Energy-saving and efficient aluminum hub preparation process

Abstract

The invention discloses an energy-saving and efficient aluminum hub preparation process. According to the method, the optimal power consumption is predicted through the machine learning model, and the smelting parameters such as temperature and pressure can be adjusted in real time, so that the energy consumption is minimized. The machine learning model can predict the optimal operation parameters in the smelting process according to the data collected in real time, so that energy waste is reduced, and the energy utilization efficiency is improved. Through the prediction model, unnecessary energy consumption, such as overhigh temperature and pressure and overlong smelting time, can be avoided, so that the overall energy consumption is reduced. The machine learning model can predict the optimal smelting parameters, the trial and error time and the adjustment time are reduced, and therefore the whole production cycle is shortened. The stability and repeatability of the smelting process can be improved by predicting and adjusting the smelting parameters in real time, shutdown and faults in the production process are reduced, and the production efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of aluminum wheel hub preparation, and specifically relates to an energy-saving and efficient aluminum wheel hub preparation process. Background Art

[0002] The preparation of aluminum wheels mainly includes casting, machining, surface treatment and other links. Its purpose is to produce wheels with high strength, light weight and exquisite appearance to meet the needs of the modern automobile industry. Aluminum wheels have become the mainstream choice of automobile wheels due to their excellent performance and environmental protection characteristics. Aluminum wheel preparation is one of the key processes in the field of automobile parts manufacturing, which is directly related to the performance, safety and appearance of the vehicle. Aluminum wheel preparation is widely used in various types of automobiles, including passenger cars, commercial vehicles, racing cars, etc. With the rapid development of the automobile industry, the demand for aluminum wheels has increased year by year. Aluminum wheel preparation technology plays an important role in the field of automobile parts manufacturing. In the future, with the continuous research and development of new materials and new processes, aluminum wheel preparation technology will become more mature, providing the automotive industry with better quality and more environmentally friendly wheel products.

[0003] However, the traditional preparation method cannot adjust the smelting parameters in real time during the smelting process, which may cause fluctuations in the production process, affecting product quality and increasing energy consumption. At the same time, the traditional smelting process relies on manual operation and has a low degree of automation, resulting in production efficiency and product quality being greatly affected by human factors. Summary of the invention

[0004] The purpose of the present invention is to provide an energy-saving and efficient aluminum wheel hub manufacturing process in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: an energy-saving and efficient aluminum wheel hub preparation process, the process comprising the following steps:

[0006] S1: Use high-purity aluminum ingots as raw materials to ensure the quality of the wheel hub. According to the performance requirements of the wheel hub, weigh 950 parts by weight of pure aluminum; 70 parts by weight of silicon; 4 parts by weight of magnesium; 8 parts by weight of iron; 2 parts by weight of copper; 1 part by weight of manganese; 1 part by weight of zinc; and 1.5 parts by weight of titanium to improve the mechanical properties and casting properties of the wheel hub.

[0007] S2: Place aluminum ingots and alloy elements into a smelting furnace, use electromagnetic stirring and nitrogen protection smelting technology to reduce burning and oxidation, improve melt quality, and optimize power consumption through machine learning methods. First, collect data during the smelting process, and then pre-process the data;

[0008] S3: Build a random forest model and randomly extract multiple subsets from the original data set for model training;

[0009] S4: The mean square error (MSE) is used to evaluate the model performance. The trained random forest model is then integrated into the smelting control system. The smelting data is collected in real time and input into the model to predict the optimal power consumption. The smelting parameters are then adjusted based on the optimal power consumption predicted by the model.

[0010] S5: Prepare the mold and use CAD software to perform three-dimensional design according to the structural characteristics of the wheel hub;

[0011] S6: Use CNC machining center to process the mold to ensure mold accuracy. Inspect the size, shape and surface quality of the mold to ensure that the mold is qualified;

[0012] S7: Degas, refine and adjust the temperature of the melt to ensure the quality of the melt. Pour the treated melt into the mold using low-pressure casting or gravity casting. The wheel hub cools naturally in the mold to ensure uniform internal structure.

