A method of manufacturing a monolithically formed large weight assembly

Abstract

The application discloses a preparation method of a monolithic large-size counterweight assembly, and belongs to the technical field of counterweight assembly production. The preparation method comprises the following specific steps: S1, a preparation step: preparing iron, carbon and some alloy elements for casting, and preparing a casting mold of the counterweight assembly; S2, raw material smelting: smelting the casting raw material prepared in S1 by using an electric arc furnace, so that the casting raw material reaches a proper molten state. The counterweight assembly is cast by using multiple different materials, so that the flowability and lubricity of the casting solution are improved, the wear resistance, the abrasion resistance and the shock resistance of the counterweight assembly casting are improved, the corrosion resistance of the counterweight assembly in an industrial scene is significantly increased, the white layer depth is reduced, and the hard points of carbides are eliminated, so that the large-size counterweight assembly has a long service life in a relatively strong corrosive environment.

Description

Technical Field

[0001] This invention belongs to the field of counterweight assembly manufacturing technology, specifically relating to a method for preparing an integrally molded large counterweight assembly. Background Technology

[0002] A counterweight assembly is an important component used to balance mechanical equipment. Its main function is to increase the stability of the equipment and reduce vibration, thereby improving the working efficiency and service life of the equipment. Large counterweight assemblies can be used to balance construction equipment such as tower cranes and cranes. In these devices, the changes in the length and weight of the boom can cause unbalanced forces during operation, requiring the use of large counterweight assemblies to balance the boom.

[0003] Since large counterweight assemblies are used in many scenarios, including some highly corrosive working environments, the use of existing large counterweight assemblies in highly corrosive environments can lead to damage and corrosion of the metal materials. This may result in weakening, deformation, cracking or breaking of the counterweight assembly, reducing its strength and rigidity, and affecting the service life of the counterweight assembly. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method for preparing an integrally molded large counterweight assembly.

[0005] The technical solution adopted to solve the above-mentioned technical problems is: a method for preparing an integrally molded large counterweight assembly, characterized by the following specific steps:

[0006] S1. Preparation steps: Prepare iron, carbon, and some alloying elements for casting, and prepare the casting mold for the counterweight assembly.

[0007] S2, Raw material melting and mold casting: The casting raw materials prepared in S1 are melted in an electric arc furnace until they reach a suitable molten state. The prepared casting mold is placed at the pouring port of the electric arc furnace, and the molten casting raw materials are poured into the casting mold to ensure that the entire mold space is filled and to eliminate defects such as air bubbles.

[0008] S3. Mold Cooling: Cool the casting mold after casting in S2 to ensure that the casting is completely solidified and has sufficient strength;

[0009] S4. Casting demolding: Manually demold the casting mold after it has cooled in S3 to ensure that the casting can be completely removed from the casting mold;

[0010] S5. Machining and finishing: Machining and finishing the counterweight assembly castings removed in S4 to ensure that the casting surface is smooth and meets the final size and shape requirements;

[0011] S6. Quality Inspection: Inspect the counterweight assembly after processing and repair in S5 to ensure that the quality of the counterweight assembly meets the standards.

[0012] S7. Surface treatment: The counterweight assembly after frequent quality inspection in S6 is surface treated to increase its performance and improve its aesthetics, and finally the finished counterweight assembly is obtained.

[0013] Furthermore, the casting raw materials in S1 include iron, carbon, silicon, nickel, and copper.

[0014] By using the above technical solutions and casting the counterweight assembly with a variety of different materials, the metallic properties of the counterweight assembly can be improved.

[0015] Furthermore, the mold used for the counterweight assembly in S1 is a steel mold, and a metal release agent is coated in the steel mold.

[0016] Through technical solutions, steel molds have higher strength and hardness, and good wear resistance, making them suitable for mass production and long-term use. They also provide higher precision and surface quality. At the same time, coating the steel mold with a metal release agent allows the metal mixture to flow better, with less adhesion, making it easier to demold the casting, reducing demolding difficulty, and improving demolding quality.

[0017] Furthermore, during the smelting process in S2, the proportion of iron is over 90%, the proportion of carbon is 2% to 4%, the proportion of silicon is 1% to 3%, the proportion of nickel is 0.25% to 2%, and the proportion of copper is 0.25% to 1%, while the smelting temperature of the electric arc furnace is controlled at 1350℃ to 1450℃.

[0018] Through the above technical solutions, the use of carbon can make the counterweight assembly more robust and wear-resistant; the use of silicon can improve the fluidity and lubricity of the casting solution; the use of nickel can help decompose carbides, reduce the depth of the white iron layer, and eliminate hard spots of carbides; the use of copper can improve the wear resistance, anti-wear properties, and shock absorption of the counterweight assembly casting, and significantly increase the corrosion resistance of the counterweight assembly in industrial settings. Furthermore, controlling the melting temperature of the electric arc furnace can ensure that the metal mixture is completely melted and maintains sufficient liquidity for casting.

[0019] Furthermore, during mold casting in S2, the temperature of the electric arc furnace is controlled at 1400℃~1500℃.

[0020] By using the above technical solution, slightly increasing the temperature of the electric arc furnace during casting can help improve the fluidity of the melt, reduce the generation of porosity and shrinkage cavities, and improve the casting quality of the counterweight assembly.

[0021] Furthermore, in step S3, air cooling is first used to cool the casting mold. When the temperature of the casting mold drops to about 600°C, a slow heating method is then used to cool the casting mold.

[0022] Through the above technical solution, since there is a large temperature difference between the inside and outside of the mold, rapid cooling with water would lead to excessive thermal stress, causing problems such as mold deformation and cracking. By first using air cooling to initially reduce the temperature of the casting mold, and then using a slow heating method to cool the casting mold, the temperature can be gradually equalized, reducing the magnitude of thermal stress, reducing the risk of mold deformation and cracking, improving the mold's resistance to thermal fatigue and service life, and allowing the internal temperature of the counterweight assembly casting to gradually and uniformly decrease, reducing the generation of defects and ensuring the quality of the counterweight assembly casting.

[0023] Furthermore, in S4, a tapping demolding method is used, in which a wooden mallet or metal hammer is used to tap the surface of the mold, causing the part to vibrate and gradually detach from the mold.

[0024] The above technical solution uses a gentle tapping demolding method to prevent damage to the counterweight assembly during demolding due to excessive demolding force, thereby improving the demolding quality of the counterweight assembly.

[0025] Furthermore, in step S5, the counterweight assembly after demolding is first desanded, then the surface of the counterweight assembly is cleaned, and then the counterweight assembly is processed by milling, turning, drilling, cutting, grinding, polishing and other processes.

[0026] The above technical solution involves first removing sand particles and impurities from the surface of the demolded counterweight assembly, then cleaning the surface of the counterweight assembly to ensure it is smooth for subsequent processing, and finally performing a series of processing steps on the counterweight assembly casting to ensure that the size and shape of the counterweight assembly meet the requirements.

[0027] Furthermore, in step S6, the counterweight assembly undergoes dimensional measurement, hardness testing, and visual inspection to ensure that the product meets design specifications and quality standards.

[0028] By using the above technical solutions, the counterweight assembly can be inspected for dimensions, hardness, and appearance, ensuring that the finished counterweight assembly meets quality standards and design specifications. Unqualified counterweight assemblies can be processed, repaired, or recast for reuse, reducing material consumption.

[0029] Furthermore, in step S7, the counterweight assembly is sprayed with anti-rust paint to improve its corrosion resistance and appearance.

[0030] The above technical solution can further improve the corrosion resistance of the counterweight assembly by spraying anti-rust paint on its surface, enabling it to have a longer service life even when used in highly corrosive environments. At the same time, spraying anti-rust paint on the surface of the counterweight assembly can also improve the aesthetics of the finished counterweight assembly.

