Quantum information processing with nitrogen–vacancy centers in diamond

Nitrogen-vacancy （NV） center in diamond is one of the most promising candidates to implement room temperature quantum computing. In this review, we briefly discuss the working principles and recent experimental progresses of this spin qubit. These results focus on understanding and prolonging center spin coherence, steering and probing spin states with dedicated quantum control techniques, and exploiting the quantum nature of these multi-spin systems, such as superposition and entanglement, to demonstrate the superiority of quantum information processing. Those techniques also stimulate the fast development of NV-based quantum sensing, which is an interdisciplinary field with great potential applications.

Machining characteristics of fine grained AZ91 Mg alloy processed by friction stir processing

AZ91Mg alloy was considered and friction stir processing(FSP)was adopted to achieve grain refinement to investigatethe effect of grain size and secondary phase on machining characteristics during drilling at various speeds and feeds.Super saturatedAZ91Mg alloy was obtained after FSP and the grain refinement was achieved from(166.5±8.7)μm to(21.7±13.5)μm.Surprisingly,hardness reduced for FSP AZ91Mg alloy(88.95±6.1)compared with AZ91alloy(108.2±15.6),which was attributed to the reducedsecondary phase.However,the mean cutting force for FSP-treated(FSPed)AZ91Mg alloy was marginally increased.The edgedamage of the drilled holes was lower for FSPed AZ91Mg alloy compared with unprocessed AZ91Mg alloy.Hence,it can beunderstood that the grain refinement may slightly increase the cutting forces during drilling but better edge finishing can be achievedin machining of AZ91Mg alloy.

Optimization Design for Fixed Table of Gantry Machining Center Based on Sensitivity and Topology Analyses

In order to decrease the deformation and stress and increase the natural frequency of the fixed table,a method of optimization driven by the sensitivity and topology analyses is proposed.The finite element model of the fixed table is constructed and analyzed by using ANSYS software.Based on the results of static analysis and modal analysis,the maximum deformation,the maximum stress,and natural frequencies are obtained.Then,the sensitivity analysis and topology optimization are carried out to find out the parameters to be optimized.The fixed table is reconstructed according to optimal design scheme.In the comparison of the results between original model and the optimized one,the maximum deformation and stress are decreased by 71.73%and 60.27%respectively.At the same time,the natural frequencies from the first mode to the sixth mode are increased by 30.28%,29.57%,29.51%,31.52%,22.19%,and 21.80%,respectively.The method can provide technology guide for the design and optimization of machining structure.

Finite Element Analysis and Optimal Design Based on ANSYS in a XH2408 Gantry Style NC Machining Center

ANSYS, the software of construction analysis, is used to analyze static and dynamic performances of a XH2408 gantry style numerical control （NC） milling machining center and optimize its construction using the finite element method. First, a finite element model is established and the static and dynamic analysis are completed as constraints and loads applied on the finite element model. It is found that both spindle box and gantry are the worst components of assembly in performance. Secondly, the spindle box and gantry are chosen as objects of optimal design separately, aiming to improve their performance. The optimal plans are accomplished on the basis of the minimum volume for the spindle box and the maximum inherent frequency for the gantry subject to the constrains. Finally, the machine tool improved is analyzed statically and dynamically based on the optimal results of the spindle box and gantry. The results show that optimal design with the finite element method increases static and dynamical performances of the XH2408 gantry style numerical control milling machining center and the technique is effective and practical in engineering applications.

Analysis on Failure Mode Severity of Machining Center Spindle System

According to the subjectivity and fuzziness of analysis on failure mode severity about spindle system of machining center,an analysis model of the failure mode severity of such a system is proposed based on the new fault severity index system, improved analytic hierarchy process( IAHP) and entropy-based fuzzy comprehensive evaluation. IAHP and entropy methods are adopted to determine the comprehensive failure severity index weight. The evaluation result is obtained after the factor set,comment set,weight set,and other parameters are determined,and then the level of risk degree and numerical value order of every spindle system failure mode is given. By taking an example,we verify that the proposed method can quantify the qualitative problem comprehensively,obtain more accurate analysis results,and provide the theoretical reference for mechanization and sequencing of failure mode effect analysis in reliability analysis. The calculation results can also serve as the basis of failure mode,effects,and criticality analysis in the subsequent step.

South China University of fechnology Advanced Metallic Materials Research and Processing Technology Center

A dvanced Metallic Materials Research and Processing Technology Center was found in December 1998. As a unit under The College of Mechanical Engineering, the Center is an expansion of the former Cast and Composite Materials Research Group, which was found in the early eighties of last century. The Center is focusing in the basic and applied research, and development of advanced metallic materials and their processing technology. It also functions as an organization

Tsinghua University Tsinghua-TOYO Research and Development Center of Magnesium and Aluminum Alloys Processing Technology

Tsinghua-TOYO Research and Development Center of Magnesium and Aluminum Alloys Processing Technology was officially established between Tsinghua University and TOYO Machinery & Metal Co. on Feb. 26, 2002, which is a non-independent legal research organization located in Department of Mechanical Engineering of Tsinghua University. The center was equipped with one set of 650t automatic magnesium and aluminum alloys diecasting machine and necessary accessories including the melting furnaces for magnesium alloys and aluminum alloys, an accurate magnesium pump to transfer the magnesium alloys to the shot sleeve of the die casting machine, a die temperature controller, and data logging systems for cavity pressure and die temperature distributions, etc. The center is aimed to the research and development of magnesium and aluminum die casting process and their related technologies, and the main research contents include:

Analysis of the Reliability Model of the Tool Magazine Subsystem in the Processing Center

Tool magazine is an important part of a processing center.Tool magazines have the advantage of reducing work parts turnover,handling,and storage time.In addition,some research shows that the use of tool magazine in machine tools in the machining center can make the cutting time utilization rate of machine tools 3-4 times higher than that of ordinary machine tools,and has better machining consistency,thus improving processing accuracy and processing efficiency,shortening the production cycle.Therefore,the reliability,efficiency and accuracy of the tool magazine have a crucial impact on the stability of the machine tool.Therefore,the reliability analysis of CNC machine tool magazine is considerably important.In view of this problem,this paper puts forward that the reliability model of the tool magazine is established after the parameter estimation and hypothesis test,and then the maintainability model of the tool magazine is built,it ends with the fault analysis of the tool magazine.

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency,so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status.Firstly,the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology,and the features related to the tool wear status are extracted in the time domain,frequency domain and time-frequency domain to form a feature sample matrix;secondly,the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types:slight wear,normal wear and sharp wear to construct a target Finally,the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status.The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%;and compared with BP model,CNN model and SVM model,the accuracy indexes are improved by 9.48%,3.44%and 1.72%respectively,which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

A Review of Energy Efficiency in Data Processing Centers

This article explores, through a case study, measures of energy efficiency in data processing centers. An analysis of this case demonstrates how the design criteria could improve the rate of consumption in IT centers, which is currently the second most contaminating industry on the planet, and is the responsible for 2% of CO2 emissions, surpassed only by the aeronautical industry. The present and future situation of IT center energy consumption and associated environmental effects is analyzed, and also looks at how state-of-the-art technology, correctly implemented, could ensure significant rationalization of data processing center energy consumption. The article will examine optimization techniques, specific problems and case studies.

A Study on Comprehensive Modeling for Errors in on-line Measuring System of Machining Center

Based on the theory of multi-body system (MBS), bine’s and huston’s methods are applied to an on-line measuring system of machining center in this paper. Through the study on modeling technique, the comprehensive model for errors calculation in an on-line measuring System of machining center have been built for the first time. Using this model, the errors can be compensated by soft.ware and the measuring accuracy can be enhanced without any more inveSt. This model can be used in all kinds of machining center.

Study of Remote Monitoring and Maintenance Guiding Technique Based on LabVIEW for Machining Centers

The virtual instruments （VIs）, as a new type of instrument based on computer, has many advanced attractive characteristics. This research is based on Vls, and brings condition monitoring and knowledge-based maintenance support together through an integrated （including hate.met, ASP. NET, XML tochnique, Vls） network environme~. Within the enviromnent, machining centers operators, engineers or managers can share real-time data through the browser-based interface and minimize machining centers downtime by providing status monitoring and remote maintenance guiding from service centers.

Failure Modes of Machining Center in Test Run

To analysis the early failures of machining centers,the failure mode effect and criticality analysis( FMECA) method was used. Based on the failure data collected from production lines in test run,all the failure modes of machining centers were summarized and criticality of all subsystems is figured out. And the process of FMECA was improved. The most critical subsystem was manipulator subsystem. The most critical failure mode was impacted manipulator. Reasons and effect of some important failure modes were analyzed. And some suggestions to solve failures were given.

Dynamic Importance Analysis of Machining Center

As an important part of CNC machine tools,machining center’s reliability,efficiency and accuracy measure the machining level of a CNC machine tool.Therefore,the research on the importance of CNC machine tools is particularly important.However,as a complex mechanical and electrical equipment,the traditional reliability importance analysis method is too simple.In order to solve this problem,this passage proposes to establish the reliability model of each part of the machining center,and then analyze its dynamic importance,which improves the limitation of only reliability importance analysis.Through the analysis the reliability importance and criticality importance,and then rank the result of importance analysis,finally it can get that the ranking results of the key components accord with the fact,so the results can provide support for the importance research of machining center.

技术为先 实力为本——访Fadal Machining Centers市场和销售部副总监Donald J.Haselton先生

Fadal是全球最大的立式加工中心(VMC)生产商之一,其产品在世界范围内销售。目前,已有超过35000台机器交到了客户的手中。这些设备得到了广泛的应用,尤其是用作模具生产加工的基础设备,更是得到了该领域客户的一致肯定。

Structure Improvement and Optimization of Gantry Milling System for Complex Boring and Milling Machining Center

To enhance the efficiency and machining precision of the TX1600G complex boring and milling machining center,a study was conducted on the structure of its gantry milling system.This study aimed to mitigate the influence of factors such as structural quality,natural frequency,and stiffness.The approach employed for this investigation involved mechanism topology optimization.To initiate this process,a finite element model of the gantry milling system structure was established.Subsequently,an objective function,comprising strain energy and modal eigenvalues,was synthesized.This objective function was optimized through multi-objective topology optimization,taking into account certain mass fraction constraints and considering various factors,including processing technology.The ultimate goal of this optimization was to create a gantry milling structure that exhibited high levels of dynamic and static stiffness,a superior natural frequency,and reduced mass.To validate the effectiveness of these topology optimization results,a comparison was made between the new and previous structures.The findings of this study serve as a valuable reference for optimizing the structure of other components within the machining center.

WORKPIECE LOCATING AND POST PROCESSING SYSTEMS ON 6-DOF CNC MILLING MACHINE

A conventional non-computerized numerical control （CNC） machine is updated by mounting a six degree-of-free （DOF） parallel mechanism on it, thus obtaining a new CNC one. The structure of this CNC milling machine is introduced, and the workpiece locating system and the post processing system of the cutter location （CL） data file are analyzed. The new machine has advantages of low costs, simple structure, good rigidity, and high precision. It is easy to be transformed and used to process the workpiece with a complex surface.

A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

Phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers may direct the users to a fake website that could seem legitimate, and then gather useful and confidential information using that site. In order to protect users from Social Engineering techniques such as phishing, various measures have been developed, including improvement of Technical Security. In this paper, we propose a new technique, namely, “A Prediction Model for the Detection of Phishing e-mails using Topic Modelling, Named Entity Recognition and Image Processing”. The features extracted are Topic Modelling features, Named Entity features and Structural features. A multi-classifier prediction model is used to detect the phishing mails. Experimental results show that the multi-classification technique outperforms the single-classifier-based prediction techniques. The resultant accuracy of the detection of phishing e-mail is 99% with the highest False Positive Rate being 2.1%.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalography

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.