraulic support manufacturing standards. However, none of these standards explicitly specify the performance requirements of the steel plates in the z-direction (thickness direction). In actual production and application, thick-gauge high-strength steel plates are prone to uneven performance in the z-direction due to rolling processes, compositional segregation, and other factors. Specifically, this manifests as lamellar tearing after welding of hydraulic support structural components, or cracking failure in the thickness direction under long-term underground load-bearing conditions. This not only increases equipment maintenance costs and disrupts mining operations but may also lead to safety accidents.

[0049] Therefore, this application addresses the industry challenges of weld lamellar tearing and long-term load-bearing cracking in the thickness direction (z-direction) of steel plates used in existing hydraulic supports due to the lack of standards and uneven performance. It proposes an intelligent detection method based on precise positioning using digital twin simulation, combined with traceable sampling and multi-dimensional performance testing (z-direction low-temperature impact, tensile, weld thermal simulation, and fatigue testing), and performs comprehensive evaluation through a performance mapping model. This technical solution enables quantitative prediction and scientific evaluation of the z-direction performance of steel plates in key areas, thereby preventing failures at the source, significantly improving the structural safety and service reliability of hydraulic supports, and effectively reducing maintenance costs and safety risks caused by equipment damage.

[0050] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application can be understood more clearly and thoroughly, and that the scope of this application can be fully conveyed to those skilled in the art.

[0051] Example 1

[0052] Figure 1 This is a schematic flowchart illustrating a method for testing the z-axis performance of a steel plate for a hydraulic support structure according to an embodiment of this application. Figure 1 As shown, the detection method includes:

[0053] Step S10: The control center obtains the design parameters of the target structural component of the hydraulic support, constructs a digital twin simulation model of the target structural component based on the design parameters, and performs finite element analysis and welding simulation through the digital twin simulation model to determine the key steel plate of the target structural component.

[0054] The design parameters include the three-dimensional model of the target structure of the hydraulic support, material properties, load spectrum, and predetermined welding process parameters.

[0055] In step S10, the three-dimensional model of the target structure of the hydraulic support comes from the design (CAD) drawings of the target structure, which defines the precise geometry and spatial dimensions of the structure; the material properties are obtained from a material database or through standard tests based on the specific steel plate grade used, including basic mechanical properties such as elastic modulus, Poisson's ratio, and density, as well as thermophysical properties such as thermal conductivity and specific heat capacity; the design load spectrum is obtained through theoretical calculations, simulations, or comprehensive analysis of historical monitoring data based on the expected working cycle of the hydraulic support downhole, describing the time history of boundary conditions such as forces, moments, or displacements that the structure will experience during service; the predetermined welding industrial parameters are derived from the manufacturing process of the structure, specifically including process information such as welding method, heat input, and welding speed.

[0056] Based on the above design parameters, the process of constructing a digital twin simulation model includes:

[0057] The 3D model is imported into a dedicated simulation software platform, such as ANSYS or ABAQUS, to generate a finite element mesh suitable for computation. Next, material property data is assigned to the model's response components to ensure that the virtual material behavior matches the actual design. Then, the design load spectrum is used as loads and boundary conditions, accurately applied to the corresponding load-bearing locations and constraint points on the model. Finally, predetermined welding process parameters are set as input conditions for the welding process simulation module. Through this series of operations, a digital twin simulation model integrating geometry, materials, loads, and processes, reflecting the "physical state" and "manufacturing process" of the structural component, is constructed. This model provides a precise virtual experimental environment for subsequent static finite element analysis and thermo-mechanical coupling simulation of the welding process.

[0058] Furthermore, after the digital twin simulation model of the component is completed, finite element analysis and welding simulation are performed using the digital twin simulation model to determine the specific key steel plates of the target structural component, including (e.g.) Figure 2 (as shown)

[0059] Step S11: Based on the three-dimensional model, material properties and design load spectrum in the design parameters, perform static finite element analysis in the digital twin simulation model to obtain the z-direction stress distribution of the target structural component under rated load, and identify the region where the z-direction stress peak exceeds the first preset threshold.

