Electrical round aluminum rod performance optimization method, system and equipment based on process optimization and response surface and medium
By constructing an annealing process model using response surface methodology, the annealing process parameters of electrical round aluminum rods were optimized, solving the problem of performance fluctuations in electrical round aluminum rods and improving resistivity, tensile strength, and elongation, thus meeting the requirements for efficient and stable operation of power systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2026-04-03
AI Technical Summary
Existing manufacturing processes for electrical round aluminum rods are difficult to achieve efficient and precise performance optimization, especially in terms of conductivity, strength and durability, and corrosion resistance. Traditional methods are based on experience and are difficult to meet the requirements of efficient and stable operation of power systems.
A regression model between the annealing process and resistivity, tensile strength and elongation was constructed using response surface methodology. The annealing process parameters were optimized, and the performance of the electrical round aluminum rod was improved by the optimal combination of process parameters.
The resistivity, tensile strength, and elongation of the electrical round aluminum rod were optimized, providing a more efficient production reference and offering performance improvement and stability assurance for the production of aluminum alloy materials in the power industry.
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of electrical round aluminum rod technology, and in particular relates to a method, system, equipment and medium for optimizing the performance of electrical round aluminum rods based on process optimization and response surface methodology. Background Technology
[0002] Aluminum and its alloys are widely used in power transmission, aerospace and transportation due to their low density, high strength, good ductility and excellent electrical conductivity, and they play an important role in the production of electrical round aluminum rods.
[0003] Electrical round aluminum poles are a common aluminum alloy product in the power industry, widely used in power transmission, substation construction, and cable production. The quality of electrical round aluminum poles directly affects the efficiency and safety of the power system; therefore, their performance requirements are high, mainly reflected in the following aspects.
[0004] First, electrical conductivity is one of the key performance characteristics of aluminum rods used in electrical engineering. To ensure efficient power transmission, the resistivity of the aluminum rod must be as low as possible. Although aluminum's conductivity is slightly lower than copper's, it can be improved to some extent by optimizing the composition and processing of aluminum alloys. Therefore, precise control and optimization of aluminum alloys are particularly important to meet the high-efficiency requirements in power transmission.
[0005] Secondly, strength and durability are also crucial performance characteristics of electrical round aluminum poles. During power transmission, aluminum poles need to withstand certain mechanical stresses, including those caused by wind, temperature changes, and physical impacts. Therefore, the tensile strength and ductility of aluminum poles must meet stringent standards. Excellent tensile strength and ductility ensure the reliability and durability of aluminum poles during long-term use, reduce the risk of failures due to material fatigue, and ensure the long-term stable operation of the power system.
[0006] Furthermore, corrosion resistance is also crucial for the performance of electrical round aluminum poles. Since aluminum poles are often exposed to outdoor environments, their corrosion resistance directly affects their service life. Although aluminum itself has good corrosion resistance, corrosion problems still exist in certain environments, such as high humidity and salt spray. Therefore, during the production process of aluminum alloys, it is essential to further optimize their corrosion resistance to ensure their reliability in harsh environments.
[0007] Finally, optimizing the manufacturing process is one of the key factors in improving the performance of electrical round aluminum rods. The manufacturing process of aluminum alloys has a significant impact on the resistivity, tensile strength, and ductility of aluminum rods. In particular, the annealing process, by controlling process parameters such as annealing temperature and time, can significantly improve the microstructure of aluminum alloys, thereby enhancing their overall performance. Optimizing the annealing process can not only improve the electrical conductivity of aluminum rods but also enhance their mechanical properties and extend their service life. Although aluminum alloys are widely used in the power industry, existing manufacturing processes and technologies still present some challenges, especially in how to further improve the performance of electrical round aluminum rods. Traditional optimization methods are often based on experience, making it difficult to achieve efficient and precise performance improvements. Furthermore, due to the complex composition of aluminum alloys, the relationship between the annealing process, the physical properties of aluminum rods, and production conditions is difficult to fully grasp, leading to significant fluctuations in product performance. Therefore, employing modern optimization techniques such as response surface methodology to scientifically analyze and optimize process parameters is an important direction for improving aluminum alloy performance and increasing production efficiency. Summary of the Invention
[0008] The purpose of this invention is to provide a method, system, device, and medium for optimizing the performance of electrical round aluminum rods based on process optimization and response surface methodology, in order to solve the problems existing in the prior art.
