Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.

Analysis on the turning point of dynamic in-plane compressive strength for a plain weave composite

Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate of composites was established. Then the upper limit of strain rate, restricted by stress equilibrium and constant loading rate, was rationally estimated and confirmed by tests. Within the achievable range of 0.001/s-895/s, it was found that the strength increased first and subsequently decreased as the strain rate increased. This feature was also reflected by the turning point(579/s) of the bilinear model for strength prediction. The transition in failure mechanism, from local opening damage to completely splitting destruction, was mainly responsible for such strain rate effects. And three major failure modes were summarized under microscopic observations: fiber fracture, inter-fiber fracture, and interface delamination. Finally, by introducing a nonlinear damage variable, a simplified ZWT model was developed to characterize the dynamic mechanical response. Excellent agreement was shown between the experimental and simulated results.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

TURNING LIGNIN INTO VALUABLE PRODUCTS

In a study published in Nature Catalysis on May 28,Chinese scientists unveiled a nove l approach to transforming lignin,a major component of woody biomass,into valuable chemical products(doi:10.1038/s41929-024-01165-w).This discovery could pave the way for more sustainable and efficient use of renewable resources.

Vibrations induced by tunnel boring machine in urban areas: In situ measurements and methodology of analysis

Excavation with tunnel boring machine(TBM)can generate vibrations,causing damages to neighbouring buildings and disturbing the residents or the equipment.This problem is particularly challenging in urban areas,where TBMs are increasingly large in diameter and shallow in depth.In response to this problem,four experimental campaigns were carried out in different geotechnical contexts in France.The vibration measurements were acquired on the surface and inside the TBMs.These measurements are also complemented by few data in the literature.An original methodology of signal processing is pro-posed to characterize the amplitude of the particle velocities,as well as the frequency content of the signals to highlight the most energetic bands.The levels of vibrations are also compared with the thresholds existing in various European regulations concerning the impact on neighbouring structures and the disturbance to local residents.

Integral Event-Triggered Attack-Resilient Control of Aircraft-on-Ground Synergistic Turning System With Uncertain Tire Cornering Stiffness

This article proposes an integral-based event-triggered attack-resilient control method for the aircraft-on-ground(AoG) synergistic turning system with uncertain tire cornering stiffness under stochastic deception attacks. First, a novel AoG synergistic turning model is established with synergistic reverse steering of the front and main wheels to decrease the steering angle of the AoG fuselage, thus reducing the steady-state error when it follows a path with some large curvature. Considering that the tire cornering stiffness of the front and main wheels vary during steering, a dynamical observer is designed to adaptively identify them and estimate the system state at the same time.Then, an integral-based event-triggered mechanism(I-ETM) is synthesized to reduce the transmission frequency at the observerto-controller end, where stochastic deception attacks may occur at any time with a stochastic probability. Moreover, an attackresilient controller is designed to guarantee that the closed-loop system is robust L2-stable under stochastic attacks and external disturbances. A co-design method is provided to get feasible solutions for the observer, controller, and I-ETM simultaneously. An optimization program is further presented to make a tradeoff between the robustness of the control scheme and the saving of communication resources. Finally, the low-and high-probability stochastic deception attacks are considered in the simulations. The results have illustrated that the AoG synergistic turning system with the proposed control method follows a path with some large curvature well under stochastic deception attacks. Furthermore,compared with the static event-triggered mechanisms, the proposed I-ETM has demonstrated its superiority in saving communication resources.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

The summer solstice is the turning point when light and temperature affect the growth of trees

The interaction between light and temperature is known to influence the seasonal growth of trees.Traditional studies on"light"primarily focus on day length,specifically short day(SD)or long day(LD)conditions.However,little research has been conducted on the process of transitioning between SD and LD conditions.

Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm(GQBA)to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats,thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and amulti-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments,and the experimental results for accuracy and convergence speed demonstrate that,in most cases,the GQBA can provide a better search capability than other algorithms.Furthermore,GQBA is applied to an optimization problem formulti-pass turning,which is designed tominimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction,which proves the effectiveness of the GQBA.

Status of research on non-conventional technology assisted single-point diamond turning

With the increasing use of difficult-to-machine materials in aerospace applications,machining requirements are becoming ever more rigorous.However,traditional single-point diamond turning(SPDT)can cause surface damage and tool wear.Thus,it is difficult for SPDT to meet the processing requirements,and it has significant limitations.Research indicates that supplementing SPDT with unconventional techniques can,importantly,solve problems due to the high cutting forces and poor surface quality for difficult-to-machine materials.This paper first introduces SPDT and reviews research into unconventional techniques for use with SPDT.The machining mechanism is discussed,and the main advantages and disadvantages of various methods are investigated.Second,hybrid SPDT is briefly described,which encompasses ultrasonic-vibration magnetic-field SPDT,ultrasonic-vibration laser SPDT,and ultrasonic-vibration cold-plasma SPDT.Compared with the traditional SPDT method,hybrid SPDT produces a better optical surface quality.The current status of research into unconventional techniques to supplement SPDT is then summarized.Finally,future development trends and the application prospects of unconventional assisted SPDT are discussed.

