(1. 太原重型機(jī)械學(xué)院 機(jī)械工程系, 太原 030024;
2. 燕山大學(xué) 機(jī)械工程學(xué)院, 秦皇島 066004)
摘 要: 斜軋穿孔中毛管質(zhì)量與許多工藝參數(shù)，如輥型、送進(jìn)角、頂頭前伸量及溫度，以及設(shè)備性能參數(shù)如穿孔機(jī)剛度、加工精度和頂桿振動(dòng)等有關(guān)。傳統(tǒng)的軋制理論難以解決其質(zhì)量問(wèn)題，應(yīng)用人工神經(jīng)網(wǎng)絡(luò)則能較好地解決毛管質(zhì)量的預(yù)測(cè)問(wèn)題。應(yīng)用實(shí)測(cè)的工藝參數(shù)與其對(duì)應(yīng)的毛管精度參數(shù)，訓(xùn)練和學(xué)習(xí)網(wǎng)絡(luò)的權(quán)值和閾值，建立起模擬穿孔機(jī)生產(chǎn)的數(shù)學(xué)模型，即網(wǎng)絡(luò)模型。預(yù)測(cè)了毛管偏差及合理的工藝參數(shù)。
關(guān)鍵字: 斜軋穿孔； 神經(jīng)網(wǎng)絡(luò)； 數(shù)學(xué)模型
predicting deviation of tube in cross piercing process
（1. Department of Engineering， Taiyuan Heavy Machinery Institute，
Taiyuan 030024， P.R.China；
2. College of Mechanical Engineering， Yanshan University，
Qinhuangdao 066004，P.R.China）
Abstract: The quality of tube hollow in cross piercing process is concerned with complicated factors， such as technical parameters including roller shape， feed angle， plug advance and temperature, and the piercing mill properties including stiffness and precision of the mill manufactured， vibration of the plug and driven systems. It is difficult to solve further problems on qualities using traditional rolling theory, and the prediction of tube hollow qualities is even more difficult. The artificial neural networks were used to solve the above problems easily. Weights and thresholds of the networks were learnt by experimental data and the model has been established in production. Technical parameters optimized and deviation of tube have been predicted.
Key words: cross piercing； artificial neural networks； mathematical modal
