Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

Statistical structure of the velocity field in cavitating flow around a 2D hydrofoil

Transformation of flow turbulence structure with cavitation occurrence, determination of the flow conditions favorable for nucleation of cavitation bubbles, influence of the statistical structure of turbulence on this process and the inverse effect of cavitation on the flow dynamics are challenging problems in modern fluid mechanics. The paper reports on the results of statistical processing of the velocity fields measured by a PIV technique in cavitating flow over a 2D symmetric hydrofoil for four flow conditions, starting from a cavitation-free regime and finishing by unsteady cloud cavitation. We analyze basic information on the statistical structure of velocity fluctuations in the form of histograms and Q-Q diagrams along with profiles of the mean velocity and turbulent kinetic energy. The research reveals that the flow turbulence pattern and distributions of turbulent fluctuations change significantly with the cavitation development. Under unsteady cloud cavitation conditions, the probability density function of the fluctuating velocity has a two-mode distribution, which indicates switching of two alternating flow conditions in a region above the hydrofoil aft part due to periodic passing of cavitation clouds. Behaviors of the mean and most probable velocities unexpectedly appear to be different with a monotonous increase of the incoming flow velocity. This finding must be caused by modification of the skewness coefficient of the fluctuating velocity.     

野外砾石统计方法的应用与对比

砾石对于研究构造活动和气候变化具有重要意义，而砾石粒径是其中的重要参数，如何准确地获取粒径数据则是砾石统计分析中的基础性工作。为了能更全面地了解和使用砾石统计方法，对地表砾石统计和地层砾石统计这2种方法从操作流程、抽样过程、样本容量、误差分析和适用条件等方面进行了分析和总结。地表砾石统计，主要有沃尔曼法和面积样本法，前者强调抽样的随机性，多适用于对粒径>2 mm砾石的统计，常用于研究砾质河床的砾石粒径顺流变化趋势；后者适用的粒径范围更广，可达细砂级沉积物，多用于探索河床砾-砂转换带和生物栖息环境划分等领域。地层砾石统计，大多使用体积样本法，强调砾石的成层性，可根据研究目的确定相应的网格筛，粒径范围也可达细砂级别，多用于沉积地层粒度对气候与构造的响应、河床监测和泥沙运移等方面。砾石统计方法的选择很大程度上取决于研究目的及野外工作条件，可根据实际情况灵活选择。     

Arbitrage‐free call option surface construction using regression splines

In this work, we suggest a novel quadratic programming‐based algorithm to generate an arbitrage‐free call option surface. The empirical performance of the proposed method is evaluated using S&P 500 Index call options. Our results indicate that the proposed method provides a more precise fit to observed option prices than other alternative methodologies. Copyright © 2014 John Wiley & Sons, Ltd.     

Information percolation and cutoff for the random‐cluster model

We consider the random‐cluster model (RCM) on with parameters p∈(0,1) and q ≥ 1. This is a generalization of the standard bond percolation (with edges open independently with probability p) which is biased by a factor q raised to the number of connected components. We study the well‐known Fortuin‐Kasteleyn (FK)‐dynamics on this model where the update at an edge depends on the global geometry of the system unlike the Glauber heat‐bath dynamics for spin systems, and prove that for all small enough p (depending on the dimension) and any q>1, the FK‐dynamics exhibits the cutoff phenomenon at with a window size , where λ_∞ is the large n limit of the spectral gap of the process. Our proof extends the information percolation framework of Lubetzky and Sly to the RCM and also relies on the arguments of Blanca and Sinclair who proved a sharp mixing time bound for the planar version. A key aspect of our proof is the analysis of the effect of a sequence of dependent (across time) Bernoulli percolations extracted from the graphical construction of the dynamics, on how information propagates.     