[0060] In the specific implementation process, the rated load in the design load spectrum, i.e., the maximum or typical static load that the structural member must withstand under the design conditions, is used as a force or pressure boundary condition and applied to the actual bearing surface of the model. For example, it is applied to the contact surface between the top beam and the top slab. Simultaneously, according to the actual installation and constraint conditions of the structure, corresponding displacement constraints are applied at the base or hinge points of the model to simulate the actual support state. After completing the above settings, based on the theory of elasticity, the stress and strain field of the entire structure in equilibrium is solved, and the normal stress distribution cloud map of the entire structural member in the z-direction, i.e., the thickness direction, is extracted. This cloud map visually displays the magnitude and distribution of tensile or compressive stress at each point inside the structure in the z-direction. On this normal stress distribution cloud map, all regions where the peak stress in the z-direction exceeds a first preset threshold are identified.

[0061] The first preset threshold is generated by combining engineering safety specifications and historical experience data. For example, the first preset threshold can be set as a certain percentage of the material's yield strength, such as 60%-80%.

[0062] Step S12: Based on the predetermined welding process parameters in the design parameters, perform a thermo-mechanical coupling simulation of the welding process in the digital twin simulation model to obtain the range of the welding heat-affected zone and the welding thermal cycle curve within the range, and identify the area where the equivalent heat output is greater than the second preset threshold.

[0063] In this step, a welding heat source model is defined in the digital twin simulation model based on predetermined welding process parameters, and its start-up path is set. The simulation will calculate the thermal field and the resulting stress field that change over time during the welding process. By analyzing the transient temperature field, the specific spatial range of the welding heat-affected zone is determined. At the same time, the welding thermal cycle curves (curves showing temperature changes over time) of each characteristic point within this region can be extracted, thereby calculating the key indicator reflecting the intensity of the welding heat input—the equivalent heat input.

[0064] The second preset threshold is the standard for judging the high heat input risk zone. This threshold is mainly based on the material's sensitivity to welding heat input (such as the critical heat input to avoid excessive grain coarsening or the generation of unfavorable structures). It is usually determined by a combination of material weldability test data, relevant welding process qualification standards, or mature industry practice experience.

[0065] Step S13: Perform spatial superposition analysis on the region where the peak stress in the z-direction exceeds the first preset threshold and the region where the equivalent heat output is greater than the second preset threshold to obtain the key steel plate of the hydraulic support structure to be tested.

[0066] Specifically, step S13 includes:

[0067] The control center conducts a criticality level assessment of the regions identified after spatial overlay analysis, which involves weighted summation of the peak z-axis stress and equivalent heat output for each region to calculate the comprehensive risk index of the region.

[0068] The area where the comprehensive risk index is higher than the third preset threshold is identified as the key steel plate of the target structural component.

[0069] Optionally, the control center first imports the regions where the peak z-axis stress obtained in step S11 exceeds a first preset threshold and the regions where the equivalent heat output obtained in step S12 exceeds a second preset threshold into a unified three-dimensional coordinate space. Then, it performs Boolean operations and spatial matching on these two regions to identify areas that overlap or are closely adjacent in space. These overlapping regions are those that simultaneously bear high tensile stress in the thickness direction and have undergone high-intensity welding thermal cycles, and are considered the potential weak areas with the highest risk of lamellar tearing and fatigue cracking.

[0070] Furthermore, a criticality level assessment is performed on each identified overlapping region, specifically by calculating a comprehensive risk index for each region. This index is obtained through weighted summation. Finally, regions with a comprehensive risk index exceeding a third preset threshold are identified as "critical steel plates." These steel plates are the core objects requiring subsequent physical sampling and in-depth performance testing.

[0071] It should be noted that the "overlapping region" mentioned in step S13 refers to those local areas in three-dimensional space that are simultaneously identified as "high z-axis stress risk zone" and "high heat input risk zone". These regions are not a vague whole, but specific local areas with clear spatial locations and boundaries, divided based on finite element analysis meshes or simulation result data points.

[0072] The regions are divided as follows: In steps S11 and S12, the results of static analysis and welding simulation are attached to the digital twin model in the form of discretized data. Each mesh element or data point has its own spatial coordinates and corresponding performance parameters (such as stress value and heat input). These massive amounts of data are filtered using preset thresholds (first and second preset thresholds). All elements or nodes whose parameters exceed the threshold, along with their immediate neighbors, are automatically classified and marked as an independent risk region. Therefore, these risk regions are essentially clusters of points or small blocks composed of high-risk elements / nodes, and they are spatially discrete.