[0013] S8: Use a combination of mechanical and manual deburring methods to remove burrs on the hub surface, and use a high-pressure water jet to clean sand and oxide impurities on the hub surface;

[0014] S9: Put the wheel hub into the aging furnace for solution treatment + artificial aging treatment to improve the mechanical properties of the wheel hub. Controlled cooling: Use water cooling or air cooling to control the cooling speed of the wheel hub to avoid deformation;

[0015] S10: Use CNC lathe to perform rough machining on the wheel hub to remove excess material. Semi-finishing: Perform semi-finishing on the CNC machining center to ensure the wheel hub size accuracy. Finishing: Use high-precision CNC machining center for finishing to ensure the wheel hub surface quality and size accuracy;

[0016] S11: Degrease, pickle, alkaline wash, and phosphating the wheel hub to improve the adhesion of the coating. Use electrostatic spraying or electrophoretic coating technology to evenly spray primer, color paint, and varnish. Put the sprayed wheel hub into a curing furnace for high-temperature curing to improve the coating performance.

[0017] S12: Check whether there are scratches, bubbles, and color defects on the wheel surface, and use a three-coordinate measuring instrument to check whether the wheel size meets the requirements;

[0018] S13: Wipe the surface of the wheel hub with a clean cloth to ensure that there is no dust or oil stains. Finally, pack the wheel hub with shockproof, moisture-proof and scratch-proof packaging materials to complete the entire energy-saving and efficient aluminum wheel hub preparation process.

[0019] In a preferred embodiment, in step S1, during the smelting process, silicon, magnesium, copper, manganese, zinc and titanium alloy elements are added in sequence.

[0020] In a preferred embodiment, in step S2, the collected data includes:

[0021] T: furnace temperature (degrees Celsius);

[0022] E: power consumption;

[0023] C: melt composition;

[0024] t: melting time;

[0025] Q: Wheel quality, according to the quality inspection results, is 0 or 1, 0 represents unqualified, 1 represents qualified.

[0026] In a preferred embodiment, in step S3, the target variable Y is defined as the target variable in the process of constructing the random forest model. Here, we take the power consumption E as an example, and we hope to predict the minimum power consumption;

[0027] Define the characteristic variable X as a set of characteristic variables, including: temperature T, melt composition C, smelting time t;

[0028] The calculation formula for random forest prediction is: Where Y^ is the predicted value of power consumption, wi is the weight of the i-th tree (usually 1 / N in random forests), hi(X) is the prediction result of the i-th tree for the input feature X, and N is the number of trees.

[0029] In a preferred embodiment, in step S3, multiple subsets are randomly selected from the original data set for training during the model training process. For each subset, a feature subset is randomly selected to construct a decision tree. The above process is repeated to construct multiple decision trees to form a random forest.

[0030] For decision trees, information gain is calculated to select the optimal feature. The calculation formula is:

[0031] Among them, IG is the information gain, D is the data set, a is the feature, Values(a) is all possible values ​​of feature a, and Dv is the subset of features a and v in the data set.

[0032] In a preferred embodiment, in step S4, the model evaluation uses mean square error (MSE) to evaluate the model performance, and the calculation formula is: Among them, Yi is the actual power consumption, Yi is the predicted power consumption, and n is the number of samples.

[0033] In a preferred embodiment, in step S6, the CNC machining center needs to ensure that the machining accuracy of the mold is within ±0.05 mm to ensure the dimensional accuracy of the wheel hub. After the machining is completed, a high-precision measuring instrument (such as a three-coordinate measuring machine) is used to inspect the size, shape and surface quality of the mold.

[0034] In a preferred embodiment, in step S7, the melt is degassed by a rotary jet method, using chlorine or nitrogen as a degassing medium. A flux rod or a filter box is used to remove non-metallic inclusions during the refining process. The melt temperature is controlled between 700-740°C to ensure that the melt has good fluidity. Low-pressure casting is used, and the pressure is controlled at 0.5-1.0 MPa to reduce pores and shrinkage casting defects.

