Composite material lined casing and manufacturing equipment thereof

Abstract

The present invention relates to the technical field of liner casings, and in particular to a composite material liner casing and manufacturing equipment thereof, comprising a liner casing body in a hollow conical structure and an outer metal protective sleeve, wherein the metal protective sleeve is sleeved on the outer wall of the liner casing body, and an anti-corrosion ceramic layer is arranged on the inner wall of the liner casing body, wherein the anti-corrosion ceramic layer is surrounded by a plurality of arc-shaped ceramic lining blocks, and a circumferential expansion gap is arranged between each ceramic lining block. The manufacturing equipment of a composite material liner casing provided by the present invention accurately determines the heating temperature by collecting the characteristic parameters of the liner casing mixture to be processed and the sintering coefficient of the liner casing blank, realizes the intelligent generation and control of the heating temperature, and ensures the high efficiency and stable quality of the sintering process.

Description

Technical Field

[0001] The invention relates to the technical field of liner casings, in particular to a composite material liner casing and a manufacturing device thereof. Background Art

[0002] The tuyere of a blast furnace is an important equipment in the blast furnace air intake system. The service life of the tuyere sleeve directly affects the output and safe production of the blast furnace. After the blast furnace is sprayed with pulverized coal, the tuyere sleeve can be worn out in a very short time due to the incorrect position of the spray gun. Even if the gun position is very correct, the friction of the pulverized coal will cause serious wear on the tuyere. The composite material insulation lining sleeve of the blast furnace tuyere is a key material for the blast furnace tuyere. The lining of the blast furnace tuyere can effectively protect the tuyere structure and prevent it from being directly exposed to high temperature and chemical corrosion environment, thereby significantly improving the operating efficiency and life of the blast furnace. The existing composite material lining sleeve is manufactured in the following general steps: mixing, molding, drying and sintering. The specific process is to weigh the selected raw materials according to the formula ratio, and then put them into the mixing equipment for full mixing, put the mixed slurry into the mold, and apply pressure through the press to mold it. The formed lining sleeve is sent to the oven for drying. After drying, it is the lining sleeve blank, and then the lining sleeve blank is sent into the furnace body for sintering.

[0003] The Chinese patent with the authorization announcement number CN101900227B discloses a ceramic particle reinforced composite material lined metal pipe and a manufacturing method, which belongs to the field of composite wear-resistant pipes. 4,4-diphenylmethane diisocyanate (MDI), polyester diol, 1,4-butanediol (BD), ceramic particles, etc. are used as raw materials to prepare the composite liquid casting material, and the polyurethane-based ceramic particle reinforced composite material lining layer is cast on the inner wall of the outer casing by a centrifugal casting process.

[0004] As in the above application, the manufacturing steps of the existing liner sleeve made of composite materials include mixing, molding, drying and sintering, that is, the selected raw materials are weighed according to the formula ratio, and then put into the mixing equipment for sufficient mixing, the mixed slurry is put into the mold, and pressure is applied by a press to mold it. The molded liner sleeve is sent to the oven for drying, and the liner sleeve blank is obtained after drying. The blank is then sent to the furnace body for sintering. At present, the existing technology is generally used for artificial temperature control when sintering the liner sleeve blank. It is impossible to intelligently adjust the temperature according to the characteristic parameters of the liner sleeve mixture and the sintering coefficient of the liner sleeve blank, which can easily affect the sintering stability and density of the liner sleeve blank. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a composite material liner casing and a manufacturing device thereof.

[0006] The present invention adopts the following technical scheme: a composite material liner casing, comprising a liner casing body in a hollow conical cylindrical structure and an outer metal protective sleeve, wherein the metal protective sleeve is arranged on the outer wall of the liner casing body, and an anti-corrosion ceramic layer is arranged on the inner wall of the liner casing body;

[0007] The anti-corrosion ceramic layer is formed by enclosing a plurality of arc-shaped ceramic lining blocks, and a circumferential expansion gap is arranged between each ceramic lining block.

