A performance optimization method and system for abrasive water jet cutting of ultra-hard rock

Abstract

The present invention provides a method and system for optimizing the performance of abrasive water jet cutting of ultra-hard rock. To solve the problems of unclear cutting parameter values and unclear importance of parameters affecting cutting performance in the installation of abrasive water jets on TBMs and in actual engineering applications, the present invention defines an evaluation method for the cutting performance of abrasive water jets and uses single-factor experiments to obtain the range of advantageous cutting parameters; conducts multi-factor experiments through the response surface experimental design method, and determines the importance ranking of each parameter by variance analysis; adopts the non-linear multiple regression analysis method to obtain the prediction model for the cutting depth of abrasive water jets; for the target cutting seam depth set during construction, the advantageous cutting parameter combination is inversely deduced. The present invention can determine the importance of abrasive water jet parameters and obtain advantageous cutting parameters, guiding the design of combined cutterheads and actual engineering construction.

Description

Technical Field

[0001] The invention belongs to the technical field of tunnel construction, and relates to a method and system for optimizing the performance of abrasive water jet cutting ultra-hard rock. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] With the vigorous development of infrastructure, tunnel construction has been widely carried out and vigorously developed. As a highly automated tunneling machine, the (full-face) Tunnel Boring Machine (TBM) has the advantages of fast tunneling efficiency, high safety, small construction disturbance, and good tunnel quality, and is widely used in the construction of tunnels (caverns) such as transportation and water conservancy. However, when the TBM encounters conditions of large buried depth, high confining pressure, and high-abrasion hard rock and extremely hard rock, the rock-breaking ability of the cutters is seriously insufficient, resulting in a series of problems such as low penetration rate, increased tool wear, cutter head cracking and wear, abnormal wear and fracture of the main bearing, and a high proportion of cutter-changing time, which seriously restricts the tunneling efficiency of the TBM and increases the construction cost.

[0004] Therefore, many new rock-breaking methods have emerged. The high-pressure water jet technology is one of them. The abrasive water jet adds hard abrasives to the high-pressure water jet, and its cutting ability is dozens of times that of the pure water jet. It has been maturely applied in the processing of high-strength alloy materials, ceramics and other materials, and is considered a rock-breaking method with great potential.

[0005] According to the inventor's understanding, in the application process of the abrasive water jet-cutter combined tunneling technology in actual projects, the following problems need to be solved urgently:

[0006] (1) There are many parameters that affect the cutting performance of the abrasive water jet. At the same time, the importance of each factor on the cutting performance of the abrasive water jet is different. Which factors should be given priority in the design and construction of the abrasive water jet TBM?

[0007] (2) When the values of the parameters that affect the cutting performance of the abrasive water jet are different, the cutting effect on the rock is different. How to define the performance of the abrasive water jet cutting ultra-hard rock and determine the value range of the dominant rock-breaking parameters to make the rock-breaking effect of the abrasive water jet assisting the cutter the best. Summary of the Invention

[0008] To solve the above problems, the present invention proposes a method and system for optimizing the performance of abrasive water jet cutting of ultra-hard rock. The present invention provides a new idea to clarify the importance of various factors affecting the cutting performance of abrasive water jet; for the various influencing parameters affecting the performance of abrasive water jet cutting of ultra-hard rock, a method for optimizing the selection of superior parameters is defined, and the range of superior parameters is selected, which helps to improve the performance of abrasive water jet cutting of ultra-hard rock and realize the efficient rock breaking of abrasive water jet assisted TBM cutters.

[0009] According to some embodiments, the present invention adopts the following technical solutions:

[0010] A method for optimizing the performance of abrasive water jet cutting of ultra-hard rock, comprising the following steps:

[0011] Taking the depth of abrasive water jet cutting of ultra-hard rock as the target, conduct single-factor abrasive water jet cutting experiments on ultra-hard rock. According to the experimental results, analyze the key parameters that significantly affect the cutting performance when abrasive water jet cuts ultra-hard rock and the influence law of each key parameter on the cutting performance of abrasive water jet, and determine the superior range of each key parameter;

[0012] Taking each key parameter as a variable and the depth of abrasive water jet cutting of ultra-hard rock as the response value, conduct abrasive water jet cutting experiments on ultra-hard rock to determine the slot depth generated by each key parameter when cutting ultra-hard rock, and sort the influence degrees of each key parameter on the slot depth;

[0013] Establish a prediction model for the cutting depth of rock abrasive water jet. Based on the experimental results, use the non-linear multiple regression analysis method to determine the coefficient values of the prediction model for the cutting depth of rock abrasive water jet.