[0035] In a preferred embodiment, in step S8, the pressure of high-pressure water jet cleaning is controlled at 10-15 MPa, and the water temperature is controlled at 40-60° C., so as to effectively remove sand and oxide scale on the surface of the hub.

[0036] In a preferred embodiment, in step S9, during the heat treatment, the wheel hub is heated to 520-540° C., kept at this temperature for 2-4 hours, and then quickly cooled to room temperature by water.

[0037] Artificial aging: The aging temperature is 150-170℃, and the insulation time is 4-6 hours to improve the mechanical properties of the wheel hub.

[0038] Cooling control: Use water cooling or air cooling to control the cooling speed and avoid wheel deformation.

[0039] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0040] 1. In the present invention, the optimal power consumption is predicted by the machine learning model, and the smelting parameters, such as temperature and pressure, can be adjusted in real time to minimize energy consumption. The machine learning model can predict the optimal operating parameters in the smelting process based on the real-time collected data, thereby reducing energy waste and improving energy utilization efficiency. Through the prediction model, unnecessary energy consumption, such as excessive temperature and pressure, and excessive smelting time, can be avoided, thereby reducing overall energy consumption.

[0041] 2. In the present invention, the machine learning model can predict the optimal smelting parameters, reduce the time for trial and error and adjustment, and thus shorten the entire production cycle. Real-time prediction and adjustment of smelting parameters can improve the stability and repeatability of the smelting process, reduce downtime and failures in the production process, and improve production efficiency. The machine learning model can predict the quality of the wheel hub, so that timely adjustments can be made during the production process, reducing the scrap rate and improving product quality. The application of machine learning in the aluminum wheel hub preparation process can not only achieve energy conservation and consumption reduction, improve energy utilization efficiency, but also improve production efficiency and product quality, thereby achieving energy conservation and high efficiency of the entire process. Through machine learning, the production process can be intelligently optimized, human intervention can be reduced, and automated and intelligent production can be achieved, which is in line with the development trend of modern manufacturing. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] Example:

[0044] An energy-saving and efficient aluminum wheel hub preparation process, the process comprising the following steps:

[0045] S1: Use high-purity aluminum ingots as raw materials to ensure the quality of the wheel hub. According to the performance requirements of the wheel hub, weigh 950 parts by weight of pure aluminum; 70 parts by weight of silicon; 4 parts by weight of magnesium; 8 parts by weight of iron; 2 parts by weight of copper; 1 part by weight of manganese; 1 part by weight of zinc; and 1.5 parts by weight of titanium to improve the mechanical properties and casting properties of the wheel hub.

[0046] S2: Place aluminum ingots and alloy elements into a smelting furnace, use electromagnetic stirring and nitrogen protection smelting technology to reduce burning and oxidation, improve melt quality, and optimize power consumption through machine learning methods. First, collect data during the smelting process, and then pre-process the data;

[0047] S3: Build a random forest model and randomly extract multiple subsets from the original data set for model training;

[0048] S4: The mean square error (MSE) is used to evaluate the model performance. The trained random forest model is then integrated into the smelting control system. The smelting data is collected in real time and input into the model to predict the optimal power consumption. The smelting parameters are then adjusted based on the optimal power consumption predicted by the model.

[0049] S5: Prepare the mold and use CAD software to perform three-dimensional design according to the structural characteristics of the wheel hub;

[0050] S6: Use CNC machining center to process the mold to ensure the mold accuracy. Check the size, shape and surface quality of the mold to ensure that the mold is qualified;

[0051] S7: Degassing, refining, and temperature adjustment are performed on the melt to ensure the quality of the melt. The processed melt is poured into the mold using low-pressure casting or gravity casting technology. The wheel hub is naturally cooled in the mold to ensure uniform internal structure.