[0009] To achieve the above objectives, this invention provides a method for optimizing the performance of electrical round aluminum rods based on process optimization and response surface methodology, comprising:
[0010] A regression model is constructed based on response surface methodology to establish the relationship between the annealing process and resistivity, tensile strength, and elongation. The resistivity, tensile strength, and elongation values are used as optimization objectives to optimize the annealing process parameters, outputting the optimal combination of process parameters. Based on the optimal combination of process parameters, the performance optimization of the electrical round aluminum rod is performed.
[0011] Optionally, the annealing process includes annealing time and annealing temperature.
[0012] Optionally, the process of constructing the regression model specifically includes:
[0013] The optimization objective was determined, and a regression model was constructed based on the optimality criterion method, with annealing time and annealing temperature as input factors and resistivity, tensile strength and elongation as response factors.
[0014] Analysis of variance and regression analysis were performed on the regression model to test its significance.
[0015] Optionally, the regression model is specifically:
[0016] R1=30.1629+0.00276062A-0.0160062B-3.68301e-05AB+2.8268e-05A 2 +2.15033e-05B 2
[0017] R2=232.71+3.13863A-0.587275B-0.00800196AB-0.0699608A 2 +0.000858039B 2
[0018] R3=8.35809-1.19713A+0.00482353B+0.00450588AB+0.0111324A2-5.41176e-05B 2
[0019] In the formula, R1, R2, and R3 are response variables, namely resistivity, tensile strength, and elongation; A and B represent annealing time and annealing temperature, respectively.
[0020] Optionally, the optimization objective specifically includes:
[0021] The resistivity, tensile strength, and elongation values are used as optimization targets, and the model is set to minimize resistivity and maximize tensile strength and elongation.
[0022] A performance optimization system for electrical round aluminum rods based on process optimization and response surface methodology includes:
[0023] The model building module is used to construct a regression model between the annealing process and resistivity, tensile strength and elongation based on the response surface methodology.
[0024] The performance optimization module is used to optimize the annealing process parameters with resistivity, tensile strength, and elongation as optimization targets, output the optimal combination of process parameters, and perform performance optimization of the electrical round aluminum rod based on the optimal combination of process parameters.
[0025] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the aforementioned method for optimizing the performance of an electrical round aluminum rod based on process optimization and response surface methodology.
[0026] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for optimizing the performance of electrical round aluminum rods based on process optimization and response surface methodology.
[0027] The technical effects of this invention are as follows:
[0028] This invention optimizes the resistivity, tensile strength, and elongation of electrical round aluminum rods. By using response surface methodology combined with annealing process parameters, the final optimized combination of process parameters with minimum resistivity and maximum tensile strength and elongation provides an important reference for the production of aluminum alloy materials in the power industry. Attached Figure Description
[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0030] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:
[0031] Figure 1 This is a conceptual diagram of the residual normal distribution in an embodiment of the present invention;
[0032] Figure 2 This is a residual fitting plot in an embodiment of the present invention;
[0033] Figure 3 This is a graph showing the actual and predicted values in an embodiment of the present invention;
[0034] Figure 4 This illustrates the relationship between the test order and the residuals in the embodiments of the present invention.
[0035] Figure 5 This illustrates the effect of the interaction between the two intermediate-level annealing parameters on resistivity in this embodiment of the invention.
[0036] Figure 6 This is the response surface of the effect of annealing parameters changing from the lowest to the highest level in the embodiments of the present invention on resistivity;
[0037] Figure 7 The interaction between the two intermediate-level annealing parameters in this embodiment of the invention affects the tensile strength.
[0038] Figure 8 This is the response surface of the effect on tensile strength when the annealing parameters in the embodiments of the present invention change from the lowest to the highest level;
[0039] Figure 9 This illustrates the effect of the interaction between the two intermediate-level annealing parameters on elongation in this embodiment of the invention.
[0040] Figure 10This is a response surface showing the effect of annealing parameters changing from the lowest to the highest level on elongation in this embodiment of the invention.
[0041] Figure 11 This refers to the distribution range of satisfaction values in the embodiments of the present invention;
[0042] Figure 12 This is a flowchart illustrating the implementation of an embodiment of the present invention. Detailed Implementation
[0043] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.
[0044] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.
[0045] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be obvious to those skilled in the art. This application specification and embodiments are merely exemplary.
[0046] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.
[0047] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0048] like Figure 1 - Figure 12 As shown, this embodiment provides a method for optimizing the performance of electrical round aluminum rods based on process optimization and response surface methodology. The method includes: constructing a regression model between the annealing process and resistivity, tensile strength, and elongation using response surface methodology; optimizing the annealing process parameters using resistivity, tensile strength, and elongation values as optimization targets; outputting the optimal combination of process parameters; and performing performance optimization of the electrical round aluminum rod based on the optimal combination of process parameters. The technical solution described in this embodiment achieves performance optimization of electrical round aluminum rods by optimizing the annealing process, providing an important reference for the production of aluminum alloy materials in the power industry.
[0049] The specific implementation process of this embodiment includes:
[0050] Model Construction: Response surface methodology (RSM) is used to optimize experimental designs or establish relationships between indicators and factors, providing functional relationships between them. Because RSM yields continuous functional relationships, while orthogonal methods only optimize discontinuous points, its advantage in optimizing experimental design is significant. In practical processing, the objective function to be approximated is usually nonlinear. Therefore, for general response surface functions, a second-order fitting model is often used, and the least squares method is employed to solve for the coefficients in the expression. Then, response surface analysis is performed on the resulting surface. Typically, the expression for the second-order polynomial of the response surface is taken as:
[0051]
[0052] Where Y(X) is the predicted response value; a0, a i b i —Coefficients of constant terms, linear terms, and second-order terms; c ij — Cross-effect coefficient of influence factors; n — Number of influence factors in the experiment.
[0053] Experimental Design and Results: Response surface methodology (RSM) experimental designs include central composite design, Box design, quadratic saturation design, and Taguchi design. These methods are used for regression analysis, and their accuracy can be evaluated using the variance of the predicted values. This experiment employs the D-optimal design method based on the optimality criterion, minimizing the joint confidence region of parameter estimates. This approach is suitable for scenarios prioritizing parameter estimation accuracy and is applicable to situations with limited experimental space, asymmetric factors, or non-standard factor levels (traditional designs such as CCD or Box-Behnken may not be directly applicable). Customizable number of experiments is supported (e.g., minimum number of experiments under budget constraints). Experimental results are shown in Table 2.
[0054] Based on the experimental results, the next steps will involve evaluating the regression coefficients, performing analysis of variance, conducting significance tests on the regression models, and testing the significance and lack of fit of individual model coefficients for the second-order models of resistivity, tensile strength, and elongation.
[0055] Table 1 Measurement Results
[0056]
[0057] Establishing a response surface model: Using RSM regression fitting, a model is established between the response parameter y and the input parameter x. iThe regression model established in this paper uses A and B as input parameters, which are represented by in the formula, respectively. Resistivity, tensile strength, and elongation are the response values. Taking the hierarchical nature of the model terms as a premise, the stepwise regression method is used to automatically remove unimportant model terms, and the resulting standard quadratic regression model is as follows:
[0058] R1=30.1629+0.00276062A-0.0160062B-3.68301e-05AB+2.8268e-05A 2 +2.15033e-05B 2
[0059] R2=232.71+3.13863A-0.587275B-0.00800196AB-0.0699608A 2 +0.000858039B 2
[0060] R3=8.35809-1.19713A+0.00482353B+0.00450588AB+0.0111324A 2 -5.41176e-05B 2
[0061] In the formula, R1, R2, and R3 are response variables, namely resistivity, tensile strength, and elongation; A and B represent annealing time and annealing temperature, respectively.
[0062] Analysis of variance and regression coefficient analysis: To clarify the significance of each factor's influence on resistivity, tensile strength, and elongation, ANOVA was used for evaluation. Tables 2-4 list the results of the analysis of variance for resistivity, tensile strength, and elongation. A probability p < 0.05 indicates that the obtained response function is valid.
[0063] Table 2 Results of resistivity variance analysis
[0064]
[0065] Table 3. Results of Tensile Strength Variance Analysis
[0066]
[0067] Table 4 Results of Elongation ANOVA
[0068]
[0069] When the probability P is less than 0.05, the obtained response function is feasible, and the ANOVA analysis results for the resistivity, tensile strength, and elongation of the corresponding surface are shown in the table above. The calculated results are consistent with the experimental data, indicating high reliability. As the probability P decreases and the test statistic F increases, the influence of the input parameters on the response becomes more significant. Setting the confidence interval to 95%, a P value less than 0.05 indicates a significant influence of this factor, while a P value less than 0.0001 indicates an extremely significant influence. The table shows that the F values of the standard quadratic regression model for resistivity, tensile strength, and elongation are 384.85, 232.22, and 1456.97, respectively, with P values all less than 0.0001. The coefficient of variation, the ratio of standard deviation to mean, is a normalized measure reflecting the dispersion of the model. The coefficients of variation for the quadratic regression model in this paper are 0.0542, 1.78, and 1.82, <10%. The multivariate correlation coefficient R0... 2 It is a commonly used metric for testing predictive models, comparing the degree of match between predicted results and actual conditions. Resistivity, tensile strength, and elongation models have an R-value of [missing information]. 2 =0.9948, 0.9915, 0.9986 > 0.9, indicating a strong consistency between the predicted values and the actual values based on the response surface methodology. The adjustment coefficients are (adj)R² = 0.9922, 0.9872, 0.9979 and (Pre)R², respectively. 2 =0.9842, 0.9649, 0.9952. The difference between the adjusted and predicted coefficients corresponding to the three response values is less than 0.2, proving that the response surface model has reached an extremely significant level and has good fitting accuracy. The response surface model can be used for subsequent prediction and optimization design. This also indicates that the response surface model has reached an extremely significant level and has good fitting accuracy, and can be used for subsequent prediction and optimization design of resistivity, tensile strength, and elongation.
[0070] Figure 1 This is a concept graph of the residual normality for resistivity, tensile strength, and elongation. The residual level is also an evaluation criterion reflecting the quality of the mathematical model. The closer the residual normality distribution graph is to a straight line, the denser the observations are near the regression line, and the better the fit. The residual normality concept graph shows that the points are distributed along a sloping straight line, indicating that the analysis results are approximately normally distributed, that is, the residuals follow a normal distribution. Figure 2 The residual fitting plot shows that the residual points are basically randomly distributed around the zero line, with no unstable variance points, missing points, or outliers. Figure 3The graph shows the relationship between actual and predicted values. It reveals that most of the analyzed data points are concentrated on a straight line with a 45-degree slope, with only a few being discrete, indicating that the actual and predicted values are very close. Therefore, the fitting equation obtained based on the response surface methodology can effectively predict resistivity, tensile strength, and elongation, demonstrating the sufficient reliability of the fitted model. Ideally, the expected value should be the same as the actual value, and all points should lie on a straight line with a slope of 1. However, due to limitations in the form of the fitting equation and computational capabilities, a perfect fit cannot be achieved. Figure 4 In the relationship between the test order and the residuals, the residual points are almost all randomly distributed around the zero line, without any correlation. This demonstrates the reliability of the analysis of variance. Furthermore, the insignificance of the lack of fit reflects that the quadratic regression model can effectively predict resistivity, tensile strength, and elongation.
[0071] Response surface methodology: While analysis of variance and regression models can reveal the strength of each influencing factor, they cannot show the trend of their impact. Response surface methodology, on the other hand, allows us to observe the trends in the effects of each factor. For example... Figure 5 As shown, the graph illustrates the effect of the interaction between the two parameters on resistivity, tensile strength, and elongation when the two factors are at their intermediate levels.
[0072] Figure 6 The response surface illustrates the effect of changes in resistivity on A and B from their lowest to highest levels. When B is at a low level, as A increases, the resistivity gradually decreases. As B increases, the initial resistivity decreases further with increasing A, and the rate of decrease gradually increases, reaching a minimum at the high levels of both A and B. When A is at a low level, as B increases, the resistivity also decreases, but the rate of decrease is smaller. When A is at a high level, as B increases, the resistivity also decreases, but at a greater rate. The graph shows a relatively steep overall surface, indicating that the trend is not unidirectional and that A and B have a significant interaction. The curvature of the response surface is more biased towards the B axis. The contour plots of A and B show that the major axis of the ellipse formed by the contour lines is significantly tilted relative to the coordinate axes. The curves intersecting the B axis have a higher density than the curves along the A axis, further confirming the significant interaction between A and B in the analysis of variance. The minimum resistivity occurs when A and B are relatively large, and the maximum resistivity occurs when A and B are relatively small.
[0073] Figure 8The diagram shows the response surface and contour plot of the effect of A and B varying from their lowest to highest levels on tensile strength. For any value of B, as A increases, the tensile strength gradually decreases from a larger value, with a greater slowdown at higher B levels. When A is low, the change in tensile strength is gradual with increasing B, but decreases significantly with increasing A content. Tensile strength is higher at lower values of A and B. The diagram shows a relatively steep overall surface with large curvature variations, indicating a significant interaction between A and B. The curvature of the response surface is more biased towards the B axis. The contour plots of A and B show that the curves intersecting at the B axis have a higher density than those intersecting at the A axis, further validating the significant interaction between A and B in the analysis of variance.