[0014] According to the target cutting depth of ultra-hard rock abrasive water jet, inversely calculate the operating parameters of the abrasive water jet and optimize the key parameters.

[0015] As an alternative implementation, the key parameters include abrasive flow rate, transverse movement speed, target distance, nozzle diameter, number of cutting passes, and pump pressure.

[0016] As an alternative implementation, the superior range of the key parameters includes an abrasive flow rate of [1150, 1250] g / min, a transverse movement speed of (0, 15] m / min, a target distance between [15, 40] mm, a nozzle diameter of [0.33, 0.5] mm, the number of cutting passes of [1, 3] times, and a pump pressure of [280, 350] MPa.

[0017] As a further defined embodiment, the cuttability index and the energy consumption index are defined. The cuttability index is the ratio of the cut depth of the abrasive water jet on the rock to the corresponding parameter variables, and the energy consumption index is the ratio of the cutting energy consumption of the abrasive water jet on the rock to the cut depth. The two indexes are used to analyze the key parameters that significantly affect the cutting performance when the abrasive water jet cuts ultra-hard rock and the influence law of each key parameter on the cutting performance of the abrasive water jet.

[0018] As a further defined embodiment, to determine the cut depth generated by each key parameter when cutting ultra-hard rock and sort the influence degrees of each key parameter on the cut depth, the specific process includes: conducting an experiment on cutting ultra-hard rock with an abrasive water jet, measuring the cut depth generated by cutting ultra-hard rock with each group of parameters, and performing a variance analysis on each parameter affecting the cut performance of the abrasive water jet. The analysis result is: the importance ranking of the process parameters of cutting ultra-hard rock with an abrasive water jet on the cutting depth is traverse speed > number of cutting passes > nozzle diameter > pump pressure > target distance.

[0019] As an alternative embodiment, the rock abrasive water jet cut depth prediction model is:

[0020]

[0021] In the formula, H is the cut depth, Vs is the traverse speed, P is the pump pressure, h is the target distance, d is the nozzle diameter, T is the number of cutting passes, and λ0, λ1, λ2, λ3, λ4, λ5 represent the influence between each parameter and the cut depth, which is obtained by substituting the experimental result data into the model for multiple iterations and calculating.

[0022] As an alternative embodiment, the rock abrasive water jet cut depth prediction model takes into account the inherent properties of the rock.

[0023] As an alternative embodiment, the rock abrasive water jet cut depth prediction model takes into account the rock material density, and the specific model is:

[0024]

[0025] In the formula, H is the cut depth, Vs is the traverse speed, P is the pump pressure, h is the target distance, d is the nozzle diameter, T is the number of cutting passes, ρ is the rock density (g / cm 3 ), and λ0, λ1, λ2, λ3, λ4, λ5, λ6 represent the influence between each parameter and the cut depth, which is obtained by substituting the experimental result data into the model for multiple iterations and calculating.

[0026] As an alternative embodiment, it further includes a verification step. After calculating the final prediction model of the cutting depth of the rock abrasive water jet, compare the predicted cutting seam depth with the actual cutting seam depth. If the average error between the two is less than the allowable value, the model is correct; otherwise, use more test data to solve the model.