[0031] In step S2, based on the real-time monitored melting state and temperature distribution, an intelligent control method is used to automatically adjust the temperature of the electric arc furnace to achieve a more uniform and faster melting process. Then, by analyzing the gas composition and pressure changes generated during the melting process, defects such as bubbles are predicted and eliminated in a timely manner, thereby improving the quality of the casting. The specific process is as follows:

[0032] Step 1: Collect historical data and real-time monitoring data on melting state and temperature distribution. This data can be acquired through sensors or monitoring equipment. The collected historical data is then cleaned, denoised, and standardized to ensure data quality and consistency.

[0033] Step 2: Extract features related to the melting state and temperature distribution from real-time monitoring data, including current, voltage, and temperature;

[0034] Step 3: Using the preprocessed historical data as a training set, an improved regression analysis method is used to establish a predictive model for the smelting process. This model can predict the power and temperature of the electric arc furnace based on the characteristics of real-time monitoring data.

[0035] Suppose there is a linear regression model y = β0 + β1x1 + β2x2 + ... + βnxn, where y is the target variable, representing the melting state and temperature distribution, x1, x2, ..., xn are the characteristic variables, representing current, voltage, and temperature, and β0, β1, ..., βn are the parameters to be determined;

[0036] Given a historical dataset X = [x11, x12, ..., xln, x21, x22, ..., x2n, ..., xml, xm2, ..., xmn] and y = [y11, y12, ..., yln, y21, y22, ..., y2n, ..., ym1, ym2, ..., ymn], where m is the number of samples;

[0037] The goal of the least squares method is to find a parameter estimate β^=(β^1,β^2,...,β^n) that minimizes the mean square error: MSE=(1 / m)∑(yi-β^i*xi)^2;

[0038] By solving the normal equation system |XTX|β^TXβ=I, where XTX is the transpose of the characteristic matrix multiplied by the characteristic matrix v, β^TX is the parameter matrix β multiplied by the transpose of the characteristic matrix X, and I is the identity matrix, the final parameter estimate β^ can be obtained.

[0039] Step 4: Evaluate the trained model using the test dataset. Based on the real-time monitored melting state and temperature distribution, input the real-time data into the trained model to obtain the prediction results. Then, automatically adjust the power and temperature of the electric arc furnace according to the prediction results to achieve a more uniform and faster melting process.

[0040] Step 5: Collect data on the composition and pressure changes of the gas generated during the melting process. This data can be monitored and recorded in real time by sensors.

[0041] Step 6: From the collected data, we can extract features related to bubble defects, including the concentration of gas components and the range of pressure variations.

[0042] Step 7: Preprocess the extracted features, such as normalization, to facilitate subsequent model training and performance evaluation.

[0043] Step 8: Based on existing research and application experience, select the logistic regression algorithm, take the preprocessed data as input, train the selected classification model, and optimize the model parameters through repeated iterations so that it can accurately distinguish between normal castings and castings with bubble defects.

[0044] The goal of logistic regression is to predict the value of the target variable y (i.e., whether there is a bubble defect) based on the input features z1, z2, ..., zn. The formula for calculating logistic regression is shown below:

[0045] h(z)=sigmoid(wO+w1*z1+w2*z2+...+wn*zn)

[0046] Where w0, w1, ..., wn are the parameters of the model, which need to be learned and optimized through training data; z1, z2, ..., zn are the input features; sigmoid() is a non-linear function that maps h(z) to the interval (0, 1).

[0047] During training, our goal is to minimize the loss function. Common loss functions include cross-entropy loss and mean squared error loss. For logistic regression, the cross-entropy loss function can be expressed as L(w)=-[y*log(h(z))+(1-y)*log(1-h(z))], where y is the true label (i.e. whether it contains bubble defects) and h(z) is the predicted value.

