船用大型锻件余热热处理工艺方法研究及参数优化
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摘要
长轴类大型锻件一般用于传动轴,是机器设备的关键和核心部件,是制造重大装备的基础件,质量要求十分严格。大型锻件由于尺寸大、重量大,需要由大型钢锭直接锻压而成,锻压后锻件性能、组织不均匀非常严重。为达到工作性能要求,必须制定合理的热处理工艺,改善其组织性能,并减少加热时间以达到节约能源的目的。本论文的研究课题来源于广东省重大科技专项(2009A080304004)“船舶工业用大型锻件锻造减量化及余热能源利用技术的研究与产业应用”。
本文以传热学理论为基础,以船用长轴类大锻件为研究对象,制定了余热热处理工艺规范,采用有限元数值模拟和试验研究相结合的方法,对锻件的余热热处理过程进行数值模拟,并通过优化算法优化了最终热处理工艺参数。本文研究内容和结论如下:
(1)根据锻件材料的化学成分、热物性数据、等温转变曲线(TTT)和相变潜热等,建立了35CrMo材料库。通过对锻件内部温度的实测,采用有限差分法编程计算,获得锻件换热系数曲线。
(2)通过物理模型试验验证了余热正火热处理工艺是可行的。试验结果表明,该工艺不仅保证了锻件中心部位的韧性、改善了表面耐磨性,而且缩短了加热时间。相对于常规正火工艺,余热正火减少了58%以上的加热时间。
(3)采用DEFORM-3D有限元软件建立了余热热处理过程的有限元计算模型,通过试验对模型进行了验证,模拟结果与实测结果基本相符。数值模拟结果表明,淬火5min时,锻件圆角部位的等效应力最大,此时等效应力为401Mpa,锻件在淬火过程中不会出现塑性变形现象。调质处理后主要组织为回火索氏体,具有良好的综合力学性能。
(4)提出了一种SVM神经网络与遗传算法相结合的长轴类大型锻件热处理工艺参数优化方法,以淬火温度、淬火保温时间、回火温度和回火保温时间等工艺参数为优化对象、加热时间和最大残余应力为优化目标,对长轴类大型锻件的最终热处理工艺进行了优化。首先进行正交试验,以试验数据为样本;通过神经网络建立工艺参数与目标之间的非线性映射模型;然后采用遗传算法对模型进行优化获取最优工艺参数。结果表明:相对于传统的调质工艺,优化工艺的加热时间减少了22%、最大残余应力下降了24%。
Long-axis heavy forgings are widely used for transmission shaft.As the basic parts of many heavy machines and equipments, the quality requirements of long-axis heavy forgings are very strict.Due to the large size and the heavy weight,the heavy forgings is forged directly by large ingots.Howevery, the forging performance and the organization is uneven seriously after being forged.To achieve the performance requirements, it is necessary to develop a reasonable process of heat treatment that improve the organization performance and save time and energy.The research subject are financially supported by the 2009 major scientific and technological projects in Guangdong province (2009A080304004)“Research on forging reduction of heavy forgings and technology of using waste heat and its application in shipping industry”.
In this paper, based on the basic theory of heat transfer, the long-axis heavy forgings as the research object, and formulate the process specification of remained heat treatment, finite element method combited with experiment examination was used to simulate heat treatment process, then optimization algorithm was used to optima parameter of heat treatment process.The main research contents and conclusions of this paper are summarized as follows:
(1)Based on chemical composition、thermophysical properties、TTT and latent heat, a library of 35CrMo has been developed.A mathematical model is established to calculate the temperature of the surface of forging from the temperature data of the center of forging.This algorithm of FDM (Finite Difference Method) is programmed and the curve of heat transfer coefficient is obtained.
(2)The process of remained heat treatment is validated by experiment, the results show that this process is feasible.The forging made with the new process have improved surface wear-ability and inner structure of forging.Also, the new technique has advantages in shortening heat preservation, saving heating time more than 58%.
(3)A FEM (Finite Element Method) model of remained heat treatment process is built based on the FEM software DEFORM-3D.The model is validated by experiments, the simulation results basically fit those measured in experiments.The results show that due to chilling effect of quenching, the temperature differences between surface and core of the forging are tremendous;During the process, the stress of the round areas of forging is maximum at 5min, which is 410Mpa, and the stress has no possibilities of plastic deformation.After quenching-tempering, the main organization of forging is temper-sorbite, with good mechanical properties.
(4) A method combining SVM (Support Vector Machine) with GA (Genetic Algorithm) to optimize heat treatment process parameters of long-axis heavy forgings was put forward, taking quenching temperature、quenching holding time、tempering temperature and tempering holding time, and so on as optimizing parameters.Heating time and residual stress as optimizing objective, the final heat treatment process of long-axis heavy forgings is optimized.Firstly, through the orthogonal experiment of the technical factors in heat treatment,the mapping model of SVM was established based on data above experiment.Then, through optimizing the model by useness of GA, the optimum heat treatment process parameters have been given.The results show that compared to conventional process, this process has save heating time more than 22% and the residual stress more than 24%.
