Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

Discussion on Reverse Design of Components with Complex Curved Surface and Computer Numerical Control Machining

With the continuous development and advancement of science and technology,the work of tool path planning has received extensive attention.Among them,curved surface generation and data processing are the focus of management and design,which necessitate the full application of reverse design of complex curved surface components to complete numerical control processing,effective optimization and upgrading,integration the tasks of point cloud data collection,and point cloud data processing to ensure that the corresponding computer numerical control machining model can exert its actual value.This paper briefly analyzes the basic principles of curved surface reconstruction as well as discusses the reverse design of complex curved components and the experimental processes and results that involved computer numerical control machining,which serves the purpose as reference only.

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors,which has motivated the researchers to develop effective automated seizure detection methods.This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases.The proposed method consists of three steps:(i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis(MSPCA),(ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD),discrete wavelet transform(DWT),and dual-tree complex wavelet transform(DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals,and(iii) allocate the feature vector to the relevant class(i.e.,seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine(SVM),k-nearest neighbor(k-NN),and linear discriminant analysis(LDA).The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process.The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

Correction of CMPAS Precipitation Products over Complex Terrain Areas with Machine Learning Models

Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.

Improve the Performance of a Complex FMS with a Hybrid Machine Learning Algorithm

Modern manufacturing systems are expected to undertake multiple tasks, flexible for extensive customization, and that trends make production systems become more and more complicated. The advantage of a complex production system is a capability to fulfill more intensive goods production and to adapt to various parameters in different conditions. The disadvantage of a complex system, on the other hand, with the pace of the increase of complexity, lies in the control difficulties rising dramatically. Moreover, classical methods are reluctant to control a complex system, and searching for the appropriate control policy tends to become more complicated. Thanks to the development of machine learning technology, this problem is provided with more possibilities for the solutions. In this paper, a hybrid machine learning algorithm, integrating genetic algorithm and reinforcement learning algorithm, is proposed to cope with the accuracy of a control policy and system optimization issue in the simulation of a complex manufacturing system. The objective of this paper is to cut down the makespan and the due date in the manufacturing system. Three use cases, based on the different recipe of the product, are employed to validate the algorithm, and the results prove the applicability of the hybrid algorithm. Besides that, some additionally obtained results are beneficial to find out a solution for the complex system optimization and manufacturing system structure transformation.

Data complexity-based batch sanitization method against poison in distributed learning

The security of Federated Learning(FL)/Distributed Machine Learning(DML)is gravely threatened by data poisoning attacks,which destroy the usability of the model by contaminating training samples,so such attacks are called causative availability indiscriminate attacks.Facing the problem that existing data sanitization methods are hard to apply to real-time applications due to their tedious process and heavy computations,we propose a new supervised batch detection method for poison,which can fleetly sanitize the training dataset before the local model training.We design a training dataset generation method that helps to enhance accuracy and uses data complexity features to train a detection model,which will be used in an efficient batch hierarchical detection process.Our model stockpiles knowledge about poison,which can be expanded by retraining to adapt to new attacks.Being neither attack-specific nor scenario-specific,our method is applicable to FL/DML or other online or offline scenarios.

A Systematic Review of Automated Classification for Simple and Complex Query SQL on NoSQL Database

A data lake(DL),abbreviated as DL,denotes a vast reservoir or repository of data.It accumulates substantial volumes of data and employs advanced analytics to correlate data from diverse origins containing various forms of semi-structured,structured,and unstructured information.These systems use a flat architecture and run different types of data analytics.NoSQL databases are nontabular and store data in a different manner than the relational table.NoSQL databases come in various forms,including key-value pairs,documents,wide columns,and graphs,each based on its data model.They offer simpler scalability and generally outperform traditional relational databases.While NoSQL databases can store diverse data types,they lack full support for atomicity,consistency,isolation,and durability features found in relational databases.Consequently,employing machine learning approaches becomes necessary to categorize complex structured query language(SQL)queries.Results indicate that the most frequently used automatic classification technique in processing SQL queries on NoSQL databases is machine learning-based classification.Overall,this study provides an overview of the automatic classification techniques used in processing SQL queries on NoSQL databases.Understanding these techniques can aid in the development of effective and efficient NoSQL database applications.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

Precision Design for Machine Tool Based on Error Prediction

Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.

A hybrid two-stage fexible flowshop scheduling problem with m identical parallel machines and a burn-in processor separately

A hybrid two-stage flowshop scheduling problem was considered which involves m identical parallel machines at Stage 1 and a burn-in processor M at Stage 2, and the makespan was taken as the minimization objective. This scheduling problem is NP-hard in general. We divide it into eight subcases. Except for the following two subcases： （1） b≥ an, max{m, B} 〈 n; （2） a1 ≤ b ≤ an, m ≤ B 〈 n, for all other subcases, their NP-hardness was proved or pointed out, corresponding approximation algorithms were conducted and their worst-case performances were estimated. In all these approximation algorithms, the Multifit and PTAS algorithms were respectively used, as the jobs were scheduled in m identical parallel machines.

Prediction of Intrinsically Disordered Proteins with a Low Computational Complexity Method

The prediction of intrinsically disordered proteins is a hot research area in bio-information.Due to the high cost of experimental methods to evaluate disordered regions of protein sequences,it is becoming increasingly important to predict those regions through computational methods.In this paper,we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence,which includes the Shannon entropy,the topological entropy,the sample entropy and three amino acid preferences including Remark 465,Deleage/Roux,and Bfactor(2STD).Particularly,we introduced the sample entropy for calculating time series complexity by mapping the amino acid sequence to a time series of 0-9.To our knowledge,the sample entropy has not been previously used for predicting IDPs and hence is being used for the first time in our study.In addition,the scheme used a properly sized sliding window in every protein sequence which greatly improved the prediction performance.Finally,we used seven machine learning algorithms and tested with 10-fold cross-validation to get the results on the dataset R80 collected by Yang et al.and of the dataset DIS1556 from the Database of Protein Disorder(DisProt)(https://www.disprot.org)containing experimentally determined intrinsically disordered proteins(IDPs).The results showed that k-Nearest Neighbor was more appropriate and an overall prediction accuracy of 92%.Furthermore,our method just used six features and hence required lower computational complexity.

A Class of Single Machine Scheduling Problems with Variable Processing Time

In this paper, single machine scheduling problems with variable processing time are raised. The criterions of the problem considered are minimizing scheduling length of all jobs, flow time and number of tardy jobs and so on. The complexity of the problem is determined. [WT5HZ]

Parallel Machine Problems with a Single Server and Release Times

Parallel machine problems with a single server and release times are generalizations of classical parallel machine problems. Before processing, each job must be loaded on a machine, which takes a certain release times and a certain setup times. All these setups have to be done by a single server, which can handle at most one job at a time. In this paper, we continue studying the complexity result for parallel machine problem with a single and release times. New complexity results are derived for special cases.

Machine learning of synaptic structure with neurons to promote tumor growth

In this paper,we use machine learning techniques to form a cancer cell model that displays the growth and promotion of synaptic and electrical signals.Here,such a technique can be applied directly to the spiking neural network of cancer cell synapses.The results show that machine learning techniques for the spiked network of cancer cell synapses have the powerful function of neuron models and potential supervisors for different implementations.The changes in the neural activity of tumor microenvironment caused by synaptic and electrical signals are described.It can be used to cancer cells and tumor training processes of neural networks to reproduce complex spatiotemporal dynamics and to mechanize the association of excitatory synaptic structures which are between tumors and neurons in the brain with complex human health behaviors.

Dimensional Complexity and Algorithmic Efficiency

This paper uses the concept of algorithmic efficiency to present a unified theory of intelligence. Intelligence is defined informally, formally, and computationally. We introduce the concept of dimensional complexity in algorithmic efficiency and deduce that an optimally efficient algorithm has zero time complexity, zero space complexity, and an infinite dimensional complexity. This algorithm is used to generate the number line.

A Hierarchical Modeling and Fault Diagnosis Method for Complex Electronic Devices

Due to the shortcomings of the diagnosis systems for complex electronic devices such as failure models hard to build and low fault isolation resolution, a new hierarchical modeling and diagnosis method is proposed based on multisignal model and support vector machine （SVM）. Multisignal model is used to describe the failure propagation relationship in electronic device system, and the most probable failure printed circuit boards （PCBs） can be found by Bayes inference. The exact failure modes in the PCBs can be identified by SVM. The results show the proposed modeling and diagnosis method is effective and suitable for diagnosis for complex electronic devices.

面向任务的载人深潜人机交互信息重要度评估

为更好地识别复杂任务环境下深海载人潜水器驾驶舱人机交互信息重要度差异,提出一种基于复杂网络的人机交互信息重要度评估方法。以悬停采样任务为研究对象,运用决策阶梯模型及任务-网络建模技术识别复杂任务相关人机交互信息元及逻辑关联,构建交互信息复杂网络模型。基于网络拓扑特征参数,从节点间逻辑影响关系的角度提出节点全局及局部影响效应评估指标,结合解释结构模型层级权重指标对交互信息节点重要度综合值进行计算,得出悬停采样任务人机交互信息重要度排序。将排序结果与其他4种算法进行对比,在证明计算有效的基础上,邀请潜航员结合实际任务流程对交互信息进行主观重要度评估。结果显示,研究所得重要度排序结果与潜航员主观排序结果呈强相关,且对重要交互信息元的识别结果与潜航员主观感知相符合,验证了所提方法识别复杂任务人机交互信息重要度差异的有效性。

机器人磨抛复杂曲面加工轨迹对表面质量的影响研究

为了探究不同加工轨迹及其排布对工件磨抛加工表面质量的影响,本文进行了机器人磨抛轨迹对工件表面质量影响规律的研究。基于Preston去除方程和Hertz接触理论建立了砂带磨抛加工材料去除深度模型,分析了表面残留纹理的生成机理。以曲面航空发动机叶片为试验样件,利用自行搭建的机器人磨抛系统,分别使用等距轨迹、摆线轨迹进行加工试验,分析材料去除效果及表面纹理情况。试验结果表明,采用传统直线加工的等距轨迹于搭接处产生条带状纹理;摆线因其多方向性的加工动作,均化了表面纹理,提高了加工表面一致性。

基于支持向量机和证据理论的复杂系统可靠性分析方法

针对复杂系统中存在极限状态方程为隐式情况及参数为认知不确定性的问题,文中提出了一种基于支持向量机和证据理论的高效可靠性分析方法。首先,基于贝叶斯方法和最大熵原理将焦元上的基本概率分配平均分配到焦元中每一个元素以实现证据体精确化;其次,面对多学科系统中极限状态方程为隐式情况,采用支持向量机(SVM)进行显式化处理。在该方法中提出了SVM训练样本抽取策略,并对SVM通过引入马尔可夫蒙特卡洛模拟法(MCMