CN118352002A - A method for predicting the yield strength of γ' phase strengthened cobalt-based superalloys - Google Patents
A method for predicting the yield strength of γ' phase strengthened cobalt-based superalloys Download PDFInfo
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Abstract
本发明公开一种γ'相强化钴基高温合金屈服强度的预测方法,属于钴基高温合金领域。该方法基于γ'相强化钴基高温合金成分‑热处理工艺‑屈服强度数据集,利用机器学习,以梯度提升算法为基础构建预测模型,实现了对该系列合金不同温度的屈服强度预测。预测模型的输入量为合金成分、热处理工艺参数、测试温度;输出量为屈服强度。该屈服强度预测方法可在较大的成分和温度范围内,对γ'相强化钴基高温合金的屈服强度做出快速精准的预测,在该系列合金的成分设计以及优化中具有较强的应用价值。
The present invention discloses a method for predicting the yield strength of a γ' phase strengthened cobalt-based high-temperature alloy, and belongs to the field of cobalt-based high-temperature alloys. The method is based on a γ' phase strengthened cobalt-based high-temperature alloy composition-heat treatment process-yield strength data set, and uses machine learning to construct a prediction model based on a gradient boosting algorithm to achieve yield strength predictions for this series of alloys at different temperatures. The input of the prediction model is alloy composition, heat treatment process parameters, and test temperature; the output is yield strength. The yield strength prediction method can make a fast and accurate prediction of the yield strength of a γ' phase strengthened cobalt-based high-temperature alloy within a large composition and temperature range, and has a strong application value in the composition design and optimization of this series of alloys.
Description
技术领域Technical Field
本发明属于钴基高温合金领域,具体涉及一种γ'相强化钴基高温合金的屈服强度的预测方法。The invention belongs to the field of cobalt-based high-temperature alloys, and in particular relates to a method for predicting the yield strength of a γ' phase-strengthened cobalt-based high-temperature alloy.
背景技术Background technique
钴基高温合金因其出色的力学性能和热稳定性,在航空、能源工业中占据着不可或缺的地位,被广泛用于飞机发动机、燃气轮机等关键热端部件。对于该类合金而言,在高温工作环境中保持稳定的力学性能,对提升发动机的性能、效率和可靠性至关重要。γ'相强化钴基高温合金是近些年开始发展的一类钴基高温合金,其具有比传统钴基高温合金更优异的高温力学性能而具有成为下一代高温结构材料的潜力。屈服强度是评估该类合金高温力学性能的一个关键参数,然而,作为一个新的合金体系,γ'相强化钴基高温合金屈服强度的预测方法尚未建立,这不利于该类合金的快速发展与工程应用。Cobalt-based superalloys occupy an indispensable position in the aviation and energy industries due to their excellent mechanical properties and thermal stability. They are widely used in key hot-end components such as aircraft engines and gas turbines. For this type of alloy, maintaining stable mechanical properties in a high-temperature working environment is crucial to improving the performance, efficiency and reliability of the engine. γ' phase-strengthened cobalt-based superalloys are a type of cobalt-based superalloy that has begun to develop in recent years. They have better high-temperature mechanical properties than traditional cobalt-based superalloys and have the potential to become the next generation of high-temperature structural materials. Yield strength is a key parameter for evaluating the high-temperature mechanical properties of this type of alloy. However, as a new alloy system, the prediction method for the yield strength of γ' phase-strengthened cobalt-based superalloys has not yet been established, which is not conducive to the rapid development and engineering application of this type of alloy.
传统的高温合金屈服强度预测方法主要依赖于经典理论和经验公式。这些方法通常面临诸多挑战,包括对材料行为的复杂计算、多个未知参数的估计以及大量实验数据的需求,这不仅提高了成本,也影响了预测的准确性和效率。近年来,随着人工智能和计算机科学领域的迅速发展,机器学习方法已经被引入到材料科学研究中。例如,机器学习已被证明能有效处理数据集,并通过算法学习预测材料性能。这种方法能够显著减少实验的需求,降低研发成本,并加速新材料的开发。Traditional methods for predicting the yield strength of high-temperature alloys mainly rely on classical theories and empirical formulas. These methods often face many challenges, including complex calculations of material behavior, estimation of multiple unknown parameters, and the need for a large amount of experimental data, which not only increases the cost but also affects the accuracy and efficiency of the prediction. In recent years, with the rapid development of artificial intelligence and computer science, machine learning methods have been introduced into materials science research. For example, machine learning has been shown to effectively process data sets and predict material properties through algorithmic learning. This approach can significantly reduce the need for experiments, reduce R&D costs, and accelerate the development of new materials.