[0008] A manufacturing device for a composite material lined casing, used for manufacturing the composite material lined casing, comprises:

[0009] Pedestal;

[0010] A furnace body, wherein the furnace body is mounted on a base, and a furnace cavity is formed inside the furnace body, and the furnace cavity is used to place the liner sleeve blank for sintering;

[0011] A heater is arranged in the furnace body and is used to heat the sintered liner sleeve blank;

[0012] The sintering control unit is arranged on the furnace body and is used to control and adjust the heating temperature of the heater. The sintering control unit includes:

[0013] A data collection module is used to collect historical manufacturing comprehensive parameters of the liner casing, wherein the manufacturing comprehensive parameters include characteristic parameters of the liner casing mixture, sintering coefficient of the liner casing blank and heating temperature data;

[0014] The characteristic parameters of the liner casing mixture include the liner casing raw material composition ratio and the shape of the raw materials;

[0015] Model training module, based on the collected historical manufacturing comprehensive parameters of the liner casing, trains a machine learning model to predict the heating temperature, collects real-time liner casing mixture characteristic parameters and liner casing blank sintering coefficient, predicts the heating temperature data based on the trained machine learning model, and controls the heater operation based on the predicted heating temperature data;

[0016] A data acquisition module is used to obtain the average particle size of the liner casing mixture and the particle size dispersion of the liner casing mixture;

[0017] A data analysis module generates a mixture particle size coefficient according to the average particle size of the mixture and the particle size dispersion of the mixture;

[0018] The temperature compensation module generates a temperature compensation instruction based on the mixture particle size coefficient and adjusts the operating temperature of the heater according to the temperature compensation instruction.

[0019] As a further description of the above technical solution: the inner lining sleeve blank is formed by mixing, molding and drying the inner lining sleeve mixture, and its preparation method is: weighing the selected raw materials according to the formula ratio, and then putting them into a mixing equipment for sufficient mixing, putting the mixed slurry into a mold, and molding it by applying pressure through a press, and the molded inner lining sleeve is sent to an oven for drying, and the inner lining sleeve blank is obtained after drying.

[0020] As a further description of the above technical solution: the parameters affecting the sintering coefficient of the inner liner sleeve blank include the outer diameter of the inner liner sleeve blank, the wall thickness of the inner liner sleeve blank and the initial density of the inner liner sleeve blank;

[0021] The sintering coefficient of the liner casing blank is expressed as:

[0022] ;

[0023] In the formula, is the sintering coefficient of the liner blank, is the initial density of the liner blank, is the outer diameter of the liner casing blank, is the wall thickness of the liner casing blank, , and is the weight factor, , and Both are greater than 0.

[0024] As a further description of the above technical solution: the method for obtaining the average particle size of the liner casing mixture includes:

[0025] Obtain the particle size data of each raw material in the liner casing mixture and establish a particle size data set ,in Indicates the particle size data of the nth raw material;

[0026] Get the proportion of each raw material in the mixture of the liner casing and establish a proportion data set ,in Indicates the proportion of the nth raw material in the mixture;

[0027] Get the average particle size of the mixture ;

[0028] The expression of the average particle size of the mixture is:

[0029] .

[0030] As a further description of the above technical solution: the method for obtaining the particle size dispersion of the inner liner casing mixture includes:

[0031] ;

[0032] In the formula, is the particle size dispersion of the lining casing mixture, is the particle size data of the nth raw material, is the average particle size of the mixture.

[0033] As a further description of the above technical solution: the calculation formula of the particle size coefficient of the inner liner casing mixture is:

[0034] ;

[0035] In the formula, is the particle size coefficient of the liner casing mixture, is the average particle size of the lining casing mixture, is the particle size dispersion of the lining casing mixture, and are weight factors, and are all greater than 0.