[0027] A performance optimization system for abrasive water jet cutting of ultra-hard rock, comprising:

[0028] A single-factor analysis module, configured to carry out single-factor abrasive water jet cutting experiments on ultra-hard rock with the cutting depth of the abrasive water jet on ultra-hard rock as the target. According to the experimental results, analyze the key parameters that significantly affect the cutting performance when the abrasive water jet cuts ultra-hard rock and the influence law of each key parameter on the cutting performance of the abrasive water jet, and determine the advantageous range of each key parameter;

[0029] A multi-factor analysis module, configured to carry out abrasive water jet cutting experiments on ultra-hard rock with each key parameter as a variable and the cutting depth of the abrasive water jet on ultra-hard rock as the response value, determine the cutting seam depth generated by each key parameter when cutting ultra-hard rock, and sort the influence degrees of each key parameter on the cutting seam depth;

[0030] A prediction model construction module, configured to establish a prediction model for the cutting depth of the rock abrasive water jet, and determine the coefficient value of the prediction model for the cutting depth of the rock abrasive water jet by using the non-linear multiple regression analysis method based on the experimental results;

[0031] A parameter optimization module, configured to inversely calculate the operating parameters of the abrasive water jet according to the target cutting depth of the ultra-hard rock abrasive water jet and optimize the key parameters.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] Through the single-factor experimental scheme of abrasive water jet cutting of ultra-hard rock, analyze the relationship between the parameters affecting the cutting performance and the cutting seam depth, clarify the advantageous range of each cutting parameter, provide a recommended range of cutting parameters for the application of the abrasive water jet in engineering, and guide the actual construction.

[0034] Based on the response surface method, conduct multi-parameter experimental design, obtain the experimental results and carry out variance analysis, obtain the sensitivity ranking of each parameter, and clarify the importance of the parameters affecting the cutting performance of the abrasive water jet during engineering construction; when designing the cutter head, the design of the most critical parameter can be preferably considered according to the parameter importance to give full play to the maximum cutting performance of the water jet-mechanical combined cutter head.

[0035] A prediction model for the cutting seam depth of abrasive water jet cutting of ultra-hard rock is established. During the TBM tunneling construction process, the optimal parameter combination can be selected according to the predetermined cutting seam depth to achieve the optimal combined tunneling effect. Description of the Drawings

[0036] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments of the invention and their descriptions are used to explain the invention and do not unduly limit the invention.

[0037] Figure 1 is a flowchart of a method for optimizing the performance of abrasive water jet cutting of ultra-hard rock in at least one embodiment of the present invention;

[0038] Figure 2 is a graph showing the relationship between the pump pressure and the abrasive water jet cutting performance in at least one embodiment of the present invention;

[0039] Figure 3 is a comparison graph of the actual result and the predicted result of the cutting depth of the abrasive water jet in at least one embodiment of the present invention;

[0040] Figure 4 is a graph showing the relationship between the rock material properties and the cutting slot depth of the abrasive water jet in at least one embodiment of the present invention;

[0041] Figure 5 is a comparison graph of the actual result and the predicted result of the cutting depth of the abrasive water jet considering the rock density in at least one embodiment of the present invention. Detailed implementation manners

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] Embodiment 1:

[0046] A method for optimizing the performance of abrasive water jet cutting of ultra-hard rock, as Figure 1 shown, includes the following steps:

[0047] Step 1: Select multiple groups of process parameters that have a greater impact on the cutting depth of abrasive water jet cutting of ultra-hard rock as variables, conduct single-factor abrasive water jet cutting experiments on ultra-hard rock, define the performance indicators of abrasive water jet cutting of ultra-hard rock, and based on the experimental results and engineering construction requirements, deeply analyze the key parameters that significantly affect the cutting performance during abrasive water jet cutting of ultra-hard rock and the influence laws of each parameter on the cutting performance of abrasive water jet, and select the range of each advantageous parameter.

[0048] Step 2: Based on the range of each advantageous parameter determined in Step 1, with the help of the response surface experimental design method, design an experimental plan with each influencing parameter as a variable and the cutting depth of abrasive water jet cutting of ultra-hard rock as the response value, conduct abrasive water jet cutting experiments on ultra-hard rock, and measure and obtain the cutting slot depth generated by cutting ultra-hard rock with each group of parameters in the experimental plan.

[0049] Step 3: Conduct variance analysis on each parameter affecting the cutting slot performance of abrasive water jet to clarify the sensitivity of each influencing parameter to the cutting slot performance of abrasive water jet.

[0050] Step 4: Establish a cutting depth prediction model for abrasive water jet. Using the nonlinear multiple regression analysis method, conduct regression analysis on the cutting depth prediction model. Substitute the experimental data in Step 2 into the model for multiple iterative processing to obtain the cutting depth prediction model of rock abrasive water jet.