[0048] By using optimization algorithms such as gradient descent, we can update the parameters w based on the gradient of the loss function relative to the model parameters, thereby continuously reducing the loss function. Ultimately, we obtain the optimal model parameters w0, w1, ..., wn, resulting in the model achieving the best performance on the training set.

[0049] Step 9: Evaluate the performance of the trained model using methods such as cross-validation. Metrics such as accuracy, recall, and F1 score can be calculated to assess the model's accuracy and robustness.

[0050] Step 10: Deploy the trained model into the actual smelting system, monitor changes in gas composition and pressure in real time, and promptly eliminate bubble defects based on model predictions to improve the quality of castings.

[0051] The beneficial effects of the present invention are as follows: (1) By using a variety of different materials to cast the counterweight assembly, the present invention can improve the fluidity and lubricity of the casting solution, improve the wear resistance, anti-wear properties, and shock absorption of the counterweight assembly casting, and significantly increase the corrosion resistance of the counterweight assembly in industrial settings. At the same time, it reduces the depth of the white iron layer and eliminates the hard spots of carbides, enabling large counterweight assemblies to have a longer service life in highly corrosive environments; (2) The present invention uses a slow heating method to cool the casting mold so that the temperature gradually becomes uniform, reducing the magnitude of thermal stress and lowering the temperature. The invention reduces the risk of mold deformation and cracking, improves the mold's thermal fatigue resistance and service life, and allows the internal temperature of the counterweight assembly casting to gradually and uniformly decrease, reducing the generation of defects and ensuring the quality of the counterweight assembly; (3) This invention controls the melting temperature and casting temperature of the electric arc furnace. Controlling the melting temperature of the electric arc furnace can ensure that the molten iron is completely melted and maintains sufficient liquidity for casting. Furthermore, the temperature of the electric arc furnace is slightly increased during casting, which can help improve the fluidity of the melt, reduce the generation of porosity and shrinkage cavities, and improve the quality of the counterweight assembly casting. (4) By automatically adjusting the power and temperature of the electric arc furnace through intelligent algorithms, a more uniform and faster melting process can be achieved, thereby improving production efficiency. Predicting and timely eliminating defects such as bubbles can reduce the number of defective products in the casting, reduce the scrap rate, and improve product quality. Furthermore, the algorithm can automatically adjust the power and temperature of the electric arc furnace based on real-time monitoring of the melting state and temperature distribution, avoiding overheating or energy waste and saving energy costs. Real-time monitoring of the melting state and temperature distribution, and adjustments made as needed, makes the melting process more stable and reduces the occurrence of process anomalies. By optimizing the melting process and predicting bubble defects, the quality of castings can be improved, reducing repair and reprocessing costs in subsequent machining processes. The intelligent algorithm can automatically adjust the power and temperature of the electric arc furnace, reducing the need for manual intervention, lowering operational difficulty, and reducing the possibility of human error. Attached Figure Description

[0052] Figure 1 This is a flowchart of the preparation process of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] like Figure 1 As shown, a method for manufacturing a large, integrally molded counterweight assembly according to this embodiment includes the following specific steps:

[0055] S1. Preparation steps: Prepare iron, carbon, and some alloying elements for casting, and prepare the casting mold for the counterweight assembly.

[0056] S2, Raw material melting and mold casting: Melt the casting raw materials prepared in S1 using an electric arc furnace until they reach a suitable molten state; place the prepared casting mold at the pouring port of the electric arc furnace, and pour the molten casting raw materials into the casting mold, ensuring that the entire mold space is filled and eliminating defects such as air bubbles;

[0057] S3. Mold Cooling: Cool the casting mold after casting in S2 to ensure that the casting is completely solidified and has sufficient strength;

[0058] S4. Casting demolding: Manually demold the casting mold after it has cooled in S3 to ensure that the casting can be completely removed from the casting mold;

[0059] S5. Machining and finishing: Machining and finishing the counterweight assembly castings removed in S4 to ensure that the casting surface is smooth and meets the final size and shape requirements;

[0060] S6. Quality Inspection: Inspect the counterweight assembly after processing and repair in S5 to ensure that the quality of the counterweight assembly meets the standards.