[0036] As a further description of the above technical solution: the method for training a machine learning model for predicting the heating temperature includes:

[0037] The collected liner casing mixture characteristic parameters and liner casing blank sintering coefficients are converted into a corresponding set of characteristic vectors;

[0038] The collected liner casing mixture characteristic parameters and liner casing blank sintering coefficient are used as the input of the machine learning model, the machine learning model takes the heating temperature corresponding to each group of liner casing mixture characteristic parameters and liner casing blank sintering coefficient as the output, takes the heating temperature actually corresponding to each group of liner casing mixture characteristic parameters and liner casing blank sintering coefficient as the prediction target, takes minimizing the loss function value of the machine learning model as the training target, and stops training when the loss function value of the machine learning model is less than or equal to the preset target loss value.

[0039] As a further description of the above technical solution: the temperature compensation instruction includes a first temperature compensation instruction, a second temperature compensation instruction and a third temperature compensation instruction, and the temperatures increased by the first temperature compensation instruction, the second temperature compensation instruction and the third temperature compensation instruction increase in sequence.

[0040] As a further description of the above technical solution: the method for generating a temperature compensation instruction based on a mixture particle size coefficient includes:

[0041] Preset the mixture particle size coefficient gradient thresholds LD1, LD2 and LD3, and LD1<LD2<LD3;

[0042] when When <LD1, no temperature compensation instruction is generated;

[0043] When LD1≤ When <LD2, the first temperature compensation instruction is generated;

[0044] When LD2≤ When <LD3, the second temperature compensation instruction is generated;

[0045] when When ≥LD3, the third temperature compensation instruction is generated.

[0046] Beneficial effects:

[0047] The present invention provides a composite material liner casing manufacturing equipment, which trains a machine learning model for predicting the heating temperature based on the collected historical liner casing manufacturing comprehensive parameters, accurately determines the heating temperature by collecting the characteristic parameters of the liner casing mixture to be processed and the sintering coefficient of the liner casing blank, realizes the intelligent generation and control of the heating temperature, ensures the high efficiency and stable quality of the sintering process, and generates the heating temperature by comprehensively considering the characteristic parameters of the liner casing mixture (i.e., the initial component ratio data) and the liner casing blank for analysis, further improving the stability and sintering effect of the liner casing blank sintering by the heating temperature;

[0048] Furthermore, the heating temperature can be optimized according to the particle size coefficient of the mixture, the accuracy of controlling the liner casing blank can be further improved, and the sintering stability and sintering effect of the liner casing blank can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further explained below in conjunction with the accompanying drawings and embodiments:

[0050] Figure 1 A schematic cross-sectional view of a composite material liner casing along the length direction provided by an embodiment of the present invention;

[0051] Figure 2 A schematic cross-sectional view of a composite material liner casing along its circumferential direction provided by an embodiment of the present invention;

[0052] Figure 3 A schematic structural diagram of a composite material liner casing manufacturing device provided in an embodiment of the present invention;

[0053] Figure 4 A front view of a manufacturing device for a composite material liner casing provided by an embodiment of the present invention;

[0054] Figure 5 Schematic diagram of module connection of the sintering control unit provided in an embodiment of the present invention Figure 1 ;

[0055] Figure 6 Schematic diagram of module connection of the sintering control unit provided in an embodiment of the present invention Figure 2 .

[0056] Figure numerals: 1. Inner liner sleeve body; 2. Metal protective sleeve; 3. Anti-corrosion ceramic layer; 301. Ceramic liner block; 302. Expansion gap; 4. Base; 5. Furnace body; 501. Furnace cavity; 6. Heater; 7. Sintering control unit. DETAILED DESCRIPTION

[0057] In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the present invention is further described below with reference to specific diagrams. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0058] Example 1

[0059] See also Figure 1-Figure 2 The embodiment of the present invention provides a technical solution: a composite material liner casing, comprising a liner casing body 1 in a hollow conical cylindrical structure and an outer metal protective sleeve 2, the metal protective sleeve 2 is sleeved on the outer wall of the liner casing body 1, and an anti-corrosion ceramic layer 3 is arranged on the inner wall of the liner casing body 1;

[0060] The anti-corrosion ceramic layer 3 is formed by enclosing a plurality of arc-shaped ceramic lining blocks 301 , and a circumferential expansion gap 302 is provided between each ceramic lining block 301 .

