本文選題：混合硬化模型 + TRIP　； 參考：《工程設計學報》2017年06期

【摘要】：為了實現(xiàn)扭曲回彈的精確預測,在高強度鋼板沖壓成形中必須對工藝參數(shù)進行有效控制�；谠囼灁�(shù)據(jù),利用響應面法和遺傳算法對Voce非線性各向同性硬化模型和Chaboche非線性隨動硬化模型組成的混合硬化模型參數(shù)進行反求�；谌S空間兩異面直線夾角,提出一種評價扭曲回彈的指標。利用液壓機進行TRIP780高強鋼雙C梁扭曲回彈試驗,并且利用三坐標測量儀測量扭曲回彈角。利用基于雙C梁有限元模型建立的回彈角響應面模型預測值逼近回彈角的測量值,從而確定混合硬化模型參數(shù)。為了實現(xiàn)扭曲回彈的精確預測,在沖壓成形中必須對有效的工藝參數(shù)進行控制。基于已驗證的雙C梁有限元模型,利用極差分析方法對相關因素進行分析,確定影響扭曲回彈的關鍵因素。研究結果表明,對扭曲回彈影響較大的因素依次為摩擦系數(shù)μ、壓邊力FN、凹模圓角半徑R1和R2,為扭曲回彈的有效控制提供一定的理論依據(jù)。
[Abstract]:In order to predict the distortion springback accurately, the process parameters must be controlled effectively in high strength steel sheet stamping. Based on the experimental data, the parameters of the mixed hardening model composed of Voce nonlinear isotropic hardening model and Chaboche nonlinear servo hardening model are obtained by using response surface method and genetic algorithm. Based on the angle between the two cross-plane lines in three dimensional space, an index to evaluate the distortion springback is proposed. The twisting springback test of TRIP780 high strength steel double C beam was carried out by hydraulic press, and the torsional springback angle was measured by three coordinate measuring instrument. The predictive value of the springback angle response surface model based on the finite element model of double C beam is used to approximate the measured value of the springback angle, and the parameters of the mixed hardening model are determined. In order to predict the distortion springback accurately, the effective process parameters must be controlled in stamping forming. Based on the verified double C beam finite element model, the relative factors are analyzed by using the range analysis method, and the key factors affecting the distortion springback are determined. The results show that the factors which have a great effect on the twist springback are friction coefficient 渭, blank holder force FN, die radius R 1 and R 2, which provides a theoretical basis for the effective control of twist springback.
【作者單位】： 西南交通大學機械工程學院;
【基金】：國家自然科學基金資助項目(51005193) 國家大學生創(chuàng)新創(chuàng)業(yè)訓練計劃項目(201710613033)
【分類號】：TG386
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本文編號：2080989