前混合水射流喷丸强化表面粗糙度预测  

Research on Surface Roughness Prediction of Premixed Water Jet Peening Strengthening

作　　者：王瑞红[1] 贾环环[1] 宋胜伟[2] 

机构地区：[1]黑龙江科技学院计算机与信息工程学院,黑龙江哈尔滨150027 [2]黑龙江科技学院机械工程学院,黑龙江哈尔滨150027

出　　处：《实验室研究与探索》2011年第9期21-23,27,共4页Research and Exploration In Laboratory

基　　金：黑龙江省教育厅科学技术研究资助项目(11541309);哈尔滨市科技创新人才研究专项资金项目(2009RFXXG203)

摘　　要：选择对喷丸表面粗糙度起主要影响作用的喷丸压力、喷嘴扫描速度和靶距3个因素,各6个水平,选用直径为0.25 mm的玻璃弹丸,应用前混合水射流对2A11铝合金进行喷丸试验;采用针描法和SJ-201 Surface Roughness Tester测量喷丸表面粗糙度;基于表面粗糙度试验数据,应用多元线性回归和多项式回归分析方法建立喷丸表面粗糙度数学模型,并将模型应用于喷丸表面粗糙度预测,研究表明:多元线性回归预测模型,计算精度高、泛化能力强、预测效果好,平均相对误差为13.37%,能够满足工业生产对喷丸表面粗糙度预测精度的要求,具有较大的实用价值。The surface roughness prediction model of premixed water jet peening strengthening was established. It chooses the 2All aluminum alloy as the tests materials, the glass bullet whose diameter is 0. 25 mm, the peening pressure, nozzle scanning speed and the target distance which play a major role in peening surface quality, and the six levels of each prarmeter to do the premixed water.jet peening experiments. The needle depiction method and SJ-201 Surface Roughness ＇Fester were chosen to measure the surface roughness. Based on the experiments data, the surface roughness mathematical model was established by multiple linear regression and polynomial regression methods, and applied in the surface roughness prediciton of the peening. The results show that the model of multiple linear regression has many advantages such as high calculation accuracy, good generalization ability, better predicted effects and the average relative error is 13.37% , it can meet the requirements of peening＇ s surface roughness prediction accuracy in the industrial production, and has better practical values.

关 键 词：水射流 喷丸强化 表面粗糙度 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]