[0061] Specifically, when the composite material liner sleeve is in use, the outer metal protective sleeve 2 is provided to facilitate welding and fixing, and the anti-corrosion ceramic layer 3 provided on the inner side can protect the liner sleeve body 1, avoid wear and corrosion of the liner sleeve body 1 by the mixed airflow of hot air and coal powder, and increase the service life of the liner sleeve body 1.

[0062] Example 2

[0063] See also Figure 3-Figure 5 The embodiment of the present invention provides a technical solution: a manufacturing device for a composite material liner casing, comprising:

[0064] Base 4;

[0065] A furnace body 5, which is mounted on the base 4, and has a furnace cavity 501 formed inside the furnace body 5, and the furnace cavity 501 is used to place the liner sleeve blank for sintering;

[0066] A heater 6 is arranged in the furnace body 5 and is used to heat the sintered liner sleeve blank;

[0067] The sintering control unit 7 is arranged on the furnace body 5 and is used to control and adjust the heating temperature of the heater 6. The sintering control unit 7 includes:

[0068] The data collection module is used to collect the historical manufacturing comprehensive parameters of the liner casing, which include the liner casing mixture characteristic parameters, the liner casing blank sintering coefficient and the heating temperature data;

[0069] The liner casing blank is formed by mixing, molding and drying the liner casing mixture. The preparation method is as follows: weighing the selected raw materials according to the formula ratio, and then putting them into a mixing device for sufficient mixing, putting the mixed slurry into a mold, and molding it by applying pressure through a press, and the molded liner casing is sent to an oven for drying, and the liner casing blank is obtained after drying.

[0070] The characteristic parameters of the liner casing mixture include the composition ratio of the liner casing raw materials and the shape of the raw materials;

[0071] It should be noted that different liner casing raw materials have different sintering temperatures. For example, materials such as alumina, silicon carbide, and magnesium aluminum spinel have different sintering temperature ranges. The ratio of different materials will affect the sintering temperature. For example, adding a certain amount of binder or sintering aid can reduce the sintering temperature. The finer the raw material particles, the lower the sintering temperature, because the fine powder has a larger specific surface area, which is conducive to diffusion and bonding during the sintering process. The shape of the raw material particles, such as spherical and flaky, will also affect the sintering temperature. Particles with regular shapes are more likely to be tightly stacked, and the sintering temperature is relatively low.

[0072] The model training module trains a machine learning model for predicting the heating temperature based on the collected historical comprehensive manufacturing parameters of the liner casing, collects real-time liner casing mixture characteristic parameters and liner casing blank sintering coefficient, predicts the heating temperature data based on the trained machine learning model, and controls the operation of the heater 6 based on the predicted heating temperature data.

[0073] The parameters that affect the sintering coefficient of the liner casing blank include the outer diameter of the liner casing blank, the wall thickness of the liner casing blank and the initial density of the liner casing blank;

[0074] The sintering coefficient of the liner casing blank is expressed as:

[0075] ;

[0076] In the formula, is the sintering coefficient of the liner blank, is the initial density of the liner blank, is the outer diameter of the liner casing blank, is the wall thickness of the liner casing blank, , and is the weight factor, , and Both are greater than 0.

[0077] It should be noted that the higher the initial density of the liner sleeve blank, the lower the sintering temperature can be, and vice versa, because the particles in the high-density blank are closer, and the diffusion path required for sintering is shorter. The larger the outer diameter of the liner sleeve blank, the higher the sintering temperature needs to be, and vice versa. The thicker the wall of the liner sleeve blank, the higher the sintering temperature needs to be, and vice versa.