[0051] Step 5: According to the target cutting depth of ultra-hard rock abrasive water jet, the operating parameters of the abrasive water jet can be inversely calculated to optimize the cutting parameters.

[0052] The following is a detailed description of the specific implementation process:

[0053] First, select five rock specimens with different strengths. The physical and mechanical property parameters of the specimens are shown in Table 1. Select the transverse movement speed Vs, pump pressure P, target distance h, nozzle diameter d, cutting times T, and abrasive flow rate m a as variables to conduct single-factor cutting experiments, and the values of each factor are shown in Table 2.

[0054] The testing tools are vernier calipers, testing gaskets, and steel needles. After the abrasive water jet cuts the ultra-hard rock specimen, place the testing gasket on the surface of the rock sample, insert the steel needle into the cutting slot formed by the water jet cutting, mark the corresponding position of the testing gasket on the steel needle, and use a vernier caliper to measure the distance between the tip of the steel needle and the marked position, which is recorded as the cutting slot depth of the water jet in this experiment.

[0055] To ensure the accuracy of the experimental data of each sample, measure the cutting slot depth at 3 equally spaced points of the abrasive water jet cutting slot, and then take the average value of the three cutting slot depths as the final cutting slot depth of the experimental sample.

[0056] Table 1

[0057]

[0058] Table 2

[0059]

[0060] The performance of abrasive water jet cutting ultra-hard rock is selected by the cutability index and energy consumption index. The cutability index represents the cut slot depth corresponding to the unit variable, and the energy consumption index is the energy consumed by the abrasive water jet to cut the unit depth. The forms of the cutability index and energy consumption index are as follows:

[0061]

[0062]

[0063] In the formula, CI P is the cutability index of pump pressure, CI vs is the cutability index of traverse speed, CI h is the cutability index of target distance, CI T is the cutability index of cutting times, CI d is the cutability index of nozzle diameter, CI ma is the cutability index of abrasive flow rate, EI is the energy consumption index of cutting depth, E is the consumed energy, v 水 is the flow velocity of abrasive water jet, v s is the traverse speed, P is the pump pressure, T is the cutting times, d is the nozzle diameter, R is the nozzle radius, L 位移 is the cutting distance, H is the cut slot depth, h is the target distance.

[0064] Substitute the cut slot depth measured after the experiment into the expressions (1) and (2) of the cutability index and energy consumption index to obtain the cutting performance of each experimental sample. Based on the results of the cutability index and energy consumption index obtained from the single-factor experiment, analyze the influence law of each process parameter on the cutability index and energy consumption index. Combining with the actual construction environment and construction requirements of TBM, the advantageous range of each factor is determined as follows: the pump pressure P is between 280 and 350 MPa, the traverse speed Vs is within the range of 15 m / min, the cutting target distance h is between 15 and 40 mm, the abrasive flow rate m a is 1150 - 1250 g / min, the nozzle diameter d is between 0.33 and 0.5 mm, and the cutting times n is less than 1 - 3 times.

[0065] Taking the pump pressure as an example below, introduce the process of carrying out the single-factor experiment and selecting the advantageous parameter range of the pump pressure:

[0066] Taking the pump pressure P as the independent variable, with 80 MPa as the minimum value, 380 MPa as the maximum value, and 80 MPa as the gradient, the target distance h = 20 mm, the traverse speed Vs = 1.2 m / min, the cutting times n = 1 time, and the abrasive flow rate ma = 27 g / s, nozzle diameter d = 0.33 mm, cutting distance L 位移 is 0.2 m, and abrasive water jet cutting of ultra-hard rock experiments are carried out to record the cut depth of each experiment.

[0067] Substitute the cut depth measured after the experiment into the pump pressure cuttability index CI P Expression:

[0068]

[0069] Calculate the pump pressure cuttability index value of each experiment;

[0070] Substitute the experimental data of each experiment into the cut depth energy consumption index expression (2) to calculate the cut depth energy consumption index value of each experiment;

[0071] Taking the pump pressure as the abscissa and the cut depth, pump pressure cuttability index and cut depth energy consumption index as the ordinates respectively, the performance curves of abrasive water jet cutting of ultra-hard rock under different pump pressure parameters are obtained, as Figure 2 shown, Figure 2 (a)-(c) of which are the relationships between pump pressure and cut depth, pump pressure cuttability index and energy consumption index respectively.