[0073] Spatial overlay analysis, based on this principle, precisely compares the spatial locations of two high-risk point clusters from different physical fields (mechanical and thermal fields). When a point cluster from the stress field and a point cluster from the heat input field intersect or are closely adjacent (spatial distance less than a set tolerance) in three-dimensional space, their intersection or union is defined as a coincident region. Therefore, the finally identified coincident region is a high-risk local volume with clear physical meaning (the combined effect of high stress and high heat input) and precise location (able to pinpoint a specific part of a specific steel plate). This provides a precise target for subsequent location of specific critical steel plates.

[0074] In specific implementation, the detection method of this application embodiment further includes:

[0075] Step S20: The control center obtains the physical coordinate information of the key steel plate of the target structural component and marks the key steel plate using a traceable marking method; according to the marking position, the sampling device is controlled to extract the sample blank and obtain the base material sample and component feature sample.

[0076] The use of traceable marking to identify key areas includes:

[0077] The control center generates marking information containing the structural component serial number, key area ID, and timestamp, and drives the laser marking equipment to engrave a QR code carrying the marking information on the non-working surface of the steel plate at the physical coordinates of the key area; the QR code adopts the QR code format with an error correction level of not less than H.

[0078] Permanent markings are made on the steel plate at corresponding physical locations using laser marking and traceable QR codes. This marking ensures a unique correspondence between the digital space and the physical object, enabling full traceability throughout the entire process.

[0079] Furthermore, the substrate specimen in step S20 includes a standard z-axis impact specimen and a z-axis tensile specimen.

[0080] Component characteristic specimens include welding thermal simulation specimens and thickness direction fatigue specimens;

[0081] Among them, the welding thermal simulation specimen is a round bar specimen with a V-shaped notch in the middle section; the thickness direction fatigue specimen is a plate specimen with the thickness direction in the z direction and a round or elliptical hole at the end of the specimen.

[0082] In the specific implementation process, the sampling device is a CNC cutting robot, and two types of four specific samples need to be prepared:

[0083] The base material specimens are used to evaluate the z-direction properties of the critical steel plate base material. The standard z-direction impact specimens are processed into V-shaped specimens according to standards (such as GB / T229), with the axis of the notch parallel to the rolled surface of the steel plate (i.e., perpendicular to the z-direction) to test the impact toughness in the thickness direction.

[0084] For example, from a key steel plate with specifications of 60mm×2000mm×6000mm, select a 500mm×500mm sample blank, avoiding surface oxide scale and scratches; then, using the first rolling surface of the steel plate as the reference surface (i.e. the surface that first contacts the roll during the rolling process to ensure the representativeness of the sample), process the impact energy sample along the z-direction (thickness direction) of the steel plate; the sample size is 10mm×10mm×55mm (cross-section 10mm×10mm, length 55mm, length direction parallel to z-direction), and a V-shaped notch is opened at the end of the sample away from the reference surface (complying with the requirements of GB / T 229 for impact sample notches), the notch depth is 2mm, and the bottom radius of the notch is 0.25mm.

[0085] The z-direction tensile specimen is processed into a round bar or plate-shaped tensile specimen, with the axis of its gauge length strictly parallel to the thickness direction (z-direction) to determine the z-direction tensile strength, yield strength and reduction of area of ​​the steel plate.

[0086] The component feature specimens are used to simulate the performance of actual structural components under working conditions. The welding thermal simulation specimen is a round bar specimen with a V-shaped notch precisely machined in its middle section. The specimen is then placed in a thermal simulation testing machine, and the welding thermal cycle curve obtained in step S12 is accurately reproduced by the program, so that the material in the notch area undergoes the same thermal process as the actual welding heat-affected zone, thereby creating a material sample with the same performance state as the actual component after welding.

[0087] Thickness-direction fatigue specimens are plate-shaped, with the thickness direction (z-direction) being the primary stress direction. Circular or elliptical holes are machined at one or both ends of the specimen to act as stress concentration sources, simulating geometric discontinuities such as holes and slots in actual structural components. This specimen is specifically designed for z-direction tension-tension or tension-compression fatigue tests to evaluate its resistance to lamellar tear fatigue propagation under alternating loads.