[0078] It should be noted that the size of the weight factor is a specific value obtained by quantifying each data to facilitate subsequent comparison. The size of the weight factor depends on the number of comprehensive parameters and the preliminary setting of the corresponding weight factor for each set of comprehensive parameters by technical personnel in this field.

[0079] Methods for training a machine learning model to predict heating temperature include:

[0080] The collected characteristic parameters of the liner casing mixture and the sintering coefficient of the liner casing blank are converted into a corresponding set of characteristic vectors;

[0081] The collected liner casing mixture characteristic parameters and liner casing blank sintering coefficient are used as the input of the machine learning model. The machine learning model takes the heating temperature corresponding to each group of liner casing mixture characteristic parameters and liner casing blank sintering coefficient as the output, and takes the heating temperature actually corresponding to each group of liner casing mixture characteristic parameters and liner casing blank sintering coefficient as the prediction target. Minimizing the loss function value of the machine learning model is used as the training target. The training is stopped when the loss function value of the machine learning model is less than or equal to the preset target loss value.

[0082] The machine learning model is any one of a deep neural network model or a deep belief network model;

[0083] It should be noted that in the machine learning model, the calculation formula for prediction error is: ,in, is the number of the characteristic data, is the prediction error, For the The predicted heating temperature corresponding to the set of characteristic data, For the The actual heating temperature corresponding to the set characteristic data.

[0084] Specifically, the manufacturing equipment of the composite material liner casing trains a machine learning model for predicting the heating temperature based on the collected historical comprehensive manufacturing parameters of the liner casing, accurately determines the heating temperature by collecting the characteristic parameters of the liner casing mixture to be processed and the sintering coefficient of the liner casing blank, realizes the intelligent generation and control of the heating temperature, ensures the high efficiency and stable quality of the sintering process, and generates the heating temperature by comprehensively considering the characteristic parameters of the liner casing mixture (i.e., the initial component ratio data) and the liner casing blank for analysis, thereby further improving the stability and sintering effect of the sintering of the liner casing blank through the heating temperature.

[0085] Example 3

[0086] See also Figure 3-Figure 6 This embodiment adds a data acquisition module, a data analysis module and a temperature compensation module on the basis of Embodiment 2.

[0087] A data acquisition module is used to obtain the average particle size of the liner casing mixture and the particle size dispersion of the liner casing mixture;

[0088] A data analysis module generates a mixture particle size coefficient according to the average particle size of the mixture and the particle size dispersion of the mixture;

[0089] The temperature compensation module generates a temperature compensation instruction based on the mixture particle size coefficient, and adjusts the working temperature of the heater 6 according to the temperature compensation instruction.

[0090] Methods for obtaining the average particle size of the liner mixture include:

[0091] Obtain the particle size data of each raw material in the liner casing mixture and establish a particle size data set ,in Indicates the particle size data of the nth raw material;

[0092] Get the proportion of each raw material in the mixture of the liner casing and establish a proportion data set ,in Indicates the proportion of the nth raw material in the mixture;

[0093] Get the average particle size of the mixture ;

[0094] The expression of the average particle size of the mixture is:

[0095] .

[0096] The method for obtaining the particle size dispersion of the liner casing mixture includes:

[0097] ;

[0098] In the formula, is the particle size dispersion of the lining casing mixture, is the particle size data of the nth raw material, is the average particle size of the mixture.

[0099] The calculation formula for the particle size coefficient of the liner casing mixture is:

[0100] ;

[0101] In the formula, is the particle size coefficient of the liner casing mixture, is the average particle size of the lining casing mixture, is the particle size dispersion of the lining casing mixture, and are weight factors, and are all greater than 0.

[0102] It should be noted that the larger the particle size coefficient of the liner casing mixture is, the higher the sintering temperature is, and vice versa. The smaller the average particle size of the liner casing mixture is, the lower the sintering temperature is, and vice versa. Because the smaller the average particle size is, the larger the specific surface area is, which is conducive to diffusion and bonding during the sintering process. The smaller the particle size dispersion of the liner casing mixture is, the lower the sintering temperature is, and vice versa. Particles with small size differences are more likely to be tightly stacked, and the sintering temperature is relatively low.