目前,机器学习在预测高温合金的高温力学性能方面已有成功应用案例,CN114818481A提出一种单晶高温合金的蠕变断裂寿命预测方法。基于机器学习的算法,得到能够快速评估合金蠕变性能的单晶高温合金蠕变断裂寿命预测模型。对于结构材料γ'相强化钴基高温合金而言,屈服强度是最为重要的指标,然而,机器学习方法对数据量的要求较高,而现有文献中提供的屈服强度的实验数据非常有限,因此目前还没有采用机器学习方法对屈服强度进行预测的现有技术。At present, machine learning has been successfully applied in predicting the high-temperature mechanical properties of high-temperature alloys. CN114818481A proposes a method for predicting the creep rupture life of single-crystal high-temperature alloys. Based on the machine learning algorithm, a single-crystal high-temperature alloy creep rupture life prediction model that can quickly evaluate the creep performance of the alloy is obtained. For structural materials γ' phase strengthened cobalt-based high-temperature alloys, yield strength is the most important indicator. However, machine learning methods have high requirements for the amount of data, and the experimental data on yield strength provided in existing literature is very limited. Therefore, there is currently no existing technology that uses machine learning methods to predict yield strength.
发明内容Summary of the invention
针对上述目前还没有采用机器学习方法对屈服强度进行预测的技术问题,本发明提供一种基于机器学习的γ'相强化钴基高温合金屈服强度的预测方法,能够快速预测该类合金在不同温度下的屈服强度,并且适用于较大的合金成分和测试温度范围。In response to the above-mentioned technical problem that there is currently no machine learning method for predicting yield strength, the present invention provides a method for predicting the yield strength of γ' phase strengthened cobalt-based high-temperature alloy based on machine learning, which can quickly predict the yield strength of such alloys at different temperatures and is suitable for a larger alloy composition and test temperature range.
本发明采用的技术方案具体如下:The technical solution adopted by the present invention is specifically as follows:
一种γ'相强化钴基高温合金屈服强度的预测方法,先建立数据集,然后基于机器学习建立γ'相强化钴基高温合金屈服强度的预测模型,具体包括以下步骤:A method for predicting the yield strength of a γ' phase strengthened cobalt-based high-temperature alloy is provided. A data set is first established, and then a prediction model for the yield strength of a γ' phase strengthened cobalt-based high-temperature alloy is established based on machine learning. The method specifically comprises the following steps:
S1.建立数据集;收集γ'相强化钴基高温合金不同温度下屈服强度的相关数据构建数据集,所述数据包括合金成分、热处理工艺参数、测试温度和相应的屈服强度;所述数据集被随机按照6~8:2~4的比例分为训练集和测试集,其中,训练集的数据用于训练预测模型,测试集的数据用于测试模型精度;S1. Establish a data set; collect relevant data on the yield strength of γ' phase strengthened cobalt-based high-temperature alloy at different temperatures to construct a data set, wherein the data includes alloy composition, heat treatment process parameters, test temperature and corresponding yield strength; the data set is randomly divided into a training set and a test set in a ratio of 6~8:2~4, wherein the data of the training set is used to train the prediction model, and the data of the test set is used to test the model accuracy;
S2.建立预测模型;对于S1中构建的训练集,使用不同机器学习算法对预测模型进行训练,选择综合预测性能最好的梯度提升算法构建最优模型;S2. Establish a prediction model; for the training set constructed in S1, use different machine learning algorithms to train the prediction model, and select the gradient boosting algorithm with the best comprehensive prediction performance to build the optimal model;
S3.屈服强度预测;将待预测γ'相强化钴基高温合金的合金成分、热处理工艺参数和测试温度输入到S2获得的最优模型中,得到屈服强度的预测值。S3. Yield strength prediction: The alloy composition, heat treatment process parameters and test temperature of the γ' phase strengthened cobalt-based high-temperature alloy to be predicted are input into the optimal model obtained in S2 to obtain the predicted value of the yield strength.
进一步地,S1中,数据收集具体过程为:以现有文献(不同的数据库获得的数据会有一些区别,但因为数据样本较多,数据上不会产生显著差异,从而不会因为数据库来源的差异而造成结果的明显差异,因此,对数据库无需做具体要求)为数据基础,通过检索关键词等检索方式(关键词如Co、Ni、屈服强度、屈服应力等)获取文献,并利用Origin软件,在获取文献的曲线图中提取不同合金在不同温度下的屈服强度数据,最终全部数据包括合金成分、热处理工艺参数、测试温度以及相应的屈服强度。Furthermore, in S1, the specific process of data collection is: based on the existing literature (the data obtained from different databases will have some differences, but because there are more data samples, there will not be significant differences in the data, and thus there will not be obvious differences in the results due to differences in database sources. Therefore, no specific requirements are required for the database), the literature is obtained by searching keywords and other search methods (keywords such as Co, Ni, yield strength, yield stress, etc.), and the yield strength data of different alloys at different temperatures are extracted from the curve chart of the obtained literature using Origin software. Finally, all the data include alloy composition, heat treatment process parameters, test temperature and corresponding yield strength.
