基于工藝參數(shù)的7005鋁合金力學(xué)性能的支持向量回歸預(yù)測(cè)
發(fā)布人：上海艾荔艾金屬材料有限公司bt990.com.cn
更新時(shí)間：2015-12-19
根據(jù)7005鋁合金在不同工藝參數(shù)(擠壓溫度、擠壓速度、淬火方式和時(shí)效條件)下的力學(xué)性能(抗拉強(qiáng)度σb、屈服強(qiáng)度σ0.2和硬度HB)實(shí)測(cè)數(shù)據(jù)集，應(yīng)用基于粒子群算法(PSO)尋優(yōu)的支持向量回歸(SVR)結(jié)合留一交叉驗(yàn)證(LOOCV)的方法，對(duì)7005鋁合金力學(xué)性能進(jìn)行建模和預(yù)測(cè)研究，并與偏最小二乘法(PLS)、反向傳播人工神經(jīng)網(wǎng)絡(luò)(BPNN)和兩者結(jié)合的PLS-BPNN模型的預(yù)測(cè)結(jié)果進(jìn)行比較。
基于工藝參數(shù)的7005鋁合金力學(xué)性能的支持向量回歸預(yù)測(cè)Quantitative prediction of mechanical properties of 7005 Al alloys from processing parameters via support vector regression根據(jù)7005鋁合金在不同工藝參數(shù)(擠壓溫度、擠壓速度、淬火方式和時(shí)效條件)下的力學(xué)性能(抗拉強(qiáng)度σb、屈服強(qiáng)度σ0.2和硬度HB)實(shí)測(cè)數(shù)據(jù)集，應(yīng)用基于粒子群算法(PSO)尋優(yōu)的支持向量回歸(SVR)結(jié)合留一交叉驗(yàn)證(LOOCV)的方法，對(duì)7005鋁合金力學(xué)性能進(jìn)行建模和預(yù)測(cè)研究，并與偏最小二乘法(PLS)、反向傳播人工神經(jīng)網(wǎng)絡(luò)(BPNN)和兩者結(jié)合的PLS-BPNN模型的預(yù)測(cè)結(jié)果進(jìn)行比較。結(jié)果表明：基于SVR-LOOCV法的預(yù)測(cè)精度最高，對(duì)3種力學(xué)性能(σb、σ0.2和HB)預(yù)測(cè)的均方根誤差(RMSE)分別為4.531 9 MPa、14.550 8 MPa和HB1.414 2，其平均相對(duì)誤差(MRE)分別為0.72%、2.61%和0.66%，均比PLS、BPNN和PLS-BPNN方法預(yù)測(cè)的RMSE和MRE要小。
The support vector regression (SVR) approach based on the particle swarm optimization (PSO) for its parameter optimization, combined with leave-one-out cross validation (LOOCV), was proposed to predict the mechanical properties (tensile strength σb, yield strength σ0.2?and hardness HB) of 7005 Al alloys under different processing parameters including extrusion temperature, extrusion velocity, quenching type and aging time. The results strongly support that the prediction precision of SVR-LOOCV method is superior to those of partial least squares (PLS), back-propagation neural networks (BPNN) and their combination PLS-BPNN model by applying the identical dataset. The root mean square errors (RMSE) for σb, σ0.2?and HB achieved by SVR-LOOCV are 4.531 9 MPa, 14.550 8 MPa and HB 1.414 2, respectively, and their mean relative errors (MRE) are 0.72%, 2.61% and 0.66%, respectively, which are less than those predicted by PLS, BPNN or PLS-BPNN approach.
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