摘要
钛合金广泛应用于航空航天等领域,但存在加工困难和加工效率低的难题。磨料水射流技术具有冷态和能量密度高等特点,加工钛合金优势明显。为探究磨料水射流铣削钛合金的特性,综合考虑射流压力、磨料流量、靶距、射流角度和进给速度五个因素加工截面轮廓的影响,以TC4钛合金为试样进行单次铣削正交实验,并以截面轮廓最大深度h_(max)、最大宽度b_(max)和半深宽度b0.5为特征评价目标。首先通过量纲分析法建立了预测截面轮廓特征的经验模型,然后根据轮廓特征影响因素的非线性特点建立了BP神经网络预测模型,再引入PSO算法对模型的权重进行全局优化建立了PSO-BP预测模型。研究结果表明:三种模型预测的平均误差均小于10%,经验模型的预测精度高于BP网络模型,但低于PSO-BP网络模型。与BP网络模型相比,PSO-BP网络模型的h_(max)、b_(max)、b0.5的平均误差分别下降了25.09%、14.67%和9.66%,优化后的模型显著提高了截面轮廓特征预测的准确度,可为提高磨料水射流铣削钛合金效率提供工艺参数指导。
Titanium alloy is widely used in the field of aerospace, but it is difficult to process and suffers the problem of low processing efficiency. Abrasive waterjet has many advantages such as cold working and high energy density, and starts to be an effective method for processing titanium alloy. In order to study the characteristics titanium alloy processed by abrasive waterjet,milling experiments using TC4 specimen with a single pass were designed and conducted by considering the influences of jet pressure,abrasive flow rate, stand-off distance, jet angle, and feed rate. Moreover, the _(max)imum depth h_(max), _(max)imum width b_(max), and half-depth width b0.5of the milled surface were used as the evaluation objectives. An empirical model for predicting these three parameters was established by dimensional analysis, a BP neural network prediction model was established according to the strong non-linear characteristics of milling factors affecting the profile, and BP-PSO model was proposed by introducing PSO algorithm to optimize the weight of the model globally. The results show that the average errors of the three models are all less than 10%. The prediction accuracy of the empirical model was higher than that of the BP network model but lower than the PSO-BP network model.Compared with the BP network model, the average errors of _(max)imum depth h_(max), _(max)imum width b_(max), and half-depth width b0.5of PSO-BP network model are reduced by 25.09%, 14.67%, and 9.66%, respectively. The optimized model significantly improves the accuracy for predicting the surface profile features of TC4 titanium alloy milled by abrasive waterjet, and also provides reasonable process parameters.
作者
万亮
钱亦楠
涂翊翔
杜航
巫世晶
李登
WAN Liang;QIAN Yinan;TU Yixiang;DU Hang;WU Shijing;LI Deng(Hubei Key Laboratory of Waterjet Theory and New Technology,Wuhan University,Wuhan 430072;School of Power and Mechanical Engineering,Wuhan University,Wuhan 430072)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2022年第23期296-305,共10页
Journal of Mechanical Engineering
基金
国家自然科学基金(52175245,51805188)
湖北省自然科学基金(2021CFB462)
国家重点研发计划(2018YFC0808401)资助项目。
关键词
磨料水射流
钛合金
神经网络
粒子群算法
轮廓
abrasive waterjet
titanium alloy
neural network
particle swarm algorithm
profile