Development and Evaluation of Intersection-Based Turning Movement Counts Framework Using Two Channel LiDAR Sensors

This paper presents vehicle localization and tracking methodology to utilize two-channel LiDAR data for turning movement counts. The proposed methodology uniquely integrates a K-means clustering technique, an inverse sensor model, and a Kalman filter to obtain the final trajectories of an individual vehicle. The objective of applying K-means clustering is to robustly differentiate LiDAR data generated by pedestrians and multiple vehicles to identify their presence in the LiDAR’s field of view (FOV). To localize the detected vehicle, an inverse sensor model was used to calculate the accurate location of the vehicles in the LiDAR’s FOV with a known LiDAR position. A constant velocity model based Kalman filter is defined to utilize the localized vehicle information to construct its trajectory by combining LiDAR data from the consecutive scanning cycles. To test the accuracy of the proposed methodology, the turning movement data was collected from busy intersections located in Newark, NJ. The results show that the proposed method can effectively develop the trajectories of the turning vehicles at the intersections and has an average accuracy of 83.8%. Obtained R-squared value for localizing the vehicles ranges from 0.87 to 0.89. To measure the accuracy of the proposed method, it is compared with previously developed methods that focused on the application of multiple-channel LiDARs. The comparison shows that the proposed methodology utilizes two-channel LiDAR data effectively which has a low resolution of data cluster and can achieve acceptable accuracy compared to multiple-channel LiDARs and therefore can be used as a cost-effective measure for large-scale data collection of smart cities.

Design of a coated thinly clad chalcogenide long-period fiber grating refractive index sensor based on dual-peak resonance near the phase matching turning point

A novel method for designing chalcogenide long-period fiber grating(LPFG) sensors based on the dual-peak resonance effect of the LPFG near the phase matching turning point(PMTP) is presented. Refractive index sensing in a high-refractive-index chalcogenide fiber is achieved with a coated thinly clad film. The dual-peak resonant characteristics near the PMTP and the refractive index sensing properties of the LPFG are analyzed first by the phase-matching condition of the LPFG. The effects of film parameters and cladding radius on the sensitivity of refractive index sensing are further discussed. The sensor is optimized by selecting the appropriate film parameters and cladding radius. Simulation results show that the ambient refractive index sensitivity of a dual-peak coated thinly clad chalcogenide LPFG at the PMTP can be 2400 nm/RIU, which is significantly higher than that of non-optimized gratings. It has great application potential in the field of chemical sensing and biosensors.

DE-DOLLARIZATION IS TRENDING AND THERE'S NO TURNING BACK

The U.S.dollar has long stood tall as the pre-eminent global currency for international cross-border trade settlements and foreign exchange reserves ever since the launch of the Bretton Woods institutions-International Monetary Fund(IMF)and World Bank(WB).

Analysis and Prediction of Effect of Turning Marks Diffraction on Image Quality of Optical System

Single-point diamond turning (SPDT) is widely used in the machining of infrared materials and metal-based mirrors. Diamond tips can scratch material, replicate the shape of the tip, and create annular turning marks on optical surfaces, which can have unpredictable adverse effects on imaging. In order to predict the effect of turning marks diffraction on the degradation of imaging quality, a model of the influence of SPDT processing parameters on the reduction of system imaging MTF under the influence of ideal grating turning marks diffraction was established. The results show that the depth of the turning mark will lead to the decline of MTF, especially the low frequency information. Finally, a method is proposed to reduce the effect of turning marks diffraction through changing the processing parameters. .

小转弯曲线隧道TBM选型与掘进姿态调控方法

小转弯隧道施工时全断面隧道掘进机(full-section tunnel boring machine,TBM)的选型设计和掘进姿态控制具有特殊性,目前还缺乏通用的理论依据和指导方法。针对此问题,首先,对传统类型TBM小转弯选型进行研究,结合已有项目数据和几何模拟,确定传统类型TBM能适应的最小转弯半径。然后,对双盾敞开式TBM的推进系统和导向系统进行针对性设计,通过分析双盾敞开式TBM推进系统结构和实际施工,提出转弯时双盾敞开式TBM推进油缸内外侧行程差值的理论计算方法和施工过程中的姿态调控方法。最后,得出如下结论:1)当隧道转弯半径小于200 m时,敞开式TBM适应难度较大,需要采用双盾敞开式TBM;2)结合抚宁抽水蓄能电站项目实际施工情况,提出的双盾敞开式TBM的理论计算方法和姿态调控方法确保了转弯段隧道的轴线偏差在要求范围内。

改进A^(*)算法的安全高效室内全局路径规划

针对A^(*)算法生成路径存在斜穿障碍物、转折点多及不平滑的情况,本文提出一种改进的A^(*)算法。首先通过排除所有强迫邻居节点来优化搜索邻域,避免生成斜穿障碍物的路径,提升路径的安全性和可靠性;其次设置安全距离,提取优化邻域后生成路径的必经转折点,减少路径冗余,简化路径结构;最后使用贝塞尔曲线对必经转折点进行插值,根据相邻两必经转折点的位置和连线斜率确定每段贝塞尔曲线控制点的数量和位置,进行分段平滑。仿真实验表明,改进的A^(*)算法较原A^(*)算法生成的路径安全性平均提升了33.68%,转折点个数平均减少了37.00%,同时机器人转向角和路径曲率连续,保证了路径的平滑。改进的A^(*)算法最终生成的路径与障碍物均保持在安全距离内,转折点少且平滑,可应用到移动机器人室内路径规划中。

Analyzing the Effects of Tool Holder Stiffness on Chatter Vibration Reduction in Turning

This paper investigates the effects of tool holder materials on chatter vibration in turning operations.The study uses a complex dynamic turning model with two degrees of freedom for the orthogonal cutting system.Tool holders made from different materials,including Al 5083,Al 6082,Al 7012,and a standard 4140 material,were subjected to chatter vibration to investigate their process damping capabilities.The study found that the standard tool holder 4140 allows for higher stable depths of cut and produces similar process damping values compared to the other tool holders.Finite element analyses(FEA)were performed to verify the experimental results,and the modal and FEA analyses produced very similar results.The study concludes that future research should investigate the effects of tool holders made from high alloy steel alloys on process damping.Overall,this paper provides important insights into the effects of tool hold-er materials on chatter vibration and process damping in turning operations,which can help in the design of more effi-cient and effective cutting systems.

强涌潮水流条件下直立圆柱体的涌潮作用力试验研究

为厘清强涌潮河口直立式圆柱体受到的涌潮作用力与涌潮水流条件、圆柱直径的关系,通过涌潮水槽模型试验研究了三种直径直立圆柱在不同潮前水深、涌潮高度条件下的涌潮作用力。结果表明,强涌潮的涌潮作用力为直立圆柱受到冲击力叠加该时刻的水流绕流阻力、圆柱前后水位压差力。通过数据回归分析,得到了一个无量纲涌潮作用力计算公式。该公式表明,涌潮作用力分析计算除了要考虑涌潮高度、圆柱直径外,潮前水深也是关键因素,且涌潮作用力与圆柱直径之间存在显著的非线性关系。

现代逻辑思维对诗性的背离与回归——从罗素、维特根斯坦到伽达默尔

亚里士多德创立的形式逻辑在原始思维中有其“互渗律”的起源,因而是基于语言本身的隐喻本性的,但直到康德,以往的隐喻都被理解为“使陌生变得熟悉”,成为形式逻辑的工具论,隐喻的“使熟悉变成陌生”的创新性一面却被忽视了。弗雷格和罗素等从逻辑中清除了陌生的东西,使之变得数理化、僵化而无创造性,必然遇到“罗素悖论”而无法自拔;维特根斯坦是一个转折,从早期的分析哲学的逻辑观到晚期摆脱工具主义而转向语言游戏,有了西方逻辑思维从背离诗性到回归诗性的苗头;最后,经过伽达默尔对这一苗头的修正和发挥,将其扩展为诠释学的“语言学转向”,为隐喻和诗性打开了一片广阔的天地。可惜,他受到海德格尔和基督教传统“圣言”的局限,最终未能就此将其创造性的对话学说提升到“语言学之后”的形而上学,而沉陷于传统存在论(本体论)的“物理学之后”中。

盾构法联络通道T接头局部冻结加固效果及影响因素研究

为研究盾构法联络通道T接头局部冻结区域的薄弱位置,探究主隧道管片材料、洞门间隙注浆体热导率、是否注浆改良土体和冻结管环数等因素对局部冻结的影响,依托天津地铁10号线某联络通道T接头局部冻结加固工程,采用数值模拟对局部冻结温度场的分布规律进行分析。研究结果表明:1)对于沿洞门处的土体,联络通道顶底部冻结效果差于联络通道侧面;主隧道钢管片向主隧道底部比向顶部延伸的更长,因而联络通道底半环冻结效果略差于顶半环。2)影响局部冻结时间的主要因素为主隧道管片材料和洞门间隙注浆体的热导率。3)主隧道部分采用钢管片工况,钢管片起到通道壁后远近土体导热“桥梁”的作用;当洞门间隙注浆体的热导率较低(如0.93 W/(m·K))时,注浆体不能及时地向主隧道钢管片输送冷量,联络通道壁后近处土体中大量冻结冷量沿钢管片传递至远处土体,使通道壁后较近处土体温度较高,因而比主隧道全部采用钢筋混凝土管片工况冻结时间长。4)随着洞门间隙注浆体热导率的增大,其向主隧道管片输送冷量的效率得到提升,主隧道附近土体的冻结效果得到改善,冻结时长有所缩短;间隙注浆体热导率由0.93 W/(m·K)提升至1.8 W/(m·K)时,可以将主隧道部分采用钢管片工况冻结时间由17 d缩短至12 d,所需冻结时长相比主隧道全部采用钢筋混凝土管片工况反而缩短1 d,即主隧道部分采用钢管片工况的冻结时间对洞门间隙注浆体热导率的改变更为敏感。