發(fā)布時間：2018-08-26 16:54

【摘要】：利用田氏正交試驗(L27)進行磨料水射流切割06Cr19Ni10鋼板實驗,將切割后工件斷面表面粗糙度作為評測加工后工件表面質(zhì)量的標準,選取的過程參數(shù)變量為:射流壓力、噴嘴橫移速度、靶距、磨料粒徑和磨料流量。對實驗數(shù)據(jù)進行回歸分析,得到表面粗糙度關(guān)于5個過程參數(shù)變量的回歸模型,通過響應面分析法對過程參數(shù)進行優(yōu)化,得到最小表面粗糙度值對應的參數(shù)值。再利用人工神經(jīng)網(wǎng)絡(luò)對實驗樣本數(shù)據(jù)進行訓練學習,得到表面粗糙度的最小預測值。分別通過人工智能算法(遺傳模式搜索算法和模擬退火法)對過程參數(shù)優(yōu)化,然后通過整合的人工神經(jīng)網(wǎng)絡(luò)-遺傳模式搜索算法-模擬退火法技術(shù)對過程參數(shù)進行進一步優(yōu)化,得到最小表面粗糙度值對應的最佳工藝參數(shù)值。通過實驗驗證了尋優(yōu)結(jié)果的可靠性,通過對比,該整合技術(shù)相比單一的遺傳模式搜索算法或模擬退火法,大大降低了表面粗糙度值和縮短了尋優(yōu)時間。
[Abstract]:The abrasive water jet cutting 06Cr19Ni10 steel plate was carried out by using the field orthogonal test (L27). The surface roughness of the cut workpiece was taken as the standard for evaluating the surface quality of the machined workpiece. The process parameters were selected as follows: jet pressure, velocity of nozzle transverse shift. Target distance, abrasive particle size and abrasive flow rate. The regression model of surface roughness about five process parameter variables is obtained by regression analysis of experimental data. The process parameters are optimized by response surface analysis, and the corresponding parameters of minimum surface roughness are obtained. Then the artificial neural network is used to train and learn the experimental sample data, and the minimum predicted value of surface roughness is obtained. The process parameters are optimized by artificial intelligence (genetic pattern search algorithm and simulated annealing algorithm), and further optimized by integrated artificial neural network-genetic pattern search algorithm-simulated annealing. The optimum process parameters corresponding to the minimum surface roughness are obtained. The reliability of the optimization results is verified by experiments. Compared with the single genetic pattern search algorithm or simulated annealing method, the integration technique can greatly reduce the value of surface roughness and shorten the searching time.
【作者單位】： 江南大學機械工程學院;江南大學江蘇省食品先進制造裝備技術(shù)重點實驗室;
【基金】：國家自然科學基金項目(51275210) 教育部預研項目(62501036035)資助
【分類號】：TG48

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