A new thermomechanical model of cutting applied to turning operations. Part II. Parametric study

In Part I of this work, Molinari and Moufki [Int. J. Mach. Tools Manufact., this issue], an analytical model of three-dimensional cutting is developed for turning processes. To analyse the influences of cutting edge geometry on the chip formation process, global effects such as the chip flow direction and the cutting forces, and local effects such as the temperature distribution and the surface contact at the rake face have been investigated. In order to accede to local parameters, the engaged part in cutting of the rounded nose is decomposed into a set of cutting edge elements. Thus each elementary chip, produced by a straight cutting edge element, is obtained from an oblique cutting process defined by the corresponding undeformed chip section and the local cutting angles. The present approach takes into account the fact that for each cutting edge element the local chip flow is imposed by the global chip movement. The material characteristics such as strain rate sensitivity, strain hardening and thermal softening, the thermomechanical coupling and the inertia effects are considered in the modelling. A detailed parametric study is provided in this paper in order to analyse the effects of cutting speed, depth of cut, feed, nose radius and cutting angles on cutting forces, global chip flow direction and temperature distribution at the rake face. The influence of friction at the tool–chip interface is also discussed.     

虚拟数控车削加工技术研究

介绍了虚拟数控机床(VMT)的发展背景以及在虚拟制造中的作用。选择了应用普及、数量大的数控车床作为研究对象。通过对虚拟数控车床的功能描述和分析，提出了虚拟数控车床的系统体系结构框架，并给出了它的实现方法及应用。     

基于乘客多运动行为的公交客流计数判定方法

针对基于单目视觉的公交乘客人数统计判定方法不稳定、计数结果不准确的现状,结合公交车门附近乘客运动行为的复杂性和多样性,以及乘客运动行为对计数判定方法的干扰,给出一种基于乘客多运动行为分析的计数判定方法。采用轨迹聚类的方式对乘客运动行为进行分析,结合轨迹的空间特征和方向特征计算轨迹距离,并使用层次聚类方法进行聚类。分析聚类结果中每一类别所对应乘客类的运动行为,讨论各乘客类的运动行为对常用计数判定准则的影响,由此提出一种改进的公交车客流计数判定方法。利用采集的乘客上下公交车视频图像进行实验,结果表明,该方法能获得较高的统计精度和较好的稳定性。     

An architectural counterpoint in Universal Everything: Fluid Bodies exhibition

ABSTRACT

This exhibition review focuses on the quest for weaving boundaries between body-form-space-and material relationship using immersive technologies. Emerging as an architectural counterpoint, Universal Everything: Fluid Bodies exhibition held in Borusan Contemporary focuses on how we perceive the motion and form in relation to it, as well as fusing senses of kinaesthetic and synaesthetic through a data driven and motion-based visual representation. Through these algorithms, the exhibition displays how neuroarchitecture reminds us of the senses of perception. The architectural counterpoint, intention as an interaction and encounter of the body with ‘the machine’ as the ‘voyeur body’ and how this observational dialogue becomes a research methodology in understanding the nature of movement in space through digital tools.     

一种直接计算空间弯管转折角的新公式

提出了一种直接计算空间弯管转折角的新公式。为了验证新公式的正确性,将传统的解析计算公式进行恒等变形后所得出的公式与新公式进行了比较分析,证明了新公式具有计算简便、涉及参数少、实用性强等优点。以宝鸡市石头河水库引水工程为例,将新公式与传统公式进行了对比计算分析,进一步证实了新公式的正确性。     

宽垄沟灌垄上覆膜条件下土壤水分运移试验研究

为研究宽垄沟灌时灌水前后且有覆膜条件下水分在垄沟间的运移及再分布,采用对比试验方法,在大棚内模拟宽垄沟灌垄上栽培条件和灌水方案,采用中子仪进行水分观测。试验数据显示,采用垄上覆膜,灌水后垄沟水分的侧向入渗,表现出明显的侧向运移变化。垄背剖面两观测点表层,在灌水后144 h（6 d）,覆膜条件下分别达到田间持水量的80%...     

基于拓扑图基尔霍夫定律在行星轮系运动中的应用

应用基尔霍夫定律建立基于拓扑图的行星轮系数学模型——回路方程组、切割方程组来分析、解决行星轮系运动学的有关问题,求解行星轮系的传动比和角速度以及行星轮系中各个构件之间的受力和力矩。     

在线手写签名认证研究

手写签名认证是人工智能领域的一个重要课题,对它的深入研究具有重要的现实意义。文章采用基于概率统计的方法表示手写签名的速度特征,用vague集相似度表示书写压力特征,并通过细分的拐点表示笔画特征进行签名认证,最后,构造了一个在线手写签名认证系统。实验证明了该方法的有效性。     

Using space windowing for a preliminary analysis of complex time data in human component system studies. Examples with eye-tracking in advertising and car/head movements in driving

Empirical studies of human systems often involve recording multidimensional signals because the system components may require physical measurements (e.g., temperature, pressure, body movements and/or movements in the environment) and physiological measurements (e.g., electromyography or electrocardiography). Analysis of such data becomes complex if both the multifactor aspect and the multivariate aspect are retained. Three examples are used to illustrate the role of fuzzy space windowing and the large number of data analysis paths. The first example is a classic simulated data set found in the literature, which we use to compare several data analysis paths generated with principal component analysis and multiple correspondence analysis with crisp and fuzzy windowing. The second example involves eye-tracking data based on advertising, with a focus on the case of one category variable, but with the possibility of several space windowing models and time entities. The third example concerns car and head movement data from a driving vigilance study, with a focus on the case involving several quantitative variables. The notions of analysis path multiplicity and information are discussed both from a general perspective and in terms of our two real examples.     

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.