Investment casting parameter directional optimization method and system

Abstract

The invention relates to a directional optimization method and system for investment casting parameters, and relates to the field of casting optimization. A quality monitoring database, mapped with a current parameter set, of an investment casting workpiece is established, a directional optimization target is configured, and a target classification identifier in the directional optimization target is determined; after key optimization parameters are selected through the main optimization target and the auxiliary optimization target, directional optimization target influence analysis of the key optimization parameters is executed, parameter directional optimization is executed after parameter optimization constraints are established according to the directional target optimization influence analysis result, and investment casting parameter directional optimization is completed through the parameter directional optimization result. The technical problems that investment casting parameter optimization is lack of systematicness and comprehensiveness, global optimization is difficult to achieve to improve the casting quality and production efficiency, the production cost is reduced, and the requirement of the market for high-quality castings is met are solved.

Description

Technical Field

[0001] The present invention relates to the field of casting optimization, and particularly to a method and system for directionally optimizing investment casting parameters. Background Art

[0002] As a precision casting method, investment casting is widely used in many fields such as aerospace, petrochemical, and medical devices because it can produce castings with complex shapes, precise dimensions, and smooth surfaces. However, the investment casting process involves numerous parameters, such as wax pattern making parameters, melting parameters, pouring parameters, and subsequent heat treatment parameters. Minor changes in these parameters may have a significant impact on the quality of the final casting.

[0003] In the traditional investment casting process, the adjustment and optimization of parameters often rely on experience and the judgment of skilled workers. This method is not only inefficient but also difficult to ensure the stability and consistency of casting quality. With the continuous development of computer technology and optimization algorithms, people have begun to try to apply advanced information technology means to the parameter optimization of investment casting to achieve automation, intelligence, and precision in the casting process. Although there have been some studies and practices on the parameter optimization of investment casting, most of these methods focus on the adjustment of single parameters or the optimization of simple parameter combinations, lacking systematic and comprehensive considerations. In addition, these methods often ignore the mutual influence and restrictive relationship between different optimization objectives, resulting in the optimization results being difficult to achieve the global optimum. Summary of the Invention

[0004] Aiming at the technical problems in the prior art that the parameter optimization of investment casting lacks systematicness and comprehensiveness, it is difficult to achieve the global optimum to improve the quality and production efficiency of castings, reduce production costs, and meet the market demand for high-quality castings, the present invention provides a method and system for directionally optimizing investment casting parameters to solve these problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: In the first aspect, the present invention provides a method for directionally optimizing investment casting parameters. The method includes: obtaining the current parameter set of investment casting and establishing a quality monitoring database of investment casting workpieces mapped to the current parameter set; configuring directionally optimized objectives according to the quality monitoring database, where the directionally optimized objectives include porosity optimization, shell film thickness uniformity optimization, casting shrinkage rate optimization, and thermal stress crack optimization; determining the target classification identifiers in the directionally optimized objectives, where the target classification identifiers include main optimization objectives, auxiliary optimization objectives, and maintenance objectives; after selecting key optimization parameters using the main optimization objectives and auxiliary optimization objectives, performing an impact analysis of the directionally optimized objectives of the key optimization parameters; after establishing parameter optimization constraints according to the impact analysis results of the directionally optimized objectives, performing parameter directional optimization, and completing the directional optimization of investment casting parameters using the results of the parameter directional optimization.

[0006] In a second aspect, the present invention provides a system for directionally optimizing investment casting parameters. The system includes: a parameter acquisition module, configured to acquire the current parameter set of investment casting and establish a quality monitoring database of investment casting workpieces mapped to the current parameter set; a target configuration module, configured to configure a directional optimization target according to the quality monitoring database, where the directional optimization target includes pore optimization, shell film thickness uniformity optimization, casting shrinkage rate optimization, and thermal stress crack optimization; a classification identification module, configured to determine a target classification identification in the directional optimization target, where the target classification identification includes a main optimization target, an auxiliary optimization target, and a maintenance target; an impact analysis module, configured to perform an impact analysis of the directional optimization target of the key optimization parameters after selecting the key optimization parameters by using the main optimization target and the auxiliary optimization target; and a directional optimization module, configured to perform parameter directional optimization after establishing parameter optimization constraints according to the impact analysis result of the directional target optimization, and complete the directional optimization of investment casting parameters by using the result of the parameter directional optimization.

[0007] The beneficial effects of the present invention are as follows: By directionally optimizing the investment casting parameters, comprehensively considering multiple optimization targets such as pores, shell film thickness uniformity, casting shrinkage rate, and thermal stress cracks, and classifying and processing different optimization targets, the key parameters are accurately selected for impact analysis. Finally, the precise adjustment of investment casting parameters is realized by using the optimization result, significantly improving the quality of castings and enhancing production efficiency and stability. Description of the Drawings

[0008] Figure 1 It is a schematic flow chart of a method for directionally optimizing investment casting parameters provided by the present invention.

[0009] Figure 2 It is a schematic structural diagram of a system for directionally optimizing investment casting parameters provided by the present invention.