进一步地,所述数据集包含400个以上的数据,涵盖8~12种合金元素的含量、4种以上的热处理工艺参数和屈服强度测试温度。Furthermore, the data set contains more than 400 data, covering the contents of 8 to 12 alloy elements, more than 4 heat treatment process parameters and yield strength test temperatures.
进一步地,数据优选400~600个,合金元素优选10种,包括Co、Ni、Al、W、Ti、Ta、Cr、Mo、V、B;热处理工艺参数包括固溶温度、固溶时间、时效温度和时效时间。Furthermore, the data are preferably 400 to 600, and the alloying elements are preferably 10, including Co, Ni, Al, W, Ti, Ta, Cr, Mo, V, and B; the heat treatment process parameters include solution temperature, solution time, aging temperature, and aging time.
进一步地,合金元素的含量范围为Co 14-88at.%,Ni 0-54at.%,Al 0-10at.%,W0-11at.%,Ti 0-12at.%,Ta 0-2.8at.%,Cr 0-18at.%,Mo 0-9at.%,V 0-12at.%,B 0-0.02at.%;固溶温度为1100 -1350℃,固溶时间为2-100 h,时效温度为760-1100℃,时效时间为4-400 h,屈服强度测试温度为25-1100℃,屈服强度为94-888MPa。Furthermore, the content of alloy elements ranges from Co 14-88at.%, Ni 0-54at.%, Al 0-10at.%, W0-11at.%, Ti 0-12at.%, Ta 0-2.8at.%, Cr 0-18at.%, Mo 0-9at.%, V 0-12at.%, B 0-0.02at.%; the solution temperature is 1100-1350℃, the solution time is 2-100 h, the aging temperature is 760-1100℃, the aging time is 4-400 h, the yield strength test temperature is 25-1100℃, and the yield strength is 94-888MPa.
进一步地,S2中,不同机器学习算法优选经典的不同机器学习算法,包括近邻算法、随机森林算法、梯度提升算法和支持向量机算法。Furthermore, in S2, different machine learning algorithms preferably select different classic machine learning algorithms, including nearest neighbor algorithm, random forest algorithm, gradient boosting algorithm and support vector machine algorithm.
进一步地,S2中,梯度提升算法参数中树的数量在160-200个范围内优化,树的深度为4-5。Furthermore, in S2, the number of trees in the gradient boosting algorithm parameters is optimized in the range of 160-200, and the depth of the tree is 4-5.
进一步地,S3中,模型的输入参数为合金成分、固溶温度、固溶时间、时效温度、时效时间和测试温度;输出参数为屈服强度。Furthermore, in S3, the input parameters of the model are alloy composition, solution temperature, solution time, aging temperature, aging time and test temperature; and the output parameter is yield strength.
为了解决屈服强度的数据量问题,发明人不仅利用现有文献中直接呈现的实验数据,同时想到从现有文献的曲线图中提取不同合金在不同温度下的屈服强度数据,从而显著扩充了屈服强度的数据范围,能够满足机器学习方法的数据量要求,但这样获得的数据确实存在一定程度的误差,因此发明人在最初构建屈服强度预测模型的过程中,以屈服强度最为相关的参数合金成分以及测试温度作为输入数据,结果表明误差很大,发明人通过不断尝试和分析,在合金成分、测试温度的基础上,增加了热处理工艺参数,最终获得非常理想的误差率和精度值,从而能够非常有效地对γ'相强化钴基高温合金的屈服强度进行预测。In order to solve the problem of the data volume of yield strength, the inventors not only used the experimental data directly presented in the existing literature, but also thought of extracting the yield strength data of different alloys at different temperatures from the curve diagrams of the existing literature, thereby significantly expanding the data range of yield strength and being able to meet the data volume requirements of the machine learning method. However, the data obtained in this way do have a certain degree of error. Therefore, in the initial process of constructing the yield strength prediction model, the inventors used the alloy composition and test temperature, the parameters most relevant to yield strength, as input data. The results showed that the error was large. Through continuous attempts and analysis, the inventors added heat treatment process parameters on the basis of alloy composition and test temperature, and finally obtained a very ideal error rate and accuracy value, which can effectively predict the yield strength of γ' phase strengthened cobalt-based high-temperature alloy.