[0052] S8: Use a combination of mechanical and manual deburring methods to remove burrs on the hub surface, and use a high-pressure water jet to clean sand and oxide impurities on the hub surface;

[0053] S9: Put the wheel hub into the aging furnace for T6 (solid solution treatment + artificial aging) treatment to improve the mechanical properties of the wheel hub. Controlled cooling: Use water cooling or air cooling to control the cooling speed of the wheel hub to avoid deformation;

[0054] S10: Use CNC lathe to perform rough machining on the wheel hub to remove excess material. Semi-finishing: Perform semi-finishing on the CNC machining center to ensure the wheel hub size accuracy. Finishing: Use high-precision CNC machining center for finishing to ensure the wheel hub surface quality and size accuracy;

[0055] S11: Perform pre-treatment such as degreasing, pickling, alkali washing, and phosphating on the wheel hub to improve the adhesion of the coating. Use electrostatic spraying or electrophoretic coating technology to evenly spray primer, color paint, and varnish. Put the sprayed wheel hub into a curing furnace for high-temperature curing to improve the coating performance.

[0056] S12: Check whether there are scratches, bubbles, color difference and other defects on the wheel surface, and use equipment such as a three-coordinate measuring instrument to check whether the wheel size meets the requirements;

[0057] S13: Wipe the surface of the wheel hub with a clean cloth to ensure that there is no dust or oil stains. Finally, pack the wheel hub with shockproof, moisture-proof and scratch-proof packaging materials to complete the entire energy-saving and efficient aluminum wheel hub preparation process.

[0058] In step S1, during the smelting process, alloy elements such as silicon, magnesium, copper, manganese, zinc, and titanium are added in order. When adding, care should be taken to prevent oxidation and burning.

[0059] In step S2, the collected data includes:

[0060] T: furnace temperature (degrees Celsius);

[0061] E: Electricity consumption (kWh);

[0062] C: melt composition (e.g. percentage of silicon, magnesium, copper, zinc);

[0063] t: melting time (hours);

[0064] Q: Wheel quality (depending on the quality inspection results, it can be 0 or 1, 0 represents unqualified and 1 represents qualified).

[0065] In step S3, the target variable Y is defined as the target variable in the process of building the random forest model. Here, we take the power consumption E as an example, and we hope to predict the minimum power consumption;

[0066] Define the characteristic variable X as a set of characteristic variables, including: temperature T, melt composition C, smelting time t;

[0067] The calculation formula for random forest prediction is: Where Y^ is the predicted value of power consumption, wi is the weight of the i-th tree (usually equal to 1 / N in random forests), hi(X) is the prediction result of the i-th tree for the input feature X, and N is the number of trees.

[0068] In step S3, during the model training process, multiple subsets are randomly selected from the original data set for training. For each subset, a feature subset is randomly selected to construct a decision tree. The above process is repeated to construct multiple decision trees to form a random forest.

[0069] For decision trees, information gain is calculated to select the optimal feature. The calculation formula is:

[0070] Where IG is information gain, D is the dataset, a is the feature, Values(a) is all possible values ​​of feature a, and Dv is the subset of the dataset where feature a is equal to v.

[0071] In step S4, the model evaluation uses mean square error (MSE) to evaluate the model performance, and the calculation formula is: Among them, Yi is the actual power consumption, Yi is the predicted power consumption, and n is the number of samples.

[0072] In step S6, the CNC machining center needs to ensure that the machining accuracy of the mold is within ±0.05mm to ensure the dimensional accuracy of the wheel hub. After the machining is completed, a high-precision measuring instrument (such as a three-coordinate measuring machine) is used to inspect the size, shape and surface quality of the mold.

[0073] In step S7, the melt is degassed by a rotary jet method, using chlorine or nitrogen as the degassing medium. The refining process uses a flux rod or a filter box to remove non-metallic inclusions. The melt temperature is controlled between 700-740°C to ensure that the melt has good fluidity. Low-pressure casting is used, and the pressure is controlled at 0.5-1.0MPa to reduce casting defects such as pores and shrinkage cavities.