[0010] Description of the reference numerals: parameter acquisition module 11, target configuration module 12, classification identification module 13, impact analysis module 14, directional optimization module 15. Detailed Embodiments

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0014] Embodiment 1: As Figure 1 shown, the embodiment of the present invention provides a method for directional optimization of investment casting parameters, and the method includes: S10: Obtain the current parameter set of investment casting and establish a quality monitoring database of the investment casting workpiece mapped to the current parameter set.

[0015] Among them, the monitoring indicators of the quality monitoring database include porosity, pore distribution, pore size, shell film thickness distribution, thickness standard deviation, shrinkage rate distribution, crack morphology, and crack size.

[0016] Exemplarily, investment casting is a precision casting technology, and its core equipment includes a wax injection machine for making wax patterns; an intermediate frequency induction furnace for metal melting; a pouring device for injecting molten metal into the wax pattern; and a heat treatment furnace for post-treatment of castings. The basic process of investment casting is as follows: First, use a wax injection machine to inject molten wax into a metal mold, and after cooling, a wax pattern is formed; then coat the surface of the wax pattern with multiple layers of refractory coatings and harden to form a shell film; then melt out the wax pattern in the shell film to leave a cavity; subsequently, inject the molten metal into the cavity through a pouring device, and after cooling and solidification, a casting is formed; finally, post-treatment processes such as cleaning and heat treatment are performed on the casting to obtain the final product. The accuracy of the equipment and the control of the process play a crucial role in the quality of the casting throughout the process.

[0017] In this solution, in order to achieve precise control and optimization of casting quality, it is necessary to comprehensively obtain the current investment casting parameter set. These parameters cover all aspects from wax pattern making to metal pouring and subsequent processing, such as wax pattern forming temperature, metal melting temperature, pouring speed, and heat treatment process parameters. Subsequently, a quality monitoring database mapped to these parameters is established. This database details the key quality indicators of investment casting workpieces under each set of parameter combinations, including porosity (e.g., the porosity of a certain casting is less than 0.5%), pore distribution (uniform dispersion or concentration in specific areas), pore size (from tiny to a few micrometers), shell mold thickness distribution (uniformity, such as the average thickness deviation not exceeding 0.1 mm), thickness standard deviation (reflecting the consistency of shell mold thickness), shrinkage rate distribution (whether the shrinkage of each part of the casting is uniform), crack morphology (such as linear cracks or reticular cracks), and crack size (tiny to almost invisible). Through such a quality monitoring database, the specific impact of different parameter combinations on casting quality can be intuitively understood, providing a data basis for subsequent optimization work. For example, when it is found that the porosity of a casting is relatively high under a certain set of parameters, relevant parameters can be adjusted according to the records in the database, such as reducing the pouring speed or optimizing the exhaust design of the wax pattern, in order to reduce porosity and improve the quality of the casting.

[0018] S20: Configure the directional optimization objectives according to the quality monitoring database. The directional optimization objectives include porosity optimization, shell mold thickness uniformity optimization, casting shrinkage rate optimization, and thermal stress crack optimization.

[0019] Optionally, a series of directional optimization objectives are further configured based on the established quality monitoring database. These objectives serve to guide the improvement direction of the subsequent casting process. Specifically, the optimization objectives cover porosity optimization, aiming to reduce the number and size of pores inside the casting, such as reducing the porosity from the original 0.8% to below 0.5% to ensure the density of the casting; shell mold thickness uniformity optimization, for the pursuit of precise control of the shell mold thickness, with its standard deviation not exceeding 0.05 mm to ensure the consistency of the casting surface quality; casting shrinkage rate optimization, that is, by adjusting the casting parameters to make the shrinkage rate of each part of the casting more uniform, avoiding deformation and cracks, such as controlling the overall shrinkage rate within ±0.2% of the predetermined value; and thermal stress crack optimization, aiming to reduce or eliminate cracks caused by thermal stress, especially for large and complex castings, such as reducing the crack length to almost invisible through precise heat treatment process control. By setting optimization objectives for these key quality indicators, the process parameters of investment casting can be adjusted targeted, thereby significantly improving the overall quality of the casting and meeting the manufacturing requirements of high precision and high performance.

[0020] S30: Determine the target classification identifier in the directional optimization objective. The target classification identifier includes the main optimization objective, the auxiliary optimization objective, and the maintenance objective.

[0021] Specifically, to ensure the comprehensiveness and efficiency of the optimization process, the directional optimization objectives are classified and identified. First, the main optimization objective is clarified, which is the core point in the optimization task and represents the most critical and urgent quality problem to be solved currently. For example, in a specific project, if the high porosity is the main factor affecting the performance of the casting, then reducing the porosity will be set as the main optimization objective, and thus resources and efforts will be concentrated to significantly reduce the number and size of pores.

[0022] Meanwhile, auxiliary optimization objectives are set. Although these objectives are not as urgent as the main optimization objective, they are also indispensable factors for improving the quality of the casting. As secondary optimization objectives, they assist in the realization of the main optimization objective. For example, while reducing the porosity, the uniformity of the shell mold thickness will also be concerned to ensure it is within a reasonable deviation range to improve the surface quality and dimensional accuracy of the casting.

