针对多主轴头五轴机床加工时需手动规划空行程的问题,提出一种空行程轨迹自动规划方法.在机床运动学建模的基础上,针对刀位点轨迹和刀轴矢量,分别提出基于最短路径的刀位点轨迹规划和无碰撞刀轴矢量规划,将空行程规划问题转化为参数优化问题.为了简化碰撞检测,提出两级相交检测算法.通过加权系数法建立空行程规划的目标函数,并采用遗传算法求解,规划出一条无碰撞、运动时间最短、旋转轴角度变化量最小、末端轨迹最短的具有柔性的理想轨迹.在Vericut建立的仿真平台上,验证了该方法的可行性和有效性.所提方法具有拓展性,通过多次分解轨迹求取棱交点能够实现复杂工件的空行程规划.

This article focuses on the research of fast-positioning method of one-plane two-hole feature, while aiming at the precise measurement and positioning of workpieces at a production site, and it builds a set of on machine measurement system based on the computer numerical control (CNC) platform and line laser. Using the isotropy of the spherical surface, global calibration of the line laser on machine measurement system is achieved through a standard sphere. The plane and holes are used as the positioning features. Based on the measurement data obtained by the line laser sensor, a fitting algorithm is designed and the size and position information of the relevant features are calculated. Finally, a test piece is used for the fast positioning test. The test results exhibit that the on machine measurement system can meet the requirements of workpiece positioning and alignment in production and processing, and the system also possesses high accuracy and stability.

Laser-induced breakdown spectroscopy (LIBS) has been widely used in the field of material detection due to its advantages such as no sample preparation, multi-clement simultaneous analysis and rapid detection. Selecting appropriate parameters of the experimental device is an important prerequisite for achieving a good detection effect. Based on the single factor experiment, a multi-factor response surface modal for optimizing the parameters of the laser-induced breakdown spectroscopy experimental device is established with laser energy, delay time and depth of focus as the influencing factors and with spectral signal-to-background ratio as the response factor. The influence of different influencing factors and their coupling effects on the spectral quality arc studied and the obtained optimal experimental parameters are laser energy of 114 mJ, delay time of 1. 86 mu s, and depth of focus of 1. 75 mm. Finally, the optimal experimental parameters obtained by the response surface methodology arc verified by experiments. The mean value of the signal-to-background ratio of the spectral line is 7.45, which is 1.92% higher than that by the single factor method, and the relative standard deviation is 3.16%, which is 0.94% lower than that by the single factor method. The results show that the response surface methodology is more effective and reliable than the single factor method.

随着我国社会发展,废旧产品的数量迅速增长,废旧铝随之大量产生.铝是优良的再生资源,传统分选技术不能将废旧铝按各自的成分牌号进行精细分类,导致很多优质铝资源被降级使用,造成巨大的浪费.研究了主成分分析(PCA)结合极限学习机(ELM)算法辅助激光诱导击穿光谱(LIBS)技术在铝合金分类识别方面的应用.选用2种系列的4个牌号铝合金作为实验样品,通过LIBS技术激发实验样品获得420组光谱数据.对原始光谱数据进行了预处理,并选取样品铝合金中5种主要差异元素(Mg、Mn、Cu、Fe和Si)的21条特征谱线构成了420×21的光谱数据矩阵,通过主成分分析对光谱数据进一步降维,使得模型输入变量从21个降至8个.选取120组光谱数据作为训练集,建立了基于极限学习机的铝合金分类模型,余下300组数据作为测试集.研究发现在主要非铝元素(Mg、Mn、Cu、Fe和Si)含量差异只有0.0021％～3.68％的情况下,PCA-ELM分类模型的平均识别准确率达到98.01％,标准差为0.82％,建模时间为0.081 s.结果表明,PCA-ELM分类模型有着很高的效率及稳定性,将其与LIBS技术结合可以适用于工业快速分类领域,为精细分类行业提供了一种参考方法.