[0088] Through the above two types of four samples, a set of samples with clear physical meaning and testing purpose was systematically obtained from a key steel plate that was digitally positioned. These samples can comprehensively reflect the performance of the base material and simulate its performance status after manufacturing and service.

[0089] Step S30: The control center receives the sampling device's instruction to complete sampling, controls the testing equipment to perform a low-temperature impact test and a tensile test on the substrate sample in the z-direction, and obtains the first performance dataset; performs a welding thermal simulation impact test and a thickness direction fatigue test on the structural component feature sample, and obtains the second performance dataset.

[0090] In step S30, the testing equipment includes: a low-temperature impact testing machine, a tensile testing machine, a welding thermal simulation testing machine, and a fatigue testing machine;

[0091] Step S30 specifically includes:

[0092] A low-temperature impact testing machine tests standard z-axis impact specimens in a substrate sample at -20℃±2℃ to obtain z-axis impact energy; a tensile testing machine tests z-axis tensile specimens in the substrate sample until fracture and obtains z-axis reduction of area; a welding thermal simulation testing machine performs thermal simulation treatment on welding thermal simulation specimens according to the thermal cycle curve in the predetermined welding process parameters, and then conducts impact tests at room temperature to obtain welding thermal simulation impact energy; a fatigue testing machine tests thickness-direction fatigue specimens with an axial cyclic load of stress ratio R=0.1 and frequency 10Hz to obtain the logarithm of fatigue life.

[0093] The z-axis impact energy and z-axis reduction of area are from the first performance dataset; the welding thermal simulation impact energy and logarithm of fatigue life are from the second performance dataset.

[0094] Specifically, the control center first controls a low-temperature impact testing machine to test standard z-axis impact specimens in a harsh environment of -20℃±2℃ to obtain their z-axis impact energy. This data directly characterizes the thickness-direction toughness of the steel plate base material under low-temperature conditions. Subsequently, a tensile testing machine axially stretches the z-axis tensile specimen until fracture, automatically calculating the crucial z-axis reduction of area, which is the most core indicator for evaluating the material's resistance to lamellar tearing. The impact energy and reduction of area together constitute the first performance dataset, fully reflecting the z-axis mechanical properties of the steel plate in its original state. The process then shifts to testing under simulated actual working conditions: a welding thermal simulation testing machine performs precise heat treatment on the notch area of ​​the welding thermal simulation specimen based on a specific welding thermal cycle curve extracted from digital twin simulation to reproduce the microstructure of the actual welding heat-affected zone; subsequently, the specimen undergoes an impact test at room temperature, and the measured welding thermal simulation impact energy quantifies the degree to which the material retains toughness after the welding thermal process. Simultaneously, a fatigue testing machine was used to test thickness-direction fatigue specimens with perforations under axial cyclic loading at a stress ratio R=0.1 and a frequency of 10Hz. The number of failure cycles was recorded and converted into the logarithm of fatigue life, thereby evaluating the material's ability to resist fatigue crack propagation under alternating stress in the thickness direction. The simulated impact energy and the logarithm of fatigue life together constitute the second performance dataset, revealing the performance evolution of the steel plate after simulated manufacturing and service. At this point, all key data characterizing the z-axis performance of the steel plate from its "original state" to its "service state" have been accurately acquired through an automated testing process, laying the data foundation for the final comprehensive intelligent evaluation.

[0095] Furthermore, after acquiring the first and second performance datasets, these measured data are used as feedback and fed back into the digital twin simulation model. Specifically, the measured z-axis mechanical properties (such as yield strength and toughness) are used to calibrate the material constitutive relations in the model; the correlation between welding thermal simulation impact energy and thermal cycling data is used to fine-tune the welding heat source model parameters. Through this closed-loop "simulation-measurement-calibration" process, the prediction accuracy of the digital twin model continuously improves with the accumulation of production batches and experimental data. This enables more precise positioning of key steel plates for similar structural components, achieving a leap from "one-time simulation" to "adaptive learning simulation," significantly enhancing the reliability and predictive capability of the method.