本文以传热学理论为基础,以船用长轴类大锻件为研究对象,制定了余热热处理工艺规范,采用有限元数值模拟和试验研究相结合的方法,对锻件的余热热处理过程进行数值模拟,并通过优化算法优化了最终热处理工艺参数。本文研究内容和结论如下:
(1)根据锻件材料的化学成分、热物性数据、等温转变曲线(TTT)和相变潜热等,建立了35CrMo材料库。通过对锻件内部温度的实测,采用有限差分法编程计算,获得锻件换热系数曲线。
(2)通过物理模型试验验证了余热正火热处理工艺是可行的。试验结果表明,该工艺不仅保证了锻件中心部位的韧性、改善了表面耐磨性,而且缩短了加热时间。相对于常规正火工艺,余热正火减少了58%以上的加热时间。
(3)采用DEFORM-3D有限元软件建立了余热热处理过程的有限元计算模型,通过试验对模型进行了验证,模拟结果与实测结果基本相符。数值模拟结果表明,淬火5min时,锻件圆角部位的等效应力最大,此时等效应力为401Mpa,锻件在淬火过程中不会出现塑性变形现象。调质处理后主要组织为回火索氏体,具有良好的综合力学性能。
(4)提出了一种SVM神经网络与遗传算法相结合的长轴类大型锻件热处理工艺参数优化方法,以淬火温度、淬火保温时间、回火温度和回火保温时间等工艺参数为优化对象、加热时间和最大残余应力为优化目标,对长轴类大型锻件的最终热处理工艺进行了优化。首先进行正交试验,以试验数据为样本;通过神经网络建立工艺参数与目标之间的非线性映射模型;然后采用遗传算法对模型进行优化获取最优工艺参数。结果表明:相对于传统的调质工艺,优化工艺的加热时间减少了22%、最大残余应力下降了24%。
Long-axis heavy forgings are widely used for transmission shaft.As the basic parts of many heavy machines and equipments, the quality requirements of long-axis heavy forgings are very strict.Due to the large size and the heavy weight,the heavy forgings is forged directly by large ingots.Howevery, the forging performance and the organization is uneven seriously after being forged.To achieve the performance requirements, it is necessary to develop a reasonable process of heat treatment that improve the organization performance and save time and energy.The research subject are financially supported by the 2009 major scientific and technological projects in Guangdong province (2009A080304004)“Research on forging reduction of heavy forgings and technology of using waste heat and its application in shipping industry”.
In this paper, based on the basic theory of heat transfer, the long-axis heavy forgings as the research object, and formulate the process specification of remained heat treatment, finite element method combited with experiment examination was used to simulate heat treatment process, then optimization algorithm was used to optima parameter of heat treatment process.The main research contents and conclusions of this paper are summarized as follows:
(1)Based on chemical composition、thermophysical properties、TTT and latent heat, a library of 35CrMo has been developed.A mathematical model is established to calculate the temperature of the surface of forging from the temperature data of the center of forging.This algorithm of FDM (Finite Difference Method) is programmed and the curve of heat transfer coefficient is obtained.
(2)The process of remained heat treatment is validated by experiment, the results show that this process is feasible.The forging made with the new process have improved surface wear-ability and inner structure of forging.Also, the new technique has advantages in shortening heat preservation, saving heating time more than 58%.
(3)A FEM (Finite Element Method) model of remained heat treatment process is built based on the FEM software DEFORM-3D.The model is validated by experiments, the simulation results basically fit those measured in experiments.The results show that due to chilling effect of quenching, the temperature differences between surface and core of the forging are tremendous;During the process, the stress of the round areas of forging is maximum at 5min, which is 410Mpa, and the stress has no possibilities of plastic deformation.After quenching-tempering, the main organization of forging is temper-sorbite, with good mechanical properties.
(4) A method combining SVM (Support Vector Machine) with GA (Genetic Algorithm) to optimize heat treatment process parameters of long-axis heavy forgings was put forward, taking quenching temperature、quenching holding time、tempering temperature and tempering holding time, and so on as optimizing parameters.Heating time and residual stress as optimizing objective, the final heat treatment process of long-axis heavy forgings is optimized.Firstly, through the orthogonal experiment of the technical factors in heat treatment,the mapping model of SVM was established based on data above experiment.Then, through optimizing the model by useness of GA, the optimum heat treatment process parameters have been given.The results show that compared to conventional process, this process has save heating time more than 22% and the residual stress more than 24%.
引文
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