In this paper, laser-induced breakdown spectroscopy (LIES) was used to obtain 320 sets of spectral data at different positions on the surfaces of aluminum alloy samples. Then, these spectral data were preprocessed, and 20 characteristic spectral lines of the six main elements in aluminum alloy were selected to form a 320 X 20 spectral data matrix. Next, the 20 variables that were inputted into the model were reduced to 6 through principal component analysis. Finally, the reduced-dimensional spectral data were inputted into the radial basis function neural network model to establish multivariate calibration models for five main nonaluminum elements (Si, Fe, Cu, Mn, and Mg) in aluminum alloy. The results revealed that the mean goodness of fit of the model was 0.978 and its mean root mean square error was 0.31 %. Principal component analysis combined with a radial basis function neural network can effectively reduce parameter fluctuations, correct matrix effects, and improve the accuracy and stability of the model quantitative analysis; in particular, this combination can significantly improve the accuracy of analysis of elements with relatively low content, such as Fe, Si, and Cu.

建立了一套废旧铝喷气式分离系统,使用不同气压吹不同体积的废旧铝进行实验,测量其被吹离距离,获得230组训练集、80组测试集数据,从而建立径向基网络模型,拟合优度可达93％,均方根误差低至2.99.结果 表明该套系统构造简单、识别效果准确,可以根据废旧铝具体情况预测出吹离距离,选取合适大小的气流进行自适应分离操作,实现对不同成分的废旧铝进行精细分离的目的.

Laser-induced breakdown spectroscopy is widely used in the material detection field because of its advantages, including online noncontact measurement and non-destructive analysis. Selecting proper analytical lines is an important prerequisite for achieving a good detection effect. 'Phis study proposed a method for adaptively selecting analytical and internal standard lines from the original spectral data of LIBS based on the global optimization ability of the genetic algorithm (GA) and the local search ability of the particle swarm optimization (PSO) algorithm. We quantitatively analyzed four major non-aluminum elements (i. e. , Mg, Mn, Si, and Fe) in aluminum alloys using the analytical and internal standard lines selected using this method. The mean values of the goodness of fit, root mean square error, and relative standard deviation arc 0. 972, 0. 35%, and 3. 53%, respectively. The results obtained by traversing all other analytical lines for a quantitative analysis and comparing their calibration performances show that the analytical and internal standard lines obtained by the PSO-GA search optimization arc optimal analytical spectral lines under current experimental conditions.

AIM:To study the effects of combination of fosinopril and losartan in treating early diabetic nephropathy. METHODS:Ninty patients of type 2 diabetes mellitus (DM) with early diabetic nephropathy were randomly divided into three groups. All patients were kept on previous management of DM. The patients in the fosinopril group were treated with fosinopril 10 mg daily, the losartan group with losartan (50 mg) daily and the combination group with fosinopril (10 mg) and losartan 50 mg daily. Period of treatment for all groups was 12 wk. Urinary albumin excretion rate (UAER), glycohemoglobin A_(1c) (HbA_(1c)), blood urea nitrogen(BUN), serum creatinine (Scr) , uratic acid (UA), the mean arterial pressure (MAP) were observed before and after the treatment. RESULTS: UAER after treatment of three groups were all decreased (P0.01). Interestingly, reduction of UAER was remarkable in the combination group[(76±(39) mg)·24 h~(-1),vs (55±36),(46±42) mg·(24 h~(-1)),(P(0.05]).) HbA_(1c) after treatment in the fosinopril group and the combination group were significantly (decreased)(P0.05). While, MAP of three groups were also decreased (P0.01). No significant differences in blood pressure were shown among three groups after the treatment. CONCLUSION: Fosinopril and losantan can lower albuminurea in the patients with early diabetic nephropathy. Furthermore there is a synergism with combination of the two drugs. Besides independent from anti-hypertension effect,renin-angiotensin system drugs also have the renal protective effect.