[0072] Analysis Figure 2 From the variation law of pump pressure and cut depth performance, for the five kinds of rocks, with the increase of pump pressure, both the cut depth and the energy consumption index increase in the form of linear functions, and the cuttability index first increases and then decreases in the form of a power function. Within a certain pump pressure range, with the increase of pump pressure, the cuttability index first increases rapidly and then slowly; when exceeding a certain pump pressure, the cuttability index begins to show a downward trend, so there is a critical pump pressure corresponding to the turning point of the cuttability index increasing speed from fast to slow. The critical pressures of the five kinds of rocks are approximately between 280 - 350 MPa; above the critical pump pressure, even if the pump pressure increases, the cuttability index increases not significantly, or even shows a downward trend; at the same time, the increase of pump pressure will cause excessive energy consumption and higher requirements for mechanical equipment. The advantageous range of pump pressure is determined to be between 280 - 350 MPa.

[0073] Based on the cutting effect of single-factor tests, the better ranges of each process parameter are determined. It is found that the traverse speed, pump pressure, target distance, nozzle diameter, and cutting times are the key process parameters for abrasive water jet cutting of ultra-hard rock. Taking each process parameter as a variable and the cut depth as the response value, the response surface design method - Box-Behnken experimental design method is used to design the experimental scheme, and the five-factor three-level abrasive water jet cutting experiment is carried out. The corresponding relationship between the response surface experimental parameters and each parameter factor level is shown in Table 3.

[0074] Table 3

[0075]

[0076] According to the response surface method, 46 sets of cutting tests are required for each type of rock, and a total of 230 sets of cutting tests are carried out for five types of rocks. After each cutting experiment, the water jet cutting slot depth of the experimental sample is measured. The multi-factor experimental scheme and the corresponding cutting slot depth values designed by the response surface method are shown in Table 4.

[0077] Table 4

[0078]

[0079]

[0080] Based on the results of the multi-factor abrasive water jet cutting experiment, the experimental data in Table 4 are subjected to variance analysis, and the analysis results are shown in Table 5.

[0081] Table 5

[0082]

[0083] The magnitude of the F value in the variance analysis results can characterize the sensitivity of each parameter to the cutting performance. The larger the parameter F value, the more sensitive the parameter is to the cutting performance. From the magnitude of the F value in the variance analysis results in Table 5, it can be seen that the F values from large to small are traverse speed, cutting times, nozzle diameter, pump pressure, and target distance. Finally, the importance order of the five process parameters affecting the cutting slot depth is determined as traverse speed, cutting times, nozzle diameter, pump pressure, and target distance.

[0084] Set the form of the abrasive water jet cutting depth prediction model as:

[0085]

[0086] In Equation (4), H is the cutting slot depth, Vs is the traverse speed, P is the pump pressure, h is the target distance, d is the nozzle diameter, T is the cutting times, and λ0, λ1, λ2, λ3, λ4, λ5 represent the influence between each parameter and the cutting slot depth.

[0087] The nonlinear multiple regression analysis method is selected to perform regression analysis on the cutting depth prediction model. The test data in Table 4 of the multi-factor experimental results are substituted into the model expression (4) for multiple iterative processing, and the specific abrasive water jet cutting depth prediction models corresponding to the five types of rocks are shown in Equation (5):

[0088]

[0089] To verify the effectiveness of the cutting depth prediction model for each type of rock, the established prediction model formula (5) was used to solve the cutting depth under the corresponding cutting parameters and compare it with the actual cutting depth in the single-factor experiment. The average errors of Rocks 1-5 were 3.7%, 2.8%, 4.4%, 1.8%, and 1.0% respectively. The results are as Figure 3 shown, which verifies the effectiveness of the five rock cutting depth prediction models established based on abrasive water jet cutting parameters.