[0103] It should be noted that the size of the weight factor is a specific value obtained by quantifying each data to facilitate subsequent comparison. The size of the weight factor depends on the number of comprehensive parameters and the preliminary setting of the corresponding weight factor for each set of comprehensive parameters by technical personnel in this field.

[0104] The temperature compensation instruction includes a first temperature compensation instruction, a second temperature compensation instruction and a third temperature compensation instruction, and the temperatures increased by the first temperature compensation instruction, the second temperature compensation instruction and the third temperature compensation instruction are increased in sequence.

[0105] Methods for generating temperature compensation instructions based on mixture particle size coefficients include:

[0106] The mixture particle size coefficient gradient thresholds LD1, LD2 and LD3 are preset, and LD1<LD2<LD3; the mixture particle size coefficient gradient thresholds are determined by those skilled in the art based on data fitting.

[0107] when When <LD1, no temperature compensation instruction is generated;

[0108] When LD1≤ When <LD2, the first temperature compensation instruction is generated;

[0109] When LD2≤ When <LD3, the second temperature compensation instruction is generated;

[0110] when When ≥LD3, the third temperature compensation instruction is generated.

[0111] Specifically, based on the above embodiments, this embodiment further adds a data acquisition module, a data analysis module and a temperature compensation module. The average particle size of the liner casing mixture and the particle size dispersion of the liner casing mixture are obtained through the data acquisition module, and a temperature compensation instruction is generated based on the mixture particle size coefficient. The working temperature of the heater 6 is adjusted according to the temperature compensation instruction, that is, the heating temperature is further optimized according to the mixture particle size coefficient, the accuracy of the control of the liner casing blank is further improved, and the stability and sintering effect of the liner casing blank sintering are improved.

[0112] The basic principles, main features and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and the description in the specification are only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A manufacturing device for a composite material inner liner casing, the composite material inner liner casing comprising an inner liner casing body (1) in a hollow conical cylindrical structure and an outer metal protective sleeve (2), the metal protective sleeve (2) being sleeved on the outer wall of the inner liner casing body (1), an anti-corrosion ceramic layer (3) being arranged on the inner wall of the inner liner casing body (1), the anti-corrosion ceramic layer (3) being formed by enclosing a plurality of arc-shaped ceramic liner blocks (301), a circumferential expansion gap (302) being arranged between each ceramic liner block (301), characterized in that: The manufacturing equipment comprises: Base (4); A furnace body (5), the furnace body (5) being mounted on a base (4), the furnace body (5) forming a furnace cavity (501) inside, the furnace cavity (501) being used to place liner casing blanks for sintering; A heater (6) is arranged in the furnace body (5) and is used to heat the sintered liner sleeve blank; A sintering control unit (7) is arranged on the furnace body (5) and is used to control and adjust the heating temperature of the heater (6). The sintering control unit (7) comprises: A data collection module is used to collect historical manufacturing comprehensive parameters of the liner casing, wherein the manufacturing comprehensive parameters include characteristic parameters of the liner casing mixture, sintering coefficient of the liner casing blank and heating temperature data; The characteristic parameters of the liner casing mixture include the liner casing raw material composition ratio and the shape of the raw materials; A model training module, based on the collected historical manufacturing comprehensive parameters of the liner casing, trains a machine learning model for predicting the heating temperature, collects real-time liner casing mixture characteristic parameters and liner casing blank sintering coefficient, predicts the heating temperature data based on the trained machine learning model, and controls the heater (6) based on the predicted heating temperature data; A data acquisition module is used to obtain the average particle size of the liner casing mixture and the particle size dispersion of the liner casing mixture; A data analysis module generates a mixture particle size coefficient according to the average particle size of the mixture and the particle size dispersion of the mixture; The temperature compensation module generates a temperature compensation instruction based on the mixture particle size coefficient, and adjusts the working temperature of the heater (6) according to the temperature compensation instruction.

