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基于GA-BP和PSO-BP神經(jīng)網(wǎng)絡(luò)的6061鋁合金板材流變應(yīng)力預(yù)測模型
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機(jī)械電子工程學(xué)院 南京林業(yè)大學(xué)

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中圖分類號(hào):

TG146.2

基金項(xiàng)目:

江蘇省高等學(xué)校自然科學(xué)基金（18KJB460020），南京林業(yè)大學(xué)高水平（高等教育）科學(xué)基金（GXL2018020）和南京林業(yè)大學(xué)青年科技創(chuàng)新基金（CX2018027）


Prediction model on flow stress of 6061 aluminum alloy sheet based on GA-BP and PSO-BP neural networks
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Affiliation:

College of Mechanical and Electronic engineering,Nanjing Forestry University

Fund Project:

Natural Science Foundation of Jiangsu Higher Education Institutions of China(18KJB460020), High-level (Higher education) Science Foundation of Nanjing Forestry University(GXL2018020) and the Youth Science and Technology Innovation Foundation of Nanjing Forestry University(CX2018027).

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    摘要:

    6061鋁合金作為一種熱可強(qiáng)化鋁合金，具有良好的成形性能，但是其塑性流變應(yīng)力受最終熱處理工藝的加熱溫度、保溫時(shí)間和冷卻方式等參數(shù)的影響很大。因此，為了獲得最終熱處理工藝參數(shù)對(duì)6061鋁合金板材的塑性性能及流變行為的影響，試驗(yàn)中以6061-T6鋁合金板材為研究對(duì)象，通過單向拉伸試驗(yàn)、金相實(shí)驗(yàn)和硬度測試等方法研究不同熱處理工藝參數(shù)(加熱溫度為500、530、560和590℃、保溫時(shí)間2小時(shí)、冷卻方式為空冷)對(duì)6061鋁合金塑性性能和硬度的影響。通過單向拉伸試驗(yàn)獲取不同熱處理工藝參數(shù)條件下6061鋁合金的真實(shí)應(yīng)力應(yīng)變曲線；借助BP、GA-BP和PSO-BP神經(jīng)網(wǎng)絡(luò)構(gòu)建不同熱處理溫度條件下6061鋁合金的本構(gòu)關(guān)系模型。研究結(jié)果表明BP、GA-BP和PSO-BP神經(jīng)網(wǎng)絡(luò)模型均能較好的擬合不同熱處理溫度條件下6061鋁合金的流變行為，但是PSO-BP神經(jīng)網(wǎng)絡(luò)模型對(duì)6061鋁合金流變應(yīng)力的預(yù)測精度更高，網(wǎng)絡(luò)預(yù)測性能更優(yōu)越，其平均絕對(duì)誤差(MAE),平均相對(duì)誤差(AARE)和相關(guān)系數(shù)(R2)分別為1.89，1.56%和0.9965。

    Abstract:

    6061 aluminum alloy, as a kind of heat strengthened aluminum alloy, has good formability, but its plastic flow stress is greatly affected by the final heat treatment parameters, such as heating temperature，holding time and cooling method. Therefore, taking 6061-T6 aluminum alloy cold-rolled sheet as the research object, the plastic deformation behavior of 6061 aluminum alloy under different heat treatment temperatures (500 °C, 530 °C, 560 and 590 °C) were analyzed through uniaxial tensile test, metallographic test and microhardness test. Combined with experimental data and BP, GA-BP and PSO-BP neural networks, the constitutive models of this material under different heat treatment temperature conditions were constructed. The results show that BP, GA-BP and PSO-BP neural network models can better fit the flow behavior of 6061 aluminum alloy under different heat treatment temperature conditions, but PSO-BP neural network model has higher prediction accuracy and performs well in predicting the flow stress of 6061 aluminum alloy , its average absolute error (MAE), average relative error (AARE) and the correlation coefficient (R2) are 1.89, 1.56% and 0.9965, respectively.

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丁鳳娟,賈向東,洪騰蛟,徐幼林.基于GA-BP和PSO-BP神經(jīng)網(wǎng)絡(luò)的6061鋁合金板材流變應(yīng)力預(yù)測模型[J].稀有金屬材料與工程,2020,49(6):1840~1853.[Feng-juan Ding, Xiang-dong Jia, Teng-jiao Hong, You-lin Xu. Prediction model on flow stress of 6061 aluminum alloy sheet based on GA-BP and PSO-BP neural networks[J]. Rare Metal Materials and Engineering,2020,49(6):1840~1853.]
DOI：10.12442/j. issn.1002-185X.20190884

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  • 收稿日期:2019-10-26
  • 最后修改日期:2020-05-01
  • 錄用日期:2019-12-10
  • 在線發(fā)布日期: 2020-07-09
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