[0074] In step S8, the pressure of high-pressure water jet cleaning is controlled at 10-15 MPa, and the water temperature is controlled at 40-60° C. to effectively remove sand and oxide scale on the surface of the hub.

[0075] In step S9, during the heat treatment, the wheel hub is heated to 520-540° C., kept at this temperature for 2-4 hours, and then quickly cooled to room temperature by water.

[0076] Artificial aging: The aging temperature is 150-170℃, and the insulation time is 4-6 hours to improve the mechanical properties of the wheel hub.

[0077] Cooling control: Use water cooling or air cooling to control the cooling speed and avoid wheel deformation.

[0078] In the present invention, the optimal power consumption is predicted by the machine learning model, and the smelting parameters, such as temperature, pressure, etc., can be adjusted in real time to minimize energy consumption. The machine learning model can predict the optimal operating parameters in the smelting process based on the real-time collected data, thereby reducing energy waste and improving energy utilization efficiency. Through the prediction model, unnecessary energy consumption, such as excessive temperature and pressure, and excessive smelting time, can be avoided, thereby reducing overall energy consumption.

[0079] In the present invention, the machine learning model can predict the optimal smelting parameters, reduce the time for trial and error and adjustment, and thus shorten the entire production cycle. Real-time prediction and adjustment of smelting parameters can improve the stability and repeatability of the smelting process, reduce downtime and failures in the production process, and improve production efficiency. The machine learning model can predict the quality of the wheel hub, so that timely adjustments can be made during the production process, reducing the scrap rate and improving product quality. The application of machine learning in the aluminum wheel hub preparation process can not only achieve energy conservation and consumption reduction, improve energy utilization efficiency, but also improve production efficiency and product quality, thereby achieving energy conservation and high efficiency of the entire process. Through machine learning, the production process can be intelligently optimized, human intervention can be reduced, and automated and intelligent production can be achieved, which is in line with the development trend of modern manufacturing industry.

[0080] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0081] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy-saving and efficient aluminum wheel hub preparation process, Features: The process comprises the following steps: S1: Use high-purity aluminum ingots as raw materials to ensure the quality of the wheel hub. According to the performance requirements of the wheel hub, weigh 950 parts by weight of pure aluminum; 70 parts by weight of silicon; 4 parts by weight of magnesium; 8 parts by weight of iron; 2 parts by weight of copper; 1 part by weight of manganese; 1 part by weight of zinc; and 1.5 parts by weight of titanium to improve the mechanical properties and casting properties of the wheel hub. S2: Place aluminum ingots and alloy elements into a smelting furnace, use electromagnetic stirring and nitrogen protection smelting technology to reduce burning and oxidation, improve melt quality, and optimize power consumption through machine learning methods. First, collect data during the smelting process, and then pre-process the data; S3: Build a random forest model and randomly extract multiple subsets from the original data set for model training; S4: The mean square error (MSE) is used to evaluate the model performance. The trained random forest model is then integrated into the smelting control system. The smelting data is collected in real time and input into the model to predict the optimal power consumption. The smelting parameters are then adjusted based on the optimal power consumption predicted by the model. S5: Prepare the mold and use CAD software to perform three-dimensional design according to the structural characteristics of the wheel hub; S6: Use CNC machining center to process the mold to ensure mold accuracy; inspect the size, shape and surface quality of the mold to ensure that the mold is qualified; S7: Degas, refine and adjust the temperature of the melt to ensure the quality of the melt. Pour the treated melt into the mold using low-pressure casting or gravity casting. The wheel hub cools naturally in the mold to ensure uniform internal structure. S8: Use a combination of mechanical and manual deburring methods to remove burrs on the hub surface, and use a high-pressure water jet to clean sand and oxide impurities on the hub surface; S9: Put the wheel hub into the aging furnace for solution treatment + artificial aging treatment to improve the mechanical properties of the wheel hub; Control cooling: Use water cooling or air cooling to control the cooling speed of the wheel hub to avoid deformation; S10: Use CNC lathe to perform rough machining on the wheel hub to remove excess material; Semi-finishing: Perform semi-finishing on the CNC machining center to ensure the wheel hub size accuracy; Finishing: Use high-precision CNC machining center to perform finishing to ensure the wheel hub surface quality and size accuracy; S11: Degrease, pickle, alkaline wash, and phosphating the wheel hub to improve the adhesion of the coating. Use electrostatic spraying or electrophoretic coating technology to evenly spray primer, color paint, and varnish. Put the sprayed wheel hub into a curing furnace for high-temperature curing to improve the coating performance. S12: Check whether there are scratches, bubbles, and color defects on the wheel surface, and use a three-coordinate measuring instrument to check whether the wheel size meets the requirements; S13: Wipe the surface of the wheel hub with a clean cloth to ensure that there is no dust or oil stains. Finally, pack the wheel hub with shockproof, moisture-proof and scratch-proof packaging materials to complete the entire energy-saving and efficient aluminum wheel hub preparation process.

2. An energy-saving and efficient aluminum wheel hub preparation process as claimed in claim 1, Features: In step S1, during the smelting process, silicon, magnesium, copper, manganese, zinc and titanium alloy elements are added in sequence.

3. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In step S2, the collected data includes: T: furnace temperature (degrees Celsius); E: power consumption; C: melt composition; t: melting time; Q: Wheel quality, according to the quality inspection results, is 0 or 1, 0 represents unqualified, 1 represents qualified.

4. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In the step S3, the target variable Y is defined as the target variable in the process of constructing the random forest model. Here, we take the power consumption E as an example, and we hope to predict the minimum power consumption; Define the characteristic variable X as a set of characteristic variables, including: temperature T, melt composition C, smelting time t; The calculation formula for random forest prediction is: Where Y^ is the predicted value of power consumption, wi is the weight of the i-th tree (usually 1 / N in random forests), hi(X) is the prediction result of the i-th tree for the input feature X, and N is the number of trees.

5. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In step S3, during the model training process, multiple subsets are randomly selected from the original data set for training; for each subset, a feature subset is randomly selected to construct a decision tree; the above process is repeated to construct multiple decision trees to form a random forest; For decision trees, information gain is calculated to select the optimal feature. The calculation formula is: Among them, IG is the information gain, D is the data set, a is the feature, Values(a) is all possible values ​​of feature a, and Dv is the subset of features a and v in the data set.

6. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In step S4, the model evaluation uses mean square error (MSE) to evaluate the model performance, and the calculation formula is: Among them, Yi is the actual power consumption, Yi is the predicted power consumption, and n is the number of samples.

7. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In step S6, the CNC machining center needs to ensure that the machining accuracy of the mold is within ±0.05 mm to ensure the dimensional accuracy of the wheel hub. After the machining is completed, the mold is inspected for size, shape and surface quality using high-precision measuring instruments.

8. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In step S7, the melt is degassed by a rotary spray method, using chlorine or nitrogen as a degassing medium; a flux rod or a filter box is used to remove non-metallic inclusions during the refining process; The melt temperature is controlled between 700-740℃ to ensure good melt fluidity; Low-pressure casting is used, and the pressure is controlled at 0.5-1.0MPa to reduce casting defects such as pores and shrinkage holes.

9. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In step S8, the pressure of the high-pressure water jet cleaning is controlled at 10-15 MPa, and the water temperature is controlled at 40-60° C., so as to effectively remove sand and oxide scale on the surface of the hub.

10. The energy-saving and efficient aluminum wheel hub manufacturing process according to claim 1, Features: In the step S9, during the heat treatment, the hub is heated to 520-540° C., kept at this temperature for 2-4 hours, and then quickly cooled to room temperature by water; Artificial aging: The aging temperature is 150-170℃, and the temperature is kept for 4-6 hours to improve the mechanical properties of the wheel hub; Cooling control: Use water cooling or air cooling to control the cooling speed and avoid wheel deformation.

Images