[0074] Figure 10 The response surface shows the effect of changes in A and B from their lowest to highest levels on elongation. For any value of B, as A increases from low to high, the elongation gradually increases. As B moves towards a higher level, the initial elongation value increases with increasing A, and the rate of increase gradually increases. When A is at a low level, the elongation increases slightly when B increases from low to high. When A is at a high level, the elongation increases significantly when B increases from low to high, with a much higher growth rate than before. As can be seen from the figure, the overall surface is very steep and the trend of change is complex. A and B have a significant nonlinear effect on the elongation. There is a clear interaction between A and B. The curvature of the response surface is slightly biased towards the B axis. From the contour plots of A and B, it can be seen that the major axis of the ellipse enclosed by the contour lines and the coordinate axes are significantly tilted. The curves in the figure intersect at the curve density of the A axis which is greater than that of the B axis, which also verifies that the interaction between A and B is significant in the analysis of variance. The maximum elongation is at the maximum values of A and B, and the minimum elongation is at the minimum value of AB.
[0075] Parameter Optimization: Two process parameters were optimized, with resistivity, tensile strength, and elongation as the optimization targets. The optimization mode was set to minimize resistivity and maximize tensile strength and elongation. The figure shows the final optimized results for resistivity, tensile strength, and elongation. Analysis shows that the changes in resistivity, tensile strength, and elongation of the two process parameters exhibit non-linear behavior. The reasonable results were selected from the optimized parameters. The optimal combination of process parameters is shown on the horizontal line at the high satisfaction and optimal response values. Optimal process parameter group: A-27.630, B-250.000. The predicted optimal resistivity is 27.349 nΩ·m, tensile strength is -117.557 MPa, and elongation is -12.728%.
[0076] In summary, this embodiment optimized the resistivity, tensile strength, and elongation of electrical round aluminum rods. By using response surface methodology combined with annealing process parameters, the final optimized combination of process parameters with minimum resistivity and maximum tensile strength and elongation was obtained, providing an important reference for the production of aluminum alloy materials in the power industry.
[0077] This embodiment provides a performance optimization system for electrical round aluminum rods based on process optimization and response surface methodology, comprising:
[0078] The model building module is used to construct a regression model between the annealing process and resistivity, tensile strength and elongation based on the response surface methodology.
[0079] The performance optimization module is used to optimize the annealing process parameters with resistivity, tensile strength, and elongation as optimization targets, output the optimal combination of process parameters, and perform performance optimization of the electrical round aluminum rod based on the optimal combination of process parameters.
[0080] In practice, this embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the described method for optimizing the performance of an electrical round aluminum rod based on process optimization and response surface methodology.
[0081] In practice, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for optimizing the performance of electrical round aluminum rods based on process optimization and response surface methodology.
[0082] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for performance optimization of electrical round aluminum rods based on process optimization and response surface methodology, characterized in that, include: A regression model is constructed based on response surface methodology to establish the relationship between the annealing process and resistivity, tensile strength, and elongation. The resistivity, tensile strength, and elongation values are used as optimization objectives to optimize the annealing process parameters, outputting the optimal combination of process parameters. Based on the optimal combination of process parameters, the performance optimization of the electrical round aluminum rod is performed.
2. The method according to claim 1, characterized in that, The annealing process includes annealing time and annealing temperature.
3. The method according to claim 2, characterized in that, The process of constructing the regression model specifically includes: The optimization objective was determined, and a regression model was constructed based on the optimality criterion method, with annealing time and annealing temperature as input factors and resistivity, tensile strength and elongation as response factors. Analysis of variance and regression analysis were performed on the regression model to test its significance.
4. The method according to claim 3, characterized in that, The regression model is specifically as follows: R1=30.1629+0.00276062A-0.0160062B-3.68301e-05AB+2.8268e-05A 2 +2.15033e-05B 2 R2=232.71+3.13863A-0.587275B-0.00800196AB-0.0699608A 2 +0.000858039B 2 R3=8.35809-1.19713A+0.00482353B+0.00450588AB+0.0111324A2-5.41176e-05B 2 In the formula, R1, R2, and R3 are response variables, namely resistivity, tensile strength, and elongation; A and B represent annealing time and annealing temperature, respectively.
5. The method according to claim 3, characterized in that, The optimization objectives specifically include: The resistivity, tensile strength, and elongation values are used as optimization targets, and the model is set to minimize resistivity and maximize tensile strength and elongation.
6. A performance optimization system for electrical round aluminum rods based on process optimization and response surface methodology, characterized in that, include: The model building module is used to construct a regression model between the annealing process and resistivity, tensile strength and elongation based on the response surface methodology. The performance optimization module is used to optimize the annealing process parameters with resistivity, tensile strength, and elongation as optimization targets, output the optimal combination of process parameters, and perform performance optimization of the electrical round aluminum rod based on the optimal combination of process parameters.
7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform a method for optimizing the performance of an electrical round aluminum rod based on process optimization and response surface according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a method for optimizing the performance of an electrical round aluminum rod based on process optimization and response surface as described in any one of claims 1-5.