[0096] Step S40: The control center inputs the first performance data and the second performance data into the trained performance mapping model to obtain the comprehensive performance score value; according to the predefined score-level mapping relationship, it determines the final performance level corresponding to the comprehensive performance score value, and outputs the final performance level as the evaluation result of the target structural component based on the z-direction performance of the steel plate.

[0097] In step S40, the performance mapping model is a regression model based on the random forest algorithm; the performance mapping model includes:

[0098] The input and normalization layer is used to receive and process the first performance dataset and the second performance dataset, and assemble the processed data into an initial feature vector.

[0099] The core feature engineering and random forest decision layer are used to generate derived features. The derived features are merged with the initial feature vector to generate an extended feature vector. The extended feature vector is then input into a random forest composed of decision trees. Each decision tree makes an independent splitting decision based on Gini impurity to generate a preliminary score prediction value.

[0100] The derived characteristic is the ratio of the z-axis reduction of area to the welding thermal simulation impact energy;

[0101] The integration and scoring output layer is used to integrate and calculate the preliminary score predictions to obtain the comprehensive performance score.

[0102] Optionally, in the specific implementation process, the steel plate z-axis performance testing method for hydraulic support structural components in this application embodiment has the following prerequisites: the thickness of the target key steel plate is ≥60mm and the strength grade is ≥Q690D. This prerequisite is based on profound engineering practice and failure analysis background, which makes this testing scheme highly targeted.

[0103] Specifically, when the steel plate thickness is ≥60mm, it falls into the category of thick and extra-thick plates. During the rolling process, the cooling rate difference between the core and the surface layer is significant, making it more prone to compositional segregation, uneven microstructure, and significant accumulation of residual stress in the thickness direction. These inherent defects make the performance inhomogeneity of thick plates prominent when subjected to z-direction loads, which is the main cause of weld lamellar tearing and fatigue cracking in the thickness direction. In contrast, the z-direction performance problem of thin plates is usually not prominent, and routine testing is sufficient. In terms of strength grade, Q690D and above belong to low-alloy high-strength steel and even ultra-high-strength steel. To achieve high strength, these steels usually have more alloying elements added and use specific heat treatment processes, which often sacrifice some plasticity and toughness while improving strength and increasing sensitivity to welding thermal cycles. Their weld heat-affected zone is more prone to the formation of hard and brittle martensitic structures, further deteriorating z-direction properties (especially toughness and crack resistance). For ordinary strength steels, their z-direction performance reserves are usually high, and the problem is not significant.

[0104] In summary, the combined effects of thickness and high strength significantly exacerbate the risk of Z-axis performance degradation. The inherent inhomogeneity caused by thickness, coupled with the inherent sensitivity and low plasticity of high-strength materials, makes these steel plates a potential weak point in heavy-duty welded structures such as hydraulic supports. Therefore, this application limits the testing to steel plates with a thickness ≥ 60 mm and a strength ≥ Q690D. A comprehensive approach, from digital twin simulation positioning to targeted sampling, and then to multi-dimensional performance testing and comprehensive evaluation, accurately diagnoses and prevents potential Z-axis failures in these high-end special steel plates under complex service conditions. This fills the gap in existing standards and practices, achieving a leap from "compliance acceptance" to "predictive assurance."

[0105] Furthermore, the method for testing the z-axis performance of steel plates for hydraulic support structural components in this embodiment also includes: embedding a full-process data storage and traceability chain based on blockchain technology, thereby constructing a tamper-proof and fully auditable trusted data closed loop.

[0106] Specifically, the control center generates a digital fingerprint (hash value) for each batch of data in real time at every critical stage—including but not limited to the digital twin model parameters and simulation results in step S10, the unique identifier of the key steel plate determined in step S13, the QR code information marked by laser in step S20 and its associated physical coordinates, the raw data generated by all test equipment in step S30, and the performance mapping model version and evaluation results in step S40. These processes are accompanied by an authoritative timestamp. These hash values ​​are not stored on a local server, but are encrypted, signed, and broadcast to multiple nodes in a network jointly maintained by equipment manufacturers, material suppliers, testing institutions, and end users for distributed notarization. Any access to or verification request for the notarized data on the chain must undergo strict access control verification and digital signature authentication. This technical architecture completely solves the drawbacks of traditional testing methods where paper or centralized electronic records are easily modified, lost, or subject to human intervention. Its core effect is the generation of a tamper-proof and traceable credit profile for each critical steel plate and its associated hydraulic support structural component, spanning the entire lifecycle from "virtual design to physical identification, sample extraction, laboratory testing, and intelligent evaluation." This technically ensures the ultimate authenticity and integrity of the testing data.