[0023] In addition, retention objectives are identified. These objectives aim to keep the existing quality indicators at an acceptable level and avoid unnecessary negative impacts caused by the adjustment of the main and auxiliary optimization objectives. For example, during the optimization process, ensure that the shrinkage rate of the casting remains within a stable range to prevent the casting from deforming or generating new cracks due to excessive pursuit of other objectives.

[0024] Through the classification and identification of objectives, the optimization process can be more precisely guided to ensure that various quality indicators are reasonably balanced during the optimization, and ultimately achieve an overall improvement in the investment casting process.

[0025] S40: After selecting the key optimization parameters using the main optimization objective and the auxiliary optimization objective, perform an impact analysis of the directional optimization objective on the key optimization parameters.

[0026] Preferably, in the key link of investment casting parameter optimization, first screen out the key optimization parameters that have the most significant impact on the casting quality according to the main optimization objective and the auxiliary optimization objective. These parameters are like sensitive switches affecting the casting quality, and small adjustments to them may bring a significant improvement in the casting quality. For example, if the main optimization objective is to reduce the porosity of the casting, the exhaust design of the wax pattern, the pouring temperature, and the chemical composition of the metal melting may be selected as the key optimization parameters because they directly affect the formation and number of pores.

[0027] After selecting the key parameters, the next step is to perform a directional optimization target impact analysis on the key optimization parameters. This step involves using advanced simulation software and experimental data to deeply explore the specific impacts of each key parameter on the main optimization target and auxiliary optimization targets. For example, by adjusting the exhaust design of the wax pattern, it can be observed how the porosity changes accordingly; at the same time, attention will also be paid to the possible chain reactions that this adjustment may have on other quality indicators such as the uniformity of the shell film thickness and the casting shrinkage rate. Through such impact analysis, a relationship map between each key parameter and the casting quality can be clearly outlined, providing a scientific basis for subsequent optimization decisions.

[0028] S50: After establishing the parameter optimization constraints according to the results of the directional target optimization impact analysis, perform parameter directional optimization, and use the results of the parameter directional optimization to complete the directional optimization of the investment casting parameters.

[0029] Specifically, after completing the directional optimization target impact analysis of the key optimization parameters, based on these analysis results, construct the constraints for parameter optimization. These constraints mean setting boundaries for parameter adjustment to ensure that the expected goals are not deviated from during the optimization process. For example, if it is found that a certain key parameter has a significant impact on the porosity but also affects the uniformity of the shell film thickness, then set a constraint to keep the change in the shell film thickness within an acceptable range while reducing the porosity.

[0030] Next, perform parameter directional optimization. This process uses advanced optimization algorithms to automatically search for the optimal parameter combination under the set constraints. That is, to find the best parameter combination, the algorithm will continuously try different parameter combinations, evaluate their impacts on the optimization target, and gradually approach the optimal solution. Finally, use the results of the parameter directional optimization to complete the directional optimization of the investment casting parameters. These optimized parameters are applied to actual production and can significantly improve the quality of the castings. For example, through the optimized parameter combination, the porosity of the casting may be successfully reduced by 30% while maintaining the uniformity of the shell film thickness, thus greatly improving the density and surface quality of the casting and meeting the customer's requirements for high-quality castings.

[0031] In a preferred embodiment, after selecting the key optimization parameters by using the main optimization objective and the auxiliary optimization objective, perform an impact analysis of the directional optimization objective of the key optimization parameters, including: obtaining a set of adjustable parameters for investment casting; performing a quantitative mapping of the impact degree of the main optimization objective and the auxiliary optimization objective on the set of adjustable parameters; constructing an objective impact matrix for all optimization objectives in the directional optimization objective, where the objective impact matrix characterizes the mutual relationship between different optimization objectives, and the mutual relationship includes a positive promotion relationship and a negative conflict relationship; calculating the sensitivity coefficient of the set of adjustable parameters according to the quantitative mapping of the impact degree and the objective impact matrix; establishing an impact analysis result of the directional objective optimization according to the calculation result of the sensitivity coefficient.

[0032] In a specific embodiment, focus on using the main optimization objective and the auxiliary optimization objective to accurately select the key optimization parameters. Obtain a set of adjustable parameters for investment casting. These parameters are like variable factors in the casting process, and their adjustment will directly affect the quality of the casting. Then, perform a quantitative mapping of the impact degree of the main optimization objective and the auxiliary optimization objective on the set of adjustable parameters. This process is like attaching an impact label to each parameter to clarify their contribution to each optimization objective.

[0033] To more comprehensively understand the relationship between the parameters and the optimization objectives, construct an objective impact matrix for all optimization objectives in the directional optimization objective. This matrix is like a network of relationships, clearly showing the positive promotion relationship and the negative conflict relationship between different optimization objectives. For example, reducing the porosity rate may have a positive promotion relationship with improving the uniformity of the shell film thickness, while having a negative conflict relationship with reducing thermal stress cracks. Furthermore, based on these quantitative mappings of the impact degree and the objective impact matrix, further calculate the sensitivity coefficient of the set of adjustable parameters. By calculating the sensitivity coefficient, it is possible to determine which key optimization parameters have the greatest impact on the objective, so as to adjust the weight of parameter optimization during the optimization process and prevent excessive or ineffective parameter adjustment.

[0034] Finally, establish an impact analysis result of the directional objective optimization according to the calculation result of the sensitivity coefficient. These results will serve as an important basis for subsequent parameter directional optimization and optimization. For example, if it is found that a certain parameter is extremely sensitive to the porosity rate, then this parameter will be adjusted more carefully during the optimization process to ensure that while reducing the porosity rate, it will not have an adverse impact on other optimization objectives. Through such a process, the directional optimization of investment casting parameters can be achieved more scientifically and accurately.

[0035] For example, there is currently an investment casting project that mainly focuses on the following three optimization objectives: The main optimization objective is to reduce the porosity (target value: reduced from the current 1.2% to below 0.8%); the secondary optimization objective is to improve the uniformity of the shell mold thickness (target value: the standard deviation of the thickness is reduced from 0.15 mm to below 0.1 mm); the maintenance objective is to maintain the casting shrinkage rate within ±0.3%. Subsequently, the following three adjustable parameters were selected for analysis. Among them, Parameter A: wax pattern exhaust design (value range: 1 - 5, the larger the value, the more optimized the exhaust design); Parameter B: pouring temperature (value range: 1500 - 1600 °C, step size 10 °C); Parameter C: metal melting time (value range: 30 - 60 minutes, step size 5 minutes). Subsequently, a quantization mapping table of the influence degree as shown in Table 1 was obtained (the value represents the change amount of the optimization objective when the parameter is adjusted by one unit): Table 1: Quantization Mapping Table of Influence Degree Furthermore, an objective influence matrix as shown in Table 2 was constructed to show the mutual relationship between the optimization objectives (simplified to a linear relationship here, which may be more complex in reality): Table 2: Objective Influence Matrix Table Next, calculate the sensitivity coefficient of each parameter according to the influence degree quantization mapping and the target influence matrix (here, the simple weighted sum method is used, and the weights are set according to the importance of the optimization objectives. Assume that the weight of the porosity is 0.6, the weight of the shell film thickness uniformity is 0.3, and the weight of the shrinkage rate is 0.1). Then, the sensitivity coefficient of parameter A = 0.6×(-0.05) + 0.3×(-0.01) + 0.1×0.02 = -0.031; the sensitivity coefficient of parameter B = 0.6×(-0.03) + 0.3×0.02 + 0.1×(-0.01) = -0.017; the sensitivity coefficient of parameter C = 0.6×0.01 + 0.3×(-0.005) + 0.1×0.005 = 0.004. According to the calculation results of the sensitivity coefficients, the following conclusions can be drawn: Parameter A has the greatest impact on the optimization objective and is a negative impact, that is, increasing the value of parameter A (optimizing the exhaust design) can significantly reduce the porosity, and at the same time, it also helps to improve the shell film thickness uniformity, but it may slightly increase the shrinkage rate. The impact of parameter B is the second, and it is also a negative impact. However, when adjusting, the relationship between the reduction of porosity and the improvement of shell film thickness uniformity needs to be weighed. The impact of parameter C is the smallest and is a positive impact, but the contribution to the optimization objective is limited when adjusting. Based on the above analysis, parameter A (wax pattern exhaust design) can be adjusted preferentially to significantly reduce the porosity and improve the shell film thickness uniformity. At the same time, parameter B (pouring temperature) can be appropriately adjusted to further optimize the porosity and shell film thickness uniformity, but attention needs to be paid to controlling the change of the shrinkage rate. The adjustment of parameter C (metal melting time) has a limited contribution to the optimization objective and can be fine-tuned after other parameters are adjusted in place.

[0036] In a preferred embodiment, the execution of parameter directional optimization includes: after establishing the control interval of the parameters, creating an initial solution set based on the current parameter set; after performing the solution fitness evaluation within the initial solution set, establishing the optimization direction and optimization step size through the parameter optimization constraints and the fitness evaluation results; using the optimization direction and the optimization step size to iteratively update the initial solution set; and completing the parameter directional optimization according to the iterative update results.

[0037] Preferably, during the process of parameter-oriented optimization, reasonable control intervals are set for each parameter. These intervals, like the "activity ranges" of the parameters, ensure the effectiveness and safety of parameter adjustment. Then, an initial solution set is created based on the current parameter set. This solution set is like the "starting point" of parameter adjustment and contains multiple possible parameter combination schemes. Next, a fitness evaluation is performed on each solution in the initial solution set. This step is like "scoring" each scheme to evaluate their satisfaction with the optimization goal. The optimization direction and optimization step size are established through the parameter optimization constraints and the fitness evaluation results. The parameter optimization constraints ensure the coordination of multi-objective optimization, making the optimization direction meet the requirements of the main optimization goal while taking into account the auxiliary optimization goal and the maintenance goal, and avoiding the phenomenon of being one-sided in the optimization process.

[0038] Subsequently, the initial solution set is iteratively updated using the optimization direction and the optimization step size. This process is a process of "gradually approaching" the optimal solution. In each iteration, the parameter combination is adjusted according to the optimization direction, and the adjustment amplitude is controlled according to the optimization step size to ensure that the parameter adjustment is both effective and stable. The parameter-oriented optimization is completed according to the iterative update results, thereby giving the optimal parameter combination scheme, which can significantly improve the quality of investment casting. For example, in a specific project, through parameter-oriented optimization, the porosity is successfully reduced by 20%, while the uniformity of the shell film thickness is improved, and the shrinkage rate of the casting is maintained stable, thus greatly improving the overall performance and market competitiveness of the casting.

[0039] In a preferred embodiment, the iterative update of the initial solution set using the optimization direction and the optimization step size includes: establishing an iterative trajectory for each solution and identifying the iterative trajectory through the solution fitness value of each iteration; configuring an iterative evaluation interval, and identifying the update state of the iterative trajectory in the iterative evaluation interval to generate an evaluation classification, where the evaluation classification includes an optimal solution evaluation classification, an exploration evaluation classification, and a suboptimal solution evaluation classification; and performing search self-optimization management for iterative update according to the evaluation classification.

[0040] Exemplarily, during the process of iteratively updating the initial solution set using the optimization direction and the optimization step size, an iterative trajectory is established for each solution. These trajectories are like the "growth paths" of the solutions in the parameter space, recording every step change from their initial state to the current state. Furthermore, these iterative trajectories are identified through the solution fitness value of each iteration, making the performance of each solution in the optimization process clear at a glance.

[0041] In order to manage the iteration process more effectively, the iteration evaluation interval is configured. This interval is an evaluation scale used to measure the performance of the solution during the iteration process. Within the iteration evaluation interval, the update status of the iteration trajectory is identified and evaluation categories are generated. These evaluation categories include excellent solution evaluation category, exploration evaluation category, and poor solution evaluation category, which represent solutions with excellent performance, solutions worthy of further exploration, and solutions with poor performance, respectively.

[0042] Specifically, the solutions in the excellent solution evaluation category are those that have performed well in the current iteration and have significantly improved their fitness values. They are important candidates for finding the optimal solution. The solutions in the exploration evaluation category have certain potential, but may need further adjustment and optimization to tap their maximum value. The solutions in the poor solution evaluation category are those that have performed poorly in the current iteration, and their fitness values ​​have not significantly improved or even decreased. They may need to be replaced or readjusted.

[0043] Finally, we perform iterative and updated search self-optimization management based on these evaluation categories. For optimal solutions, we increase the search intensity and try to find better solutions near them; for exploratory solutions, we adjust the optimization strategy and encourage them to explore in more promising directions; and for inferior solutions, we consider replacing them or making substantial adjustments to them in order to achieve better performance in subsequent iterations.

[0044] In a preferred embodiment, the search self-optimization management that performs iterative updates based on the evaluation classification includes: configuring a local proxy model in the optimal solution evaluation classification, using the local proxy model to predict improvement trends, and generating a first reference optimization direction; configuring a penalty optimization identification layer in the inferior solution evaluation classification, using the penalty optimization identification layer to identify erroneous improvement directions, and establishing window improvement taboos; performing iterative fine-tuning updates on solutions within the optimal solution evaluation classification using the first reference optimization direction and window improvement taboos, performing mixed exploration iterative updates on the exploration evaluation classification using the first reference optimization direction and window improvement taboos, and configuring random factors to perform iterative updates on solutions within the inferior solution evaluation classification.

[0045] In detail, in the process of iteratively updating the search self-optimization management according to the evaluation classification, differentiated optimization strategies are adopted for solutions of different evaluation classifications. For the solutions in the evaluation classification of the best solution, a local proxy model is configured. This model can predict its improvement trend based on the performance of the current solution and generate the first reference optimization direction. This direction provides clear guidance for finding a better solution near the best solution. For the solutions in the evaluation classification of the worst solution, a penalty optimization recognition layer is configured. This recognition layer is like an error correction mechanism. It can identify the wrong direction of the solution in the improvement process and establish window improvement taboos to prevent the solution from falling into these wrong directions again in subsequent iterations.

[0046] Furthermore, the first reference optimization direction and window improvement taboos are used to fine-tune and iteratively update the solutions in the optimal solution evaluation category. This process fine-tunes the parameter combination so that the solution can further approach the optimal solution while maintaining excellent performance. For the solutions in the exploration evaluation category, a hybrid exploration iterative update strategy is adopted. It is also guided by the first reference optimization direction and window improvement taboos, but the solution is allowed to explore in a wider parameter space to discover potential optimal solution areas. For the solutions in the inferior solution evaluation category, a random factor is configured to perform iterative updates. This random factor can introduce a certain degree of randomness in the solution improvement process, helping the solution to jump out of the current predicament and find new improvement directions. Through search self-optimization management, the performance of the optimal solution can be improved, while the inferior solution can be gradually transformed into a potential exploration solution. Finally, a globally optimal parameter combination is found, which significantly improves the quality and production efficiency of castings.

[0047] In a preferred embodiment, the execution of parameter-directed optimization also includes: establishing an energy consumption evaluation function of the parameter; establishing a balance adaptation function based on the energy consumption evaluation function and the quality evaluation function; screening optimization schemes for the parameter-directed optimization based on the balance adaptation function, and outputting the optimization scheme screening results as parameter-directed optimization results.

[0048] Specifically, in the process of performing parameter-oriented optimization, we not only focus on the impact of parameters on casting quality, but also introduce an energy consumption evaluation function to consider the impact of parameter adjustment on energy consumption. The energy consumption evaluation function quantifies the energy consumption under different parameter combinations, helping to understand whether parameter adjustment will increase energy consumption while improving quality.

[0049] Based on the energy consumption evaluation function and the quality evaluation function, a balanced adaptation function is established. This function comprehensively considers the two factors of quality and energy consumption, ensuring that in the optimization process, quality will not be pursued unilaterally while ignoring energy consumption, nor will quality be sacrificed in order to reduce energy consumption. Then, based on this balanced adaptation function, the optimization scheme screening of parameter-oriented optimization is carried out. This process refers to selecting those solutions that meet the quality requirements and have low energy consumption from many possible parameter combinations. These solutions represent the best practices for parameter adjustment. Finally, the optimization scheme screening results are output as parameter-oriented optimization results. This result guides the search for the optimal parameter combination scheme, which not only improves the quality of castings, but also controls energy consumption. By introducing the energy consumption evaluation function and establishing a balanced adaptation function, the parameter combination scheme can be successfully screened out, achieving a win-win situation for quality and energy consumption.

[0050] For example, in the investment casting process, the energy consumption mainly comes from the operation of heating equipment (such as furnaces, drying ovens, etc.). To simplify the problem, assume that the energy consumption is mainly related to the heating time and heating temperature. Therefore, the energy consumption evaluation function can be expressed as: E = α×T×t + β; where: E represents the energy consumption (unit: kilowatt-hour, kWh), T represents the heating temperature (unit: degree Celsius, °C), t represents the heating time (unit: hour, h), and α and β are constants used to adjust the scale of energy consumption calculation and can be obtained by fitting experimental data. Assume that in a certain investment casting process, when the heating temperature increases by 10 °C, the energy consumption increases by 5 kWh / h (i.e., α = 0.5 kWh / °C·h); the basic energy consumption (such as the standby energy consumption of equipment, which exists even without heating) is 2 kWh (i.e., β = 2 kWh). If the heating temperature is 1500 °C and the heating time is 4 hours, then the energy consumption is: E = 0.5×1500×4 + 2 = 3002 kWh.

[0051] The balance adaptation function needs to comprehensively consider factors of both quality and energy consumption. Assume there is a quality evaluation function Q, which represents the quality of the casting (such as porosity, uniformity of shell film thickness, etc., and can be represented by a comprehensive index, with a value range between 0 and 1, where 1 represents the best quality). Then, the balance adaptation function F can be expressed as: ; or, in order to compare energy consumption and quality on the same magnitude scale, the energy consumption can be normalized to obtain an energy consumption penalty term P E , and then it is combined with the quality evaluation function: ; ; where, and are weight coefficients used to adjust the relative importance of quality and energy consumption in the balance adaptation function; and are respectively the minimum and maximum values of the energy consumption for normalization. Assume the following data is obtained in a certain investment casting project: the value of the quality evaluation function Q is 0.85 (indicating good casting quality); the energy consumption E is 3000 kWh; the minimum value of the energy consumption is 2000 kWh, and the maximum value is 4000 kWh; the weight coefficient = 0.7, = 0.3 (indicating that more importance is attached to quality, but energy consumption is also an important consideration). Then the energy consumption penalty term is: ; the balance adaptation function is: F = 0.7×0.85−0.3×0.5 = 0.595−0.15 = 0.445. This value can be used to compare the comprehensive performance under different parameter combinations, and the larger the value, the better the comprehensive performance. In the process of parameter directional optimization, select those parameter combinations that make the value of the balance adaptation function the largest as the optimal solution.

[0052] The directional optimization method for investment casting parameters provided by the embodiments of the present invention has at least the following technical effects: 1. By configuring the directional optimization objectives and clearly distinguishing the main optimization objectives, auxiliary optimization objectives, and maintenance objectives, the comprehensive and precise optimization of the quality of investment casting workpieces is achieved. This multi-objective optimization strategy not only focuses on key quality indicators such as porosity and shell film thickness uniformity but also considers potential problems such as casting shrinkage rate and thermal stress cracks, thus ensuring the comprehensiveness and practicality of the optimization results. Through the calculation of sensitivity coefficients, the key optimization parameters that have the greatest impact on the optimization objectives can be accurately identified, providing a clear direction and focus for subsequent parameter directional optimization and improving the optimization efficiency.

[0053] 2. In the process of parameter directional optimization, an iterative update strategy is adopted. By establishing an iterative trajectory, configuring an iterative evaluation interval, and generating evaluation classifications, the real-time monitoring and intelligent management of the dynamic changes of the solution set are realized. This iterative update method can gradually approach the optimal solution, improving the accuracy and reliability of the optimization results. And the search self-optimization management strategy differentially processes the solution set according to the evaluation classification. The solutions within the excellent solution evaluation classification are fine-tuned and iteratively updated, the solutions in the exploration evaluation classification are mixed and explored for iterative update, and the solutions in the inferior solution evaluation classification are configured with random factors for iterative update. This strategy can make full use of the information of the solution set, improve the search efficiency, and avoid falling into local optimal solutions.

[0054] 3. An energy consumption evaluation function is introduced and combined with the quality evaluation function to establish a balance adaptation function. This balance optimization strategy can effectively control energy consumption while ensuring the quality of the casting, achieving a win-win situation for quality and energy consumption. Through the screening of the optimization scheme based on the balance adaptation function, a parameter directional optimization result that not only meets the quality requirements but also has low energy consumption can be output, providing strong support for the sustainable development of the investment casting industry.

[0055] Embodiment 2: As Figure 2 shown, based on the same inventive concept as the directional optimization method for investment casting parameters provided in Embodiment 1, the embodiments of the present invention also provide a directional optimization system for investment casting parameters. The system includes: A parameter acquisition module 11, configured to acquire the current parameter set of investment casting and establish a quality monitoring database of the investment casting workpiece mapped to the current parameter set. Among them, the monitoring indicators of the quality monitoring database include porosity, pore distribution, pore size, shell film thickness distribution, thickness standard deviation, shrinkage rate distribution, crack morphology, and crack size.

[0056] The target configuration module 12 is used to configure the directional optimization targets according to the quality monitoring database. The directional optimization targets include pore optimization, shell film thickness uniformity optimization, casting shrinkage rate optimization, and thermal stress crack optimization.

[0057] The classification identification module 13 is used to determine the target classification identification in the directional optimization targets. The target classification identification includes the main optimization target, the auxiliary optimization target, and the maintenance target.

[0058] The impact analysis module 14 is used to perform the impact analysis of the directional optimization target of the key optimization parameters after selecting the key optimization parameters by using the main optimization target and the auxiliary optimization target.

[0059] The directional optimization module 15 is used to establish parameter optimization constraints according to the results of the directional target optimization impact analysis, perform parameter directional optimization, and complete the directional optimization of investment casting parameters by using the results of the parameter directional optimization.

[0060] Furthermore, the impact analysis module 14 is also used to perform the following steps: Obtain the set of adjustable parameters of investment casting; perform a quantitative mapping of the influence degree of the set of adjustable parameters on the main optimization target and the auxiliary optimization target; construct a target impact matrix for all optimization targets in the directional optimization target, where the target impact matrix characterizes the mutual relationship between different optimization targets, and the mutual relationship includes a positive promotion relationship and a negative conflict relationship; calculate the sensitivity coefficient of the set of adjustable parameters according to the quantitative mapping of the influence degree and the target impact matrix; establish the results of the directional target optimization impact analysis according to the calculation results of the sensitivity coefficient.

[0061] Furthermore, the directional optimization module 15 is also used to perform the following steps: After establishing the control interval of the parameters, create an initial solution set based on the current parameter set; perform the solution fitness evaluation within the initial solution set, and establish the optimization direction and optimization step size through the parameter optimization constraints and the fitness evaluation results; use the optimization direction and the optimization step size to iteratively update the initial solution set; complete the parameter directional optimization according to the iterative update results.

[0062] Furthermore, the directional optimization module 15 is also used to perform the following steps: Establish an iterative trajectory for each solution, and identify the iterative trajectory through the solution fitness value of each iteration; configure an iterative evaluation interval, identify the update status of the iterative trajectory within the iterative evaluation interval, and generate an evaluation classification, where the evaluation classification includes an optimal solution evaluation classification, an exploration evaluation classification, and a suboptimal solution evaluation classification; perform search self-optimization management for iterative updates according to the evaluation classification.

[0063] Furthermore, the directional optimization module 15 is also used to perform the following steps: Configure a local proxy model in the optimal solution evaluation and classification, use the local proxy model to predict the improvement trend, and generate a first reference optimization direction; configure a penalty optimization recognition layer in the inferior solution evaluation and classification, use the penalty optimization recognition layer to identify the wrong improvement direction, and establish a window improvement taboo; perform iterative update of the solution fine-tuning within the optimal solution evaluation and classification with the first reference optimization direction and the window improvement taboo, perform iterative update of the hybrid exploration of the exploration evaluation and classification with the first reference optimization direction and the window improvement taboo, and configure a random factor to perform iterative update of the solution within the inferior solution evaluation and classification.

[0064] Furthermore, the directional optimization module 15 is further configured to perform the following steps: Establish an energy consumption evaluation function for the parameters; establish a balance adaptation function based on the energy consumption evaluation function and the quality evaluation function; screen the optimization solution for the parameter directional optimization based on the balance adaptation function, and output the screening result of the optimization solution as the parameter directional optimization result.

[0065] Through the foregoing detailed description of a method for directional optimization of investment casting parameters in this specification, those skilled in the art can clearly know a system for directional optimization of investment casting parameters in this embodiment. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, please refer to the description in the method part.

[0066] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for directional optimization of investment casting parameters, characterized in that: The method comprises: Obtaining a current parameter set for investment casting and establishing a quality monitoring database for investment casting workpieces mapped to the current parameter set; Configuring directional optimization targets according to the quality monitoring database, wherein the directional optimization targets include pore optimization, shell film thickness uniformity optimization, casting shrinkage optimization, and thermal stress crack optimization; Determine a target classification identifier in the directional optimization target, wherein the target classification identifier includes a primary optimization target, an auxiliary optimization target, and a maintained target; After selecting key optimization parameters using the main optimization objective and the auxiliary optimization objective, performing directional optimization objective impact analysis of the key optimization parameters; After establishing the parameter optimization constraints according to the results of the directional target optimization impact analysis, the parameter directional optimization is performed, and the directional optimization of the investment casting parameters is completed using the parameter directional optimization results.

2. A method for directional optimization of investment casting parameters according to claim 1, characterized in that: After the key optimization parameters are selected by using the main optimization objective and the auxiliary optimization objective, a directional optimization objective impact analysis of the key optimization parameters is performed, including: Get the set of adjustable parameters for investment casting; Performing quantitative mapping of the influence of the adjustable parameter set on the main optimization objective and the auxiliary optimization objective; Constructing a target influence matrix of all optimization targets in the directional optimization target, wherein the target influence matrix represents the mutual relationship between different optimization targets, and the mutual relationship includes a positive promotion relationship and a negative conflict relationship; Calculate the sensitivity coefficients of the adjustable parameter set based on the impact degree quantification mapping and the target impact matrix; The directional target optimization impact analysis results are established based on the sensitivity coefficient calculation results.

3. A method for directional optimization of investment casting parameters as claimed in claim 2, characterized in that: The execution parameter directional optimization comprises: After establishing the control interval of the parameters, create an initial solution set based on the current parameter set; After executing the fitness evaluation of the solutions in the initial solution set, the optimization direction and optimization step size are established through the parameter optimization constraints and the fitness evaluation results; Iteratively updating the initial solution set using the optimization direction and the optimization step size; Complete parameter directional optimization based on the iterative update results.

4. A method for directional optimization of investment casting parameters as claimed in claim 3, characterized in that: The iterative updating of the initial solution set using the optimization direction and the optimization step size includes: Establish an iteration trajectory for each solution, and identify the iteration trajectory through the solution fitness value of each round of iteration; Configure an iterative evaluation interval, perform update state identification of the iterative trajectory in the iterative evaluation interval, and generate an evaluation classification, wherein the evaluation classification includes an optimal solution evaluation classification, an exploration evaluation classification, and an inferior solution evaluation classification; Search self-optimization management is performed with iterative updates based on the evaluation classification.

5. A method for directional optimization of investment casting parameters as claimed in claim 4, characterized in that: The search self-optimization management that is iteratively updated according to the evaluation classification includes: In the optimal solution evaluation classification, a local proxy model is configured, and improvement trend prediction is performed using the local proxy model to generate a first reference optimization direction; A penalty optimization identification layer is configured in the inferior solution evaluation classification, and the penalty optimization identification layer is used to identify the wrong improvement direction and establish a window improvement taboo; The first reference optimization direction and window improvement taboo are used to perform iterative updates on fine-tuning of solutions within the superior solution evaluation category. The first reference optimization direction and window improvement taboo are used to perform mixed exploration iterative updates on the exploration evaluation category. Random factors are configured to perform iterative updates on solutions within the inferior solution evaluation category.

6. A method for directional optimization of investment casting parameters as claimed in claim 1, characterized in that: The execution parameter directional optimization further includes: Establish the energy consumption evaluation function of parameters; Establish a balance adaptation function based on the energy consumption evaluation function and the quality evaluation function; Based on the balance adaptation function, the optimization scheme screening of the parameter-oriented optimization is performed, and the optimization scheme screening result is output as the parameter-oriented optimization result.

7. A method for directional optimization of investment casting parameters as claimed in claim 1, characterized in that: The monitoring indicators of the quality monitoring database include porosity, pore distribution, pore size, shell film thickness distribution, thickness standard deviation, shrinkage distribution, crack morphology, and crack size.

8. An investment casting parameter directional optimization system, characterized in that: A method for implementing a directional optimization method for investment casting parameters according to any one of claims 1 to 7, the system comprising: A parameter acquisition module, used to acquire a current parameter set of investment casting and establish a quality monitoring database of investment casting workpieces mapped with the current parameter set; A target configuration module, used to configure directional optimization targets according to the quality monitoring database, wherein the directional optimization targets include pore optimization, shell film thickness uniformity optimization, casting shrinkage optimization, and thermal stress crack optimization; A classification identification module, used to determine the target classification identification in the directional optimization target, wherein the target classification identification includes a main optimization target, an auxiliary optimization target and a maintained target; An impact analysis module, used to perform directional optimization target impact analysis of the key optimization parameters after selecting the key optimization parameters using the main optimization target and the auxiliary optimization target; The directional optimization module is used to establish parameter optimization constraints according to the results of directional target optimization impact analysis, perform parameter directional optimization, and use the parameter directional optimization results to complete the directional optimization of investment casting parameters.

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