[0061] S7. Surface treatment: The counterweight assembly after frequent quality inspection in S6 is surface treated to increase its performance and improve its aesthetics, and finally the finished counterweight assembly is obtained.

[0062] The casting materials in S1 include iron, carbon, silicon, nickel, and copper. The mold used for the counterweight assembly in S1 is a steel mold coated with a metal release agent. The steel mold is suitable for mass production and long-term use, and possesses good wear resistance and hardness, enabling the counterweight assembly casting to achieve higher precision and surface quality. Furthermore, the use of the metal release agent in the steel mold improves the fluidity of the metal mixture, reducing its adhesion and making the counterweight assembly casting easier to demold, thus reducing demolding difficulty and improving the demolding quality.

[0063] During smelting in S2, iron accounts for over 90%, carbon 2%–4%, silicon 1%–3%, nickel 0.25%–2%, and copper 0.25%–1%. The smelting temperature in the electric arc furnace is controlled at 1350℃–1450℃. During mold casting in S2, the electric arc furnace temperature is controlled at 1400℃–1500℃. Carbon provides wear resistance to the counterweight assembly, silicon improves the fluidity of the molten metal mixture during casting, nickel reduces the depth of the white iron layer, and copper provides good corrosion resistance. This effectively enhances the various metallic properties of the counterweight assembly. The smelting temperature in the electric arc furnace ensures complete melting of the molten metal mixture and maintains sufficient liquidity. Controlling the casting temperature in the electric arc furnace reduces porosity and shrinkage cavities during casting, thus improving the casting quality of the counterweight assembly.

[0064] In step S2, based on the real-time monitored melting state and temperature distribution, an intelligent control method is used to automatically adjust the temperature of the electric arc furnace to achieve a more uniform and faster melting process. Then, by analyzing the gas composition and pressure changes generated during the melting process, defects such as bubbles are predicted and eliminated in a timely manner, thereby improving the quality of the casting. The specific process is as follows:

[0065] Step 1: Collect historical data and real-time monitoring data on melting state and temperature distribution. This data can be acquired through sensors or monitoring equipment. The collected historical data is then cleaned, denoised, and standardized to ensure data quality and consistency.

[0066] Step 2: Extract features related to the melting state and temperature distribution from real-time monitoring data, including current, voltage, and temperature;

[0067] Step 3: Using the preprocessed historical data as a training set, an improved regression analysis method is used to establish a predictive model for the smelting process. This model can predict the power and temperature of the electric arc furnace based on the characteristics of real-time monitoring data.

[0068] Suppose there is a linear regression model y = β0 + β1x1 + β2x2 + ... + βnxn, where y is the target variable, representing the melting state and temperature distribution, x1, x2, ..., xn are the characteristic variables, representing current, voltage, and temperature, and β0, β1, ..., βn are the parameters to be determined;

[0069] Given a historical dataset X = [x11, x12, ..., xln, x21, x22, ..., x2n, ..., xml, xm2, ..., xmn] and y = [y11, y12, ..., yln, y21, y22, ..., y2n, ..., ym1, ym2, ..., ymn], where m is the number of samples;

[0070] The goal of the least squares method is to find a parameter estimate β^=(β^1,β^2,...,β^n) that minimizes the mean square error: MSE=(1 / m)∑(yi-β^i*xi)^2;

[0071] By solving the normal equation system |XTX|β^TXβ=I, where XTX is the transpose of the characteristic matrix multiplied by the characteristic matrix X, β^TX is the parameter matrix β multiplied by the transpose of the characteristic matrix X, and I is the identity matrix, the final parameter estimate β^ can be obtained.

[0072] Step 4: Evaluate the trained model using the test dataset. Based on the real-time monitored melting state and temperature distribution, input the real-time data into the trained model to obtain the prediction results. Then, automatically adjust the power and temperature of the electric arc furnace according to the prediction results to achieve a more uniform and faster melting process.

[0073] Step 5: Collect data on the composition and pressure changes of the gas generated during the melting process. This data can be monitored and recorded in real time by sensors.

[0074] Step 6: From the collected data, we can extract features related to bubble defects, including the concentration of gas components and the range of pressure variations.

[0075] Step 7: Preprocess the extracted features, such as normalization, to facilitate subsequent model training and performance evaluation.

[0076] Step 8: Based on existing research and application experience, select the logistic regression algorithm, take the preprocessed data as input, train the selected classification model, and optimize the model parameters through repeated iterations so that it can accurately distinguish between normal castings and castings with bubble defects.

[0077] The goal of logistic regression is to predict the value of the target variable y (i.e., whether there is a bubble defect) based on the input features z1, z2, ..., zn. The formula for calculating logistic regression is shown below:

[0078] h(z)=sigmoid(w0+w1*z1+w2*z2+...+wn*zn)

[0079] Where w0, w1, ..., wn are the parameters of the model, which need to be learned and optimized through training data; z1, z2, ..., zn are the input features; sigmoid() is a non-linear function that maps h(z) to the interval (0, 1).

[0080] During training, our goal is to minimize the loss function. Common loss functions include cross-entropy loss and mean squared error loss. For logistic regression, the cross-entropy loss function can be expressed as L(w)=-[y*log(h(z))+(1-y)*log(1-h(z))], where y is the true label (i.e. whether it contains bubble defects) and h(z) is the predicted value.

[0081] By using optimization algorithms such as gradient descent, we can update the parameters w based on the gradient of the loss function relative to the model parameters, thereby continuously reducing the loss function. Ultimately, we obtain the optimal model parameters w0, w1, ..., wn, resulting in the model achieving the best performance on the training set.

[0082] Step 9: Evaluate the performance of the trained model using methods such as cross-validation. Metrics such as accuracy, recall, and F1 score can be calculated to assess the model's accuracy and robustness.

[0083] Step 10: Deploy the trained model into the actual smelting system, monitor changes in gas composition and pressure in real time, and promptly eliminate bubble defects based on model predictions to improve the quality of castings.

[0084] In S3, air cooling is first used to cool the casting mold. Once the temperature of the casting mold drops to around 600℃, a slow heating method is used to further cool the casting mold. In S4, a gentle tapping demolding method is used, where a wooden or metal hammer is used to gently tap the surface of the mold, causing the part to vibrate and gradually detach from the mold. Using a slow heating method to cool the casting mold reduces thermal stress, lowers the risk of mold deformation and cracking, and also allows the internal temperature of the counterweight assembly casting to gradually decrease, reducing the probability of defects in the counterweight assembly casting and effectively improving the quality of the counterweight assembly casting. At the same time, the gentle tapping demolding method avoids damage to the counterweight assembly casting during demolding due to the light tapping force, effectively improving the demolding quality of the counterweight assembly.

[0085] In S5, the counterweight assembly after demolding is first desanded, then the surface of the counterweight assembly is cleaned, and then the counterweight assembly is processed by milling, turning, drilling, cutting, grinding, polishing and other processes. The desanding process is given priority to remove the sand and impurities on the surface of the counterweight assembly after demolding. Then the surface of the counterweight assembly is cleaned to make the surface of the counterweight assembly smooth, so as to facilitate subsequent processing of the counterweight assembly. After that, a series of processing is carried out on the counterweight assembly according to the design size and shape to make the processed counterweight assembly consistent with the design.

[0086] In S6, the counterweight assembly undergoes dimensional measurements, hardness tests, and visual inspections to ensure it meets design specifications and quality standards. In S7, the counterweight assembly is coated with anti-rust paint to improve its corrosion resistance and aesthetic appearance. Multiple tests are conducted on the counterweight assembly after a series of processing steps to ensure its processing quality. If any counterweight assembly fails to meet quality standards, it can be reprocessed or recast for reuse. Once the counterweight assembly passes inspection, one side of its surface is coated with anti-rust paint, which improves both its corrosion resistance and aesthetic appearance.

[0087] In summary, by using a variety of different materials to cast the counterweight assembly, this invention can improve the fluidity and lubricity of the casting solution, enhance the wear resistance, anti-wear properties, and shock absorption of the counterweight assembly casting, and significantly increase the corrosion resistance of the counterweight assembly in industrial settings. It also reduces the depth of the white iron layer and eliminates carbide hard spots, enabling large counterweight assemblies to have a longer service life in highly corrosive environments.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A method for manufacturing a large, integrally molded counterweight assembly, characterized in that, The specific steps include the following: S1. Preparation steps: Prepare iron, carbon, and some alloying elements for casting, and prepare the casting mold for the counterweight assembly. S2, Raw material melting and mold casting: The casting raw materials prepared in S1 are melted in an electric arc furnace until they reach a suitable molten state; then the prepared casting mold is placed at the pouring port of the electric arc furnace, and the molten casting raw materials are poured into the casting mold, ensuring that the entire mold space is filled and eliminating air bubble defects. S3. Mold Cooling: Cool the casting mold after casting in S2 to ensure that the casting is completely solidified and has sufficient strength; S4. Casting demolding: Manually demold the casting mold after it has cooled in S3 to ensure that the casting can be completely removed from the casting mold; S5. Machining and finishing: Machining and finishing the counterweight assembly castings removed in S4 to ensure that the casting surface is smooth and meets the final size and shape requirements; S6. Quality Inspection: Inspect the counterweight assembly after processing and repair in S5 to ensure that the quality of the counterweight assembly meets the standards. S7. Surface treatment: The counterweight assembly after frequent quality inspection in S6 is surface treated to increase its performance and improve its aesthetics, and finally the finished counterweight assembly is obtained. The casting raw materials in S1 include iron, carbon, silicon, nickel, and copper; During the smelting process in S2, the proportion of iron is over 90%, the proportion of carbon is 2% to 4%, the proportion of silicon is 1% to 3%, the proportion of nickel is 0.25% to 2%, and the proportion of copper is 0.25% to 1%. At the same time, the smelting temperature of the electric arc furnace is controlled at 1350℃ to 1450℃. During mold casting, the temperature of the electric arc furnace is controlled at 1400℃ to 1500℃.

2. The method for preparing an integrally molded large counterweight assembly according to claim 1, characterized in that, The mold used for the counterweight assembly in S1 is a steel mold, and a metal release agent is coated inside the steel mold.

3. The method for preparing an integrally molded large counterweight assembly according to claim 1, characterized in that, In S4, a tapping demolding method is used, in which a wooden mallet or metal hammer is used to tap the surface of the mold, causing the part to vibrate and gradually detach from the mold.

4. The method for preparing an integrally molded large counterweight assembly according to claim 1, characterized in that, In step S5, the counterweight assembly after demolding is first desanded, then the surface of the counterweight assembly is cleaned, and then the counterweight assembly is milled, turned, drilled, cut, ground and polished.

5. The method for preparing an integrally molded large counterweight assembly according to claim 1, characterized in that: In step S6, the counterweight assembly undergoes dimensional measurement, hardness testing, and visual inspection to ensure that the product meets design specifications and quality standards.

6. The method for preparing an integrally molded large counterweight assembly according to claim 1, characterized in that, In step S7, the counterweight assembly is sprayed with anti-rust paint to improve its corrosion resistance and appearance.