[0107] On the other hand, see Figure 3 An embodiment of this application provides a steel plate z-axis performance testing system for hydraulic support structural components, comprising: a control center, a sampling device, testing equipment, and an industrial Internet of Things platform;

[0108] The control center is connected to the sampling device and testing equipment through an industrial Internet of Things (IoT) platform.

[0109] The control center performs the z-axis performance testing method for steel plates used in hydraulic support structures as described above.

[0110] Furthermore, the system also includes laser marking equipment connected to the control center.

[0111] Specifically, this testing system achieves seamless integration and intelligent linkage of hardware and software through an industrial Internet of Things (IoT) platform. The control center, acting as the system's brain, not only runs digital twin simulation and intelligent decision-making algorithms, but also sends marking instructions containing three-dimensional coordinates to the laser marking equipment, path planning and action instructions to the CNC sampling device, and uniformly schedules testing equipment such as the low-temperature impact testing machine, tensile testing machine, welding thermal simulation testing machine, and multi-axis fatigue testing machine, automatically triggering test programs and collecting raw data. All hardware status and process data are fed back to the control center in real time through the IoT platform, forming an automated testing pipeline of "simulation-driven - precise execution - data closed loop," thereby transforming the method into a highly efficient, reliable, and repeatable physical industrial system.

[0112] The method for testing the z-axis performance of steel plates for hydraulic support structural components in this application embodiment accurately locates key steel plates by constructing a digital twin model, and obtains representative samples using traceable identification and sampling technology. Combined with multi-dimensional tests simulating actual working conditions (z-axis low-temperature impact, tensile, welding thermal simulation, and fatigue tests), the method finally uses a performance mapping model for intelligent comprehensive evaluation. This realizes the transformation of the z-axis performance of thick-gauge high-strength hydraulic support steel plates from "empirical judgment" to "quantitative prediction," thereby effectively preventing lamellar tearing and thickness-direction cracking failures from the design and manufacturing source, and significantly improving the safety, reliability, and service life of structural components.

[0113] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0114] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0115] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0116] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0117] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for testing the z-axis performance of steel plates used in hydraulic support structural components, characterized in that, include: S10. The control center obtains the design parameters of the target structural component of the hydraulic support, constructs a digital twin simulation model of the target structural component based on the design parameters, and performs finite element analysis and welding simulation through the digital twin simulation model to determine the key steel plate of the target structural component. S20. The control center obtains the physical coordinate information of the key steel plate of the target structural component and marks the key steel plate using a traceable marking method; according to the marking position, the control of the sampling device extracts the sample blank and obtains the base material sample and the component feature sample. S30. The control center receives the instruction from the sampling device to complete the sampling, controls the testing equipment to perform a low-temperature impact test and a tensile test on the substrate sample in the z-direction to obtain a first performance dataset; performs a welding thermal simulation impact test and a thickness direction fatigue test on the structural component feature sample to obtain a second performance dataset. S40. The control center inputs the first performance data and the second performance data into the trained performance mapping model to obtain a comprehensive performance score value; according to the predefined score-level mapping relationship, it determines the final performance level corresponding to the comprehensive performance score value, and outputs the final performance level as the evaluation result of the target structural component based on the z-direction performance of the steel plate.

2. The method for testing the z-axis performance of steel plates used in hydraulic support structures according to claim 1, characterized in that, The design parameters include: The three-dimensional model, material properties, load spectrum, and predetermined welding process parameters of the target structure of the hydraulic support.

3. The method for testing the z-axis performance of steel plates used in hydraulic support structures according to claim 2, characterized in that, S10 specifically includes: S11. Based on the three-dimensional model, material properties and design load spectrum in the design parameters, perform static finite element analysis in the digital twin simulation model to obtain the z-direction stress distribution of the target structural component under rated load, and identify the region where the z-direction stress peak exceeds the first preset threshold. S12. Based on the predetermined welding process parameters in the design parameters, perform a thermo-mechanical coupling simulation of the welding process in the digital twin simulation model to obtain the range of the welding heat-affected zone and the welding thermal cycle curve within the range, and identify the region where the equivalent heat output is greater than the second preset threshold. S13. Perform spatial superposition analysis on the region where the z-direction stress peak exceeds the first preset threshold and the region where the equivalent heat output is greater than the second preset threshold to obtain the key steel plate of the hydraulic support structure to be tested.

4. The method for testing the z-axis performance of steel plates used in hydraulic support structures according to claim 3, characterized in that, S13 specifically includes: The control center conducts a criticality level assessment of the regions identified after spatial overlay analysis, that is, it calculates the comprehensive risk index of the region by weighted summing of the peak stress in the z-direction and the equivalent heat output of each region. The area where the comprehensive risk index is higher than the third preset threshold is identified as the key steel plate of the target structural component.

5. The method for testing the z-axis performance of steel plates for hydraulic support structural components according to claim 1, characterized in that, In S20, The substrate specimens include standard z-axis impact specimens and z-axis tensile specimens; The component feature specimens include welding thermal simulation specimens and thickness direction fatigue specimens; The welding thermal simulation specimen is a round bar specimen with a V-shaped notch in the middle section; the thickness fatigue specimen is a plate-shaped specimen with the thickness direction in the z direction and a round or elliptical hole at the end of the specimen.

6. The method for testing the z-axis performance of steel plates for hydraulic support structural components according to claim 5, characterized in that, In S30, the testing equipment includes: a low-temperature impact testing machine, a tensile testing machine, a welding thermal simulation testing machine, and a fatigue testing machine; S30 specifically includes: A low-temperature impact testing machine tests a standard z-axis impact specimen in the substrate sample at -20℃±2℃ to obtain the z-axis impact energy; a tensile testing machine tests a z-axis tensile specimen in the substrate sample until fracture and obtains the z-axis reduction of area; a welding thermal simulation testing machine performs thermal simulation treatment on the welding thermal simulation specimen according to the thermal cycle curve in the predetermined welding process parameters, and then performs an impact test at room temperature to obtain the welding thermal simulation impact energy; a fatigue testing machine tests the thickness direction fatigue specimen with an axial cyclic load of stress ratio R=0.1 and frequency 10Hz to obtain the logarithm of fatigue life. The z-axis impact energy and z-axis reduction of area are the first performance dataset; the welding thermal simulation impact energy and fatigue life logarithm are the second performance dataset.

7. The method for testing the z-axis performance of steel plates used in hydraulic support structures according to claim 1, characterized in that, In S40, the performance mapping model is a regression model based on the random forest algorithm; The performance mapping model includes: The input and normalization layer is used to receive and process the first performance dataset and the second performance dataset, and assemble the processed data into an initial feature vector. The core feature engineering and random forest decision layer are used to generate derived features. The derived features are merged with the initial feature vector to generate an extended feature vector. The extended feature vector is then input into a random forest composed of decision trees. Each decision tree makes an independent splitting decision based on Gini impurity to generate a preliminary score prediction value. The derived feature is the ratio of the z-axis reduction of area to the welding thermal simulation impact energy; The integration and scoring output layer is used to integrate and calculate the preliminary score prediction values ​​to obtain the comprehensive performance score value.

8. The method for testing the z-axis performance of steel plates used in hydraulic support structures according to claim 1, characterized in that, In step S20, identifying the key area using a traceable marking method includes: The control center generates marking information containing the structural component serial number, key area ID, and timestamp, and drives the laser marking equipment to engrave a QR code carrying the marking information on the non-working surface of the steel plate at the physical coordinates of the key area; the QR code adopts the QR code format with an error correction level of not less than H.

9. The method for testing the z-axis performance of steel plates for hydraulic support structural components according to claim 3, characterized in that, The thickness of the key steel plate is ≥60mm, and the strength grade is ≥Q690D.

10. A system for testing the z-axis performance of steel plates used in hydraulic support structures, characterized in that, This includes: a control center, sampling devices, testing equipment, and an industrial IoT platform; The control center is connected to the sampling device and testing equipment through an industrial Internet of Things platform. The control center performs the z-axis performance testing method for steel plates used in hydraulic support structures as described in any one of claims 1 to 9.

Images