A Statistical Study of the Correlation between Geomagnetic Storms and M ≥ 7.0 Global Earthquakes during 1957–2020

In order to find out whether the geomagnetic storms and large-mega earthquakes are correlated or not, statistical studies based on Superposed Epoch Analysis (SEA), significance analysis, and Z test have been applied to the Dst index data and M ≥ 7.0 global earthquakes during 1957–2020. The results indicate that before M ≥ 7.0 global earthquakes, there are clearly higher probabilities of geomagnetic storms than after them. Geomagnetic storms are more likely to be related with shallow earthquakes rather than deep ones. Further statistical investigations of the results based on cumulative storm hours show consistency with those based on storm days, suggesting that the high probability of geomagnetic storms prior to large-mega earthquakes is significant and robust. Some possible mechanisms such as a reverse piezoelectric effect and/or electroosmotic flow are discussed to explain the statistical correlation. The result might open new perspectives in the complex process of earthquakes and the Lithosphere-Atmosphere-Ionosphere (LAI) coupling.     

Dimensional scaling of flame propagation in discrete particulate clouds

The critical dimension necessary for a flame to propagate in suspensions of fuel particles in oxidiser is studied analytically and numerically. Two types of models are considered: First, a continuum model, wherein the individual particulate sources are not resolved and the heat release is assumed spatially uniform, is solved via conventional finite difference techniques. Second, a discrete source model, wherein the heat diffusion from individual sources is modelled via superposition of the Green's function of each source, is employed to examine the influence of the random, discrete nature of the media. Heat transfer to cold, isothermal walls and to a layer of inert gas surrounding the reactive medium are considered as the loss mechanisms. Both cylindrical and rectangular (slab) geometries of the reactive medium are considered, and the flame speed is measured as a function of the diameter and thickness of the domains, respectively. In the continuum model with inert gas confinement, a universal scaling of critical diameter to critical thickness near 2:1 is found. In the discrete source model, as the time scale of heat release of the sources is made small compared to the interparticle diffusion time, the geometric scaling between cylinders and slabs exhibits values greater than 2:1. The ability of the flame in the discrete regime to propagate in thinner slabs than predicted by continuum scaling is attributed to the flame being able to exploit local fluctuations in concentration across the slab to sustain propagation. As the heat release time of the sources is increased, the discrete source model reverts back to results consistent with the continuum model. Implications of these results for experiments are discussed.     

初始微观结构缺陷和断裂的温度依赖及相关性

纳米线(NW)结构内的微观结构缺陷对NW的机械性能存在一定的影响。NW断裂位置的预测关系着纳米器件应用的寿命,进而引起了人们的广泛关注。在本工作中,基于统计分析,分别研究了单晶铜纳米线(Cu NW)拉伸过程中出现的断裂位置以及在应力屈服点处产生的初始微观结构缺陷(初始缺陷)的位置对温度的依赖性,进一步探究了两者之间的联系。利用分子动力学(MD)模拟了单晶Cu NW在20~300 K的温度范围内的拉伸状态,共包含6个体系,各温度体系包含300个独立的样本。基于机器学习,采用density-based spatial clustering of applications with noise (DBSCAN)算法,将hexagonal close-packed (hcp)原子划分为各个初始缺陷以进一步确定其位置。统计结果显示,当温度低于50 K时,初始缺陷的位置集中在NW的两端。随着模拟温度的上升,MD模拟结果展现了单晶Cu NW的拉伸过程中的杨氏模量、平均屈服应力、平均势能等机械性能对温度的依赖性。温度的升高进一步促使了更多初始缺陷的产生,并使得初始缺陷的位置由统计分布的两端向中间平均化。与初始缺陷相比,各温度下的断裂位置集中在两端。统计结果表明,模拟的温度范围对NW的断裂位置无明显影响,但对初始缺陷的产生具有明显影响。当温度低于100 K时,初始缺陷的位置分布与断裂位置分布呈现了一致性。由于两者具有不同的温度依赖,其差异随着温度的上升逐渐显现。对不同温度下的微观结构形变行为观察发现,断裂失效明显受到NW两端的表面效应和阻挡效应的影响。最终的断裂位置受塑性形变中后期的影响,与应力屈服区产生的初始缺陷无直接联系。