7. The method for preparing an integrally molded large counterweight assembly according to claim 1, characterized in that, In step S2, based on the real-time monitored melting state and temperature distribution, an intelligent control method is used to automatically adjust the temperature of the electric arc furnace to achieve a more uniform and faster melting process. Then, by analyzing the gas composition and pressure changes generated during the melting process, bubble defects are predicted and eliminated in a timely manner, thereby improving the quality of the casting. The specific process is as follows: Step 1: Collect historical data and real-time monitoring data on melting state and temperature distribution. This data is acquired through sensors or monitoring equipment. The collected historical data is cleaned, denoised, and standardized to ensure data quality and consistency. Step 2: Extract features related to the melting state and temperature distribution from real-time monitoring data, including current, voltage, and temperature; Step 3: Using the preprocessed historical data as a training set, an improved regression analysis method is used to establish a predictive model for the smelting process. This model predicts the power and temperature of the electric arc furnace based on the characteristics of real-time monitoring data. Suppose we have a linear regression model y = β0 + β1x1 + β2x2 + ... + βnxn, where y is the target variable, representing the melting state and temperature distribution, x1, x2, ..., xn are the characteristic variables, representing current, voltage, and temperature, and β0, β1, ..., βn are the parameters to be determined; Given a historical dataset X = [x11, x12, ..., x1n, x21, x22, ..., x2n, ..., xm1,xm2, ..., xmn] and y = [y11, y12, ..., y1n, y21, y22, ..., y2n, ..., ym1, ym2,..., ymn], where m is the number of samples; The goal of the least squares method is to find a parameter estimate β^= (β^1, β^2, ..., β^n) that minimizes the mean square error: MSE = (1 / m)∑(yi - β^i*xi)^2; By solving the normal equation system |XTX|β^TXβ = I, where XTX is the transpose of the characteristic matrix multiplied by the characteristic matrix X, β^TX is the parameter matrix β multiplied by the transpose of the characteristic matrix X, and I is the identity matrix, the final parameter estimate β^ is obtained. Step 4: Evaluate the trained model using the test dataset. Based on the real-time monitored melting state and temperature distribution, input the real-time data into the trained model to obtain the prediction results. Then, automatically adjust the power and temperature of the electric arc furnace according to the prediction results to achieve a more uniform and faster melting process. Step 5: Further collect data on the composition and pressure changes of the gas generated during the melting process. This data is monitored and recorded in real time by sensors. Step 6: Extract features related to bubble defects from the collected data, including the concentration of gas components and the range of pressure variations; Step 7: Normalize the extracted features to facilitate subsequent model training and performance evaluation. Step 8: Based on existing research and application experience, select the logistic regression algorithm, take the preprocessed data as input, train the selected classification model, and optimize the model parameters through repeated iterations so that it can accurately distinguish between normal castings and castings with bubble defects. The goal of logistic regression is to predict the value of the target variable y based on the input features z1, z2, ..., zn. The input features include the concentration of gas components and the range of pressure variations. The formula for calculating logistic regression is shown below: h(z) = sigmoid(w0 + w1 * z1 + w2 * z2 + ... + wn * zn) Where w0, w1, ..., wn are the parameters of the model, which need to be learned and optimized through training data; z1, z2, ..., zn are the input features; sigmoid() is a non-linear function that maps h(z) to the interval (0, 1); During training, the goal is to minimize the loss function. For logistic regression, the cross-entropy loss function is expressed as L(w) = -[y * log(h(z)) + (1 - y) * log(1 - h(z))], where y is the true label, representing whether there is a bubble defect, and h(z) is the predicted value. The gradient descent optimization algorithm updates the parameters w based on the gradient of the loss function with respect to the model parameters, thereby continuously reducing the loss function and finally obtaining the optimal model parameters w0, w1, ..., wn, so that the model has the best performance on the training set. Step 9: Evaluate the performance of the trained model; Step 10: Deploy the trained model into the actual smelting system, monitor changes in gas composition and pressure in real time, and promptly eliminate bubble defects based on model predictions to improve the quality of castings.

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