2. The manufacturing equipment of a composite material liner casing according to claim 1, characterized in that: The liner casing blank is formed by mixing, molding and drying the liner casing mixture, and its preparation method is: weighing the selected raw materials according to the formula ratio, and then putting them into a mixing device for sufficient mixing, putting the mixed slurry into a mold, and molding it by applying pressure through a press, and the molded liner casing is sent to an oven for drying, and the liner casing blank is obtained after drying.

3. The manufacturing equipment of a composite material liner casing according to claim 1, characterized in that: The parameters affecting the sintering coefficient of the inner liner sleeve blank include the outer diameter of the inner liner sleeve blank, the wall thickness of the inner liner sleeve blank and the initial density of the inner liner sleeve blank; The sintering coefficient of the liner casing blank is expressed as: ； In the formula, is the sintering coefficient of the liner blank, is the initial density of the liner blank, is the outer diameter of the liner casing blank, is the wall thickness of the liner casing blank, , and is the weight factor, , and Both are greater than 0.

4. The manufacturing equipment of a composite material liner casing according to claim 1, characterized in that: The method for obtaining the average particle size of the liner casing mixture comprises: Obtain the particle size data of each raw material in the liner casing mixture and establish a particle size data set ,in Indicates the particle size data of the nth raw material; Get the proportion of each raw material in the mixture of the liner casing and establish a proportion data set ,in Indicates the proportion of the nth raw material in the mixture; Get the average particle size of the mixture ; The expression of the average particle size of the mixture is: 。 5. The manufacturing equipment of a composite material liner casing according to claim 4, characterized in that: The method for obtaining the particle size dispersion of the liner casing mixture includes: ； In the formula, is the particle size dispersion of the lining casing mixture, is the particle size data of the nth raw material, is the average particle size of the mixture.

6. The manufacturing equipment of a composite material liner casing according to claim 5, characterized in that: The calculation formula of the liner casing mixture particle size coefficient is: ； In the formula, is the particle size coefficient of the liner casing mixture, is the average particle size of the liner casing mixture, is the particle size dispersion of the lining casing mixture, and are weight factors, and are all greater than 0.

7. The manufacturing equipment of a composite material liner casing according to claim 1, characterized in that: The method for training a machine learning model for predicting a heating temperature includes: The collected characteristic parameters of the liner casing mixture and the sintering coefficient of the liner casing blank are converted into a corresponding set of characteristic vectors; The collected liner casing mixture characteristic parameters and liner casing blank sintering coefficient are used as the input of the machine learning model, the machine learning model takes the heating temperature corresponding to each group of liner casing mixture characteristic parameters and liner casing blank sintering coefficient as the output, takes the heating temperature actually corresponding to each group of liner casing mixture characteristic parameters and liner casing blank sintering coefficient as the prediction target, takes minimizing the loss function value of the machine learning model as the training target, and stops training when the loss function value of the machine learning model is less than or equal to the preset target loss value.

8. The manufacturing equipment of a composite material liner casing according to claim 6, characterized in that: The temperature compensation instruction includes a first temperature compensation instruction, a second temperature compensation instruction and a third temperature compensation instruction, and the temperatures increased by the first temperature compensation instruction, the second temperature compensation instruction and the third temperature compensation instruction are increased in sequence.

9. The manufacturing equipment of composite material liner casing according to claim 8, characterized in that: The method for generating a temperature compensation instruction based on a mixture particle size coefficient comprises: Preset the mixture particle size coefficient gradient thresholds LD1, LD2 and LD3, and LD1<LD2<LD3; when When <LD1, no temperature compensation instruction is generated; When LD1≤ When <LD2, the first temperature compensation instruction is generated; When LD2≤ When <LD3, the second temperature compensation instruction is generated; when When ≥LD3, the third temperature compensation instruction is generated.

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