本文選題：2.25Cr-1Mo-0.25V鋼　切入點(diǎn)：動態(tài)再結(jié)晶模型　出處：《太原科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：大型鍛件常被用為大型機(jī)械設(shè)備的核心部件。惡劣的工作環(huán)境對材料的宏觀力學(xué)性能和微觀組織狀態(tài)都提出了極高的要求。掌握這類大型鍛件在熱變形過程中微觀組織演變規(guī)律,對實(shí)現(xiàn)鍛造過程中對微觀組織的預(yù)測和工藝參數(shù)的制定具有重要的指導(dǎo)性意義。2.25Cr-1Mo-0.25V鋼是石化工業(yè)中的大型核心設(shè)備——加氫反應(yīng)器的主要制造材料。本文以該材料為對象,對其高溫?zé)嶙冃魏笪⒂^組織的演變進(jìn)行了分析和建模。本文在Gleeble-1500D熱模擬試驗(yàn)機(jī)上以40μm、70μm、120μm三種不同平均晶粒尺寸的2.25Cr-1Mo-0.25V鋼作為試樣,采用不同的熱變形參數(shù),進(jìn)行熱壓縮試驗(yàn)。通過對試驗(yàn)獲得的不同熱變形參數(shù)下的應(yīng)力應(yīng)變曲線以及變形后試樣的微觀組織,闡述了2.25Cr-1Mo-0.25V鋼熱變形組織演變規(guī)律,討論了熱變形參數(shù)對變形后試樣平均晶粒尺寸、動態(tài)再結(jié)晶體積分?jǐn)?shù)和晶粒尺寸的影響。然后以熱壓縮試驗(yàn)獲得的試驗(yàn)數(shù)據(jù)為基礎(chǔ),在討論了BP神經(jīng)網(wǎng)絡(luò)的算法、網(wǎng)絡(luò)結(jié)構(gòu)以及模擬退火算法相關(guān)參數(shù)后,建立了基于模擬退火算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)的2.25Cr-1Mo-0.25V鋼的動態(tài)再結(jié)晶模型,包括4-17-11-1的平均晶粒尺寸模型、3-14-1的臨界應(yīng)變模型、4-14-7-1的動態(tài)再結(jié)晶體積分?jǐn)?shù)模型及4-16-12-1的動態(tài)再結(jié)晶晶粒尺寸模型。對各個模型的訓(xùn)練結(jié)果進(jìn)行分析,各模型訓(xùn)練樣本相對誤差最大僅為4.71%,測試樣本最大僅為5.74%,說明了各模型具有較好的準(zhǔn)確性和泛化性能。將建立的各個模型,通過二次開發(fā)的形式嵌入DEFORM有限元軟件中,實(shí)現(xiàn)了對2.25Cr-1Mo-0.25V鋼熱變形過程中動態(tài)再結(jié)晶過程的模擬。通過模擬結(jié)果與一組2.25Cr-1Mo-0.25V鋼圓柱體鐓粗試驗(yàn)試驗(yàn)數(shù)據(jù)的比較,驗(yàn)證了所建立動態(tài)再結(jié)晶模型的準(zhǔn)確性。
[Abstract]:Large forgings are often used as the core components of large mechanical equipment. The poor working environment requires very high macroscopic mechanical properties and microstructures of materials. Master this kind of large forgings in the process of hot deformation. The law of the evolution of the organization, It is of great guiding significance to predict the microstructure in forging process and to formulate process parameters. Steel 2.25Cr-1Mo-0.25V is the main manufacturing material of hydrogenation reactor, which is a large core equipment in petrochemical industry. The evolution of microstructure after hot deformation at high temperature was analyzed and modeled. In this paper, three kinds of 2.25Cr-1Mo-0.25V steel with different average grain size, 40 渭 m, 70 渭 m and 120 渭 m, were used as samples on Gleeble-1500D thermal simulation machine, and different thermal deformation parameters were adopted. Thermal compression test was carried out. Through the stress-strain curves under different thermal deformation parameters obtained from the test and the microstructure of the specimens after deformation, the evolution law of hot deformation microstructure of 2.25Cr-1Mo-0.25V steel was described. The effects of thermal deformation parameters on average grain size, dynamic recrystallization volume integral number and grain size of deformed samples are discussed. Based on the experimental data obtained from thermal compression test, the algorithm of BP neural network is discussed. The dynamic recrystallization model of 2.25Cr-1Mo-0.25V steel based on BP neural network optimized by simulated annealing algorithm is established after the network structure and the parameters of simulated annealing algorithm. The critical strain model of 4-17-11-1, the critical strain model of 3-14-1, the dynamic recrystallization volume integral number model of 4-14-7-1 and the dynamic recrystallization grain size model of 4-16-12-1, are included. The training results of each model are analyzed. The maximum relative error of each model training sample is only 4.71, and the maximum of test sample is only 5.74, which shows that each model has better accuracy and generalization performance. Each model will be embedded in DEFORM finite element software through the form of secondary development. The dynamic recrystallization process of 2.25Cr-1Mo-0.25V steel during hot deformation was simulated, and the accuracy of the dynamic recrystallization model was verified by comparing the simulation results with that of a group of 2.25Cr-1Mo-0.25V steel cylinder upsetting test data.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TG142.1

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