[0090] To improve the universality of the abrasive water jet cutting depth prediction model, the correlations between six material properties of rock materials, namely density, microhardness, compressive strength, tensile strength, elastic modulus, and wave velocity, and the cutting depth were considered. The maximum cutting depth under the same constant operating variables in Table 4 was selected, and the degree of linear correlation between the maximum cutting depth and each rock property was analyzed, as Figure 4 shown. The results show that rock density has a significant effect on the cutting depth, and microhardness, elastic modulus, and wave velocity have a moderate correlation with the cutting depth, while the correlations between compressive strength and tensile strength and the cutting depth are poor. Among these six rock material properties, rock density has the strongest correlation with the cutting depth and is the easiest to obtain in engineering construction. Therefore, rock density is selected as the characterization of its inherent property.

[0091] Considering the inherent property of rock material density, a cutting depth prediction model was established, and the model expression is shown in Equation (6):

[0092] H = 6845.215ρ -3.328 Vs -0.717 h -0.182 P 0.749 d 0.931 T 0.737 (6)

[0093] In the formula, H is the cutting depth (mm), ρ is the rock density (g / cm 3 ), Vs is the traverse speed (m / s), P is the pump pressure (MPa), h is the target distance (m), d is the nozzle diameter (m), and T is the number of cutting passes.

[0094] In the actual engineering of abrasive water jet-TBM cutter combined rock breaking, for the set target cutting depth, a parameter combination table with excellent cutting performance can be obtained through the abrasive water jet cutting depth prediction model formula (6). Under the parameter combination derived from the cutting depth prediction model, the performance of abrasive water jet cutting ultra-hard rock can be fully exerted, while taking into account the requirements of actual engineering construction, guiding the efficient construction of abrasive water jet-TBM combination.

[0095] To verify the applicability of the model, the cutting depths of five rock materials were predicted and compared with the actual experimental results. The average error of the cutting depths of the five rocks was approximately 3.8%, asFigure 5 As shown, the applicability of the model was verified.

[0096] It should be noted that the values of the various parameters in the above embodiments are all exemplary. In other embodiments, adjustments or changes can be made according to other situations such as the rock conditions. Details are not elaborated here.

[0097] In summary, the present invention can solve the problems of unclear cutting parameter values and unclear importance of parameters affecting cutting performance that occur in the mounting of abrasive water jets on TBMs and actual engineering applications, can determine the importance of abrasive water jet parameters and obtain optimal cutting parameters, and guide the design of the combined cutter head and actual engineering construction.

[0098] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, they do not limit the protection scope of the present invention. Those skilled in the art should understand that, based on the technical solutions of the present invention, various modifications or deformations that can be made without creative efforts by those skilled in the art are still within the protection scope of the present invention.

Claims

1. A method for optimizing the performance of abrasive water jet cutting of ultra-hard rock, characterized in that, It includes the following steps: Taking the depth of abrasive water jet cutting ultra-hard rock as the target, conduct single-factor abrasive water jet cutting ultra-hard rock experiments. According to the experimental results, analyze the key parameters that significantly affect the cutting performance when abrasive water jet cuts ultra-hard rock and the influence law of each key parameter on the cutting performance of abrasive water jet, and determine the advantageous range of each key parameter; Taking each key parameter as a variable and the depth of abrasive water jet cutting ultra-hard rock as the response value, conduct abrasive water jet cutting ultra-hard rock experiments, determine the slot depth generated by each key parameter when cutting ultra-hard rock, and sort the influence degrees of each key parameter on the slot depth; Establish a prediction model for the cutting depth of rock abrasive water jet. Based on the experimental results, use the non-linear multiple regression analysis method to determine the coefficient values of the prediction model for the cutting depth of rock abrasive water jet; According to the target cutting depth of ultra-hard rock abrasive water jet, inversely calculate the operating parameters of the abrasive water jet and optimize the key parameters.

2. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1, characterized in that, The key parameters include abrasive flow rate, transverse movement speed, target distance, nozzle diameter, number of cutting passes, and pump pressure.

3. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1 or 2, characterized in that, The advantageous range of the key parameters includes that the abrasive flow rate is [1150, 1250] g / min, the transverse movement speed is (0, 15] m / min, the target distance is between [15, 40] mm, the nozzle diameter is [0.33, 0.5] mm, the number of cutting passes is [1, 3] times, and the pump pressure is [280, 350] MPa.

4. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1 or 2, characterized in that, Define the cutability index and the energy consumption index. The cutability index is the ratio of the slot depth of the abrasive water jet on the rock to the corresponding parameter variable, and the energy consumption index is the ratio of the cutting energy consumption of the abrasive water jet on the rock to the slot depth; use the two indices to analyze the key parameters that significantly affect the cutting performance when abrasive water jet cuts ultra-hard rock and the influence law of each key parameter on the cutting performance of abrasive water jet.

5. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1, characterized in that, The specific process of determining the slot depth generated by each key parameter when cutting ultra-hard rock and sorting the influence degrees of each key parameter on the slot depth includes: conducting abrasive water jet cutting ultra-hard rock experiments, measuring the slot depth generated by each group of parameters when cutting ultra-hard rock, and performing variance analysis on each parameter affecting the slotting performance of the abrasive water jet. The analysis result is: the importance ranking of the process parameters of abrasive water jet cutting ultra-hard rock on the cutting depth is transverse movement speed > number of cutting passes > nozzle diameter > pump pressure > target distance.

6. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1, characterized in that, The prediction model for the cutting depth of rock abrasive water jet is: In the formula, H is the slot depth, Vs is the transverse movement speed, P is the pump pressure, h is the target distance, d is the nozzle diameter, T is the number of cutting passes, and λ0, λ1, λ2, λ3, λ4, λ5 represent the influence between each parameter and the slot depth, which is calculated by substituting the experimental result data into the model for multiple iterations.

7. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1, characterized in that, The prediction model for the cutting depth of rock abrasive water jet considers the inherent properties of the rock. The rock density has a significant impact on the slot depth, and the microhardness, elastic modulus, and wave velocity have a medium correlation with the slot depth, while the compressive strength and tensile strength have a poor correlation with the slot depth.

8. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1 or 7, characterized in that, The prediction model for the cutting depth of rock abrasive water jet considers the rock material density. The specific model is: Wherein, H is the cutting slot depth, Vs is the transverse movement speed, P is the pump pressure, h is the target distance, d is the nozzle diameter, T is the cutting times, ρ is the rock density (g / cm 3 ), λ0, λ1, λ2, λ3, λ4, λ5, λ6 represent the influence between each parameter and the cutting slot depth. Substitute the experimental result data into the model for multiple iterations and calculate to obtain.

9. The method for optimizing the performance of abrasive water jet cutting of ultra-hard rock according to claim 1, characterized in that, It also includes a verification step. After calculating the final prediction model of the cutting depth of the rock abrasive water jet, compare the predicted cutting slot depth with the actual cutting slot depth. If the average error between the two is less than the allowable value, the model is correct; otherwise, use more experimental data to solve the model.

10. An abrasive water jet cutting ultra-hard rock performance optimization system, characterized in that it includes: The single-factor analysis module is configured to conduct single-factor abrasive water jet cutting experiments on ultra-hard rocks with the cutting depth of the abrasive water jet as the target. According to the experimental results, analyze the key parameters that significantly affect the cutting performance when the abrasive water jet cuts ultra-hard rocks and the influence law of each key parameter on the cutting performance of the abrasive water jet, and determine the advantageous range of each key parameter. The multi-factor analysis module is configured to conduct abrasive water jet cutting experiments on ultra-hard rocks with each key parameter as a variable and the cutting depth of the abrasive water jet as the response value, determine the cutting slot depth generated by each key parameter for cutting ultra-hard rocks, and sort the influence degrees of each key parameter on the cutting slot depth. The prediction model construction module is configured to establish a prediction model for the cutting depth of the rock abrasive water jet, and based on the experimental results, use the nonlinear multiple regression analysis method to determine the coefficient values of the prediction model for the cutting depth of the rock abrasive water jet. The parameter optimization module is configured to inversely calculate the operating parameters of the abrasive water jet according to the target cutting depth of the ultra-hard rock abrasive water jet and optimize the key parameters.

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