本发明的有益效果在于:The beneficial effects of the present invention are:
本发明通过不同的机器学习算法的筛选,获得基于梯度提升模型建立的γ'相强化钴基高温合金屈服强度预测方法,该方法能快速预测不同成分和工艺条件下该系列合金在不同温度下的屈服强度,不仅预测精度高,而且适用的成分和温度范围较大。最优模型在训练集与测试集上的具体实施效果如图2所示,结果表明,最优模型的测试集平均相对误差为6.4%,精度值R2达到0.91,即具有优秀的预测性能。因此,本发明的预测方法在γ'相强化钴基高温合金的成分设计和工艺优化方面具有显著的工程应用价值。The present invention obtains a yield strength prediction method for γ' phase strengthened cobalt-based high-temperature alloys based on a gradient boosting model by screening different machine learning algorithms. The method can quickly predict the yield strength of the series of alloys at different temperatures under different compositions and process conditions. It not only has high prediction accuracy, but also has a large applicable composition and temperature range. The specific implementation effect of the optimal model on the training set and the test set is shown in Figure 2. The results show that the average relative error of the test set of the optimal model is 6.4%, and the accuracy value R2 reaches 0.91, that is, it has excellent prediction performance. Therefore, the prediction method of the present invention has significant engineering application value in the composition design and process optimization of γ' phase strengthened cobalt-based high-temperature alloys.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明的流程框图;Fig. 1 is a flow chart of the present invention;
图2至图5分别为近邻算法、随机森林算法、梯度提升算法以及支持向量机算法在不同参数下的测试集平均误差率的结果图;Figures 2 to 5 are the result graphs of the average error rate of the test set under different parameters of the nearest neighbor algorithm, the random forest algorithm, the gradient boosting algorithm and the support vector machine algorithm;
图6为本发明预测方法在训练集与测试集上的具体实施效果图;FIG6 is a diagram showing the specific implementation effect of the prediction method of the present invention on a training set and a test set;
图7为本发明预测方法的训练集之外合金的实施效果图。FIG. 7 is a diagram showing the implementation effect of the prediction method of the present invention on alloys outside the training set.
具体实施方式Detailed ways
下面结合附图和具体实施例对本发明做进一步详细说明,但本发明并不限于此。The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments, but the present invention is not limited thereto.
实施例1Example 1
以Co、Ni、屈服强度、屈服应力为关键词,从万方数据库检索现有文献,通过去噪处理,排除明显不符合条件的文献,搜集γ'相强化钴基高温合金不同温度屈服强度的相关数据402个,构建数据集,数据结构为:合金成分-热处理工艺参数-测试温度-屈服强度,把数据集随机按照8:2的比例分为训练集和测试集,其中,训练集的数据用于训练预测模型,测试集的数据用于测试模型精度。With Co, Ni, yield strength and yield stress as keywords, the existing literature was retrieved from the Wanfang database. Through denoising, the literature that obviously did not meet the conditions was excluded. 402 relevant data on the yield strength of γ' phase strengthened cobalt-based high-temperature alloys at different temperatures were collected to construct a data set with the data structure of alloy composition-heat treatment process parameters-test temperature-yield strength. The data set was randomly divided into a training set and a test set in a ratio of 8:2. The data in the training set was used to train the prediction model, and the data in the test set was used to test the model accuracy.
基于上述训练集和测试集把合金成分、固溶温度、固溶时间、时效温度、时效时间和测试温度作为模型的输入参量;输出参量为屈服强度。使用近邻算法,随机森林算法,梯度提升算法,支持向量机算法训练预测模型,由图2至图5可知,近邻算法在K值为2时平均误差率取得最小为16%;随机森林算法显示出平均误差率上小下大的规律,当树的数量为180,深度为20时,平均误差率取到小值是11%;梯度提升算法在树的数量为200,深度为5时,平均误差率取到小值是6%;支持向量机算法当正则化参数在100附近,核函数参数位于0.01左右时,测试集平均误差率处于较小范围为15%,因此选择综合预测性能最好的梯度提升算法来构建最优模型。Based on the above training set and test set, alloy composition, solution temperature, solution time, aging temperature, aging time and test temperature are used as input parameters of the model; the output parameter is yield strength. The nearest neighbor algorithm, random forest algorithm, gradient boosting algorithm and support vector machine algorithm are used to train the prediction model. From Figures 2 to 5, it can be seen that the nearest neighbor algorithm has a minimum average error rate of 16% when the K value is 2; the random forest algorithm shows a rule that the average error rate is small at the top and large at the bottom. When the number of trees is 180 and the depth is 20, the average error rate is 11%; the gradient boosting algorithm has a minimum average error rate of 6% when the number of trees is 200 and the depth is 5; when the regularization parameter of the support vector machine algorithm is around 100 and the kernel function parameter is around 0.01, the average error rate of the test set is in a small range of 15%, so the gradient boosting algorithm with the best comprehensive prediction performance is selected to build the optimal model.
基于梯度提升算法,该算法参数中树的数量优化范围为160-200,树的深度为4-5。最优模型的测试集平均误差率为6.4%,精度值R2达到0.91。最优模型在训练集与测试集上的具体实施效果如图6所示。Based on the gradient boosting algorithm, the number of trees in the algorithm parameters is optimized in the range of 160-200, and the depth of the tree is 4-5. The average error rate of the test set of the optimal model is 6.4%, and the accuracy value R2 reaches 0.91. The specific implementation effect of the optimal model on the training set and the test set is shown in Figure 6.
从测试集选择5条γ'相强化钴基高温合金数据,这些合金涵盖三元、四元、五元、六元、七元体系合金,测试温度涵盖25℃至1000℃,这五种不同的合金成分和工艺参数如表1所示。Five γ′ phase strengthened cobalt-based high-temperature alloy data are selected from the test set. These alloys cover ternary, quaternary, quinary, hexavalent and heptad system alloys. The test temperature ranges from 25°C to 1000°C. The five different alloy compositions and process parameters are shown in Table 1.
表1 五种不同的合金成分和工艺参数Table 1 Five different alloy compositions and process parameters
将上述5个合金的合金成分、固溶温度、固溶时间、时效温度、时效时间、测试温度按表1中的数据输入梯度提升模型,分别获得屈服强度的预测值。5个合金的预测结果如图7所示,5个合金通过本发明方法所得预测屈服强度值与实验屈服强度值的误差率分别为5.25%、6.57%、4.67%、5.00%和0.96%,可见本发明的预测方法预测可靠性高。The alloy composition, solution temperature, solution time, aging temperature, aging time, and test temperature of the above five alloys were input into the gradient lifting model according to the data in Table 1, and the predicted values of yield strength were obtained respectively. The prediction results of the five alloys are shown in FIG7 , and the error rates between the predicted yield strength values and the experimental yield strength values of the five alloys obtained by the method of the present invention are 5.25%, 6.57%, 4.67%, 5.00%, and 0.96%, respectively, which shows that the prediction method of the present invention has high prediction reliability.
从测试集选择2条γ'相强化钴基高温合金数据,输入数据仅为合金成分和测试温度,代入到最优预测模型中,这两种不同的合金成分及预测结果如表2所示。Two γ′ phase strengthened cobalt-based high-temperature alloy data were selected from the test set. The input data were only the alloy composition and the test temperature. They were substituted into the optimal prediction model. The two different alloy compositions and prediction results are shown in Table 2.
表 2 两种不同的合金成分及预测结果Table 2 Two different alloy compositions and prediction results
将上述两个合金的合金成分以及测试温度按表2中的数据输入梯度提升模型,分别获得屈服强度的预测值。由表2能够得知,合金1的预测屈服强度值与实验屈服强度值的误差率为54%,合金2的预测屈服强度值与实验屈服强度值的误差率为63%,从而能够得知,仅以屈服强度强相关的合金成分以及测试温度作为输入数据,会导致误差率过大,从而无法获得稳定且准确的预测结果,由上表1可知,在合金成分以及测试温度的基础上同时引入热处理工艺参数作为输出量,能够显著降低预测结果的误差率,从而能够确保本发明预测方法的有效性和可靠性。The alloy composition and test temperature of the above two alloys are input into the gradient lifting model according to the data in Table 2, and the predicted values of yield strength are obtained respectively. It can be seen from Table 2 that the error rate between the predicted yield strength value of alloy 1 and the experimental yield strength value is 54%, and the error rate between the predicted yield strength value of alloy 2 and the experimental yield strength value is 63%. It can be seen that only using the alloy composition and test temperature that are strongly related to yield strength as input data will result in too large an error rate, so that stable and accurate prediction results cannot be obtained. It can be seen from Table 1 above that introducing heat treatment process parameters as output on the basis of alloy composition and test temperature can significantly reduce the error rate of the prediction result, thereby ensuring the effectiveness and reliability of the prediction method of the present invention.
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