A Machine Learning-Based Web Application for Heart Disease Prediction

This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.

Hybrid Multi-Object Optimization Method for Tapping Center Machines

This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control(CNC)machines.First,rigid tapping parameters and uniform(including 41-level and 19-level)layouts were adopted to collect representative data for modeling.Next,ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data.In tapping center machines,the synchronization errors and cycle times are important consid-erations,so these two objects were used to build the ANFIS models.Then,a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously.The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the nonsystematic method.Additionally,the optimal parameter combination was used to optimize existing CNC tools during the commissioning process.Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping.The posi-tion gain and prefeedback coefficient can reduce the synchronization errors significantly,and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors.The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms;it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield,there-by helping factories achieve carbon reduction.

ACCURATE MEASUREMENT OF ROTARY MACHINE AXIS CENTER TRACE

Four methods aiming at measuring rotary machine axis center trace arediscussed in detail. The comparative analysis is made on some aspects such as measurement accuracy,on-machine characteristics, feasibility, practical operation convenience and the integrity ofmeasurement information. In order to simplify measurement, the axis profile error is ignored intraditional condition, while the measurement accuracy will be reduced. The 3-point method that theaxis profile error is firstly separated has better real time character, at the same time, not onlythe axis motion error but also the axis profile error can be measured. All of those information canbe used to diagnose the fault origin. The analysis result is proved to be correct by the experiment.

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.

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.

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.

A hardware-based algorithm for virtual machine provisioning in a private cloud

Cloud computing is becoming a key factor in the market day by day. Therefore, many companies are investing or going to invest in this sector for development of large data centers. These data centers not only consume more energy but also produce greenhouse gases. Because of large amount of power consumption, data center providers go for different types of power generator to increase the profit margin which indirectly affects the environment. Several studies are carried out to reduce the power consumption of a data center. One of the techniques to reduce power consumption is virtualization. After several studies, it is stated that hardware plays a very important role. As the load increases, the power consumption of the CPU is also increased. Therefore, by extending the study of virtualization to reduce the power consumption, a hardware-based algorithm for virtual machine provisioning in a private cloud can significantly improve the performance by considering hardware as one of the important factors.

Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

To improve the training speed of support vector machine （SVM）, a method called improved center distance ratio method （ICDRM） with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental resuhs show that the improved SVM method-ICDRM does well at identification rate and training speed.

Optimised Migrate Virtual Machine Rejuvenation

Server virtualization is an essential component in virtualized software infrastructure such as cloud computing. Virtual machines are generated through a software called virtual machine monitor (VMM) running on physical servers. The risks of software aging caused by aging-related bugs affect both VM and VMM. As a result, service reliability degrades may generate huge financial losses to companies. This paper presents an analytic model using stochastic reward nets for time-based rejuvenation techniques of VMM and VM. We propose to manipulate the VM behavior while the VMM rejuvenation is according to the load on the system. Using a previous Petri net model of virtualized server, we performed an algorithm in order to optimize rejuvenation technique and achieve high availability. So we perform Migrate-VM rejuvenation or Warm-VM rejuvenation while there are current jobs in the system. Although Migrate-VM rejuvenation is better than Warm-VM rejuvenation in steady state availability, it can’t be always performed as it depends on the capacity of the other host. When the queue is empty and the virtual machine has no current jobs to serve, we propose to combine both VMM rejuvenation and VM rejuvenation. We show that the proposed technique can enhance the availability of VMs.

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.

Early Nutrient Diagnosis of Kentucky Bluegrass Combining Machine Learning and Compositional Methods

Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.

A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.

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.

Construction and application of pre-classified smooth semi-supervised twin support vector machine

In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S2TSVM) is proposed by adding the original unlabeled samples. In S2TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S2TSVM (PS2TSVM) is proposed. Compared with S2TSVM, PS2TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS2TSVM is smoothed. After smoothing the model, the pre-classified smooth S2TSVM (PS3TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS3TSVM model has better classification results.

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.

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。

马扎克五轴加工中心后处理研究及验证

在制造业飞速发展的今天,产品对零件的结构及精度要求越来越高。传统三轴加工中心无法满足零件加工要求,而五轴联动机床能很好适应复杂零件的加工工艺。然而,机床厂家并不会免费提供该设备的专用后处理,这给机床使用人员带来了极大困难,同时也浪费了人力成本和时间成本。这里针对马扎克五轴加工中心,运用Tcl脚本语言在UG/Post环境中编制适合该机床的后处理系统。该系统能有效读取刀位文件,并且按照机床要求生成相应加工程序,该程序能安全有效地运行于马扎克VARIAXIS j-500/5X机床。最后通过加工检测五轴联动机床的S试件证明了该后处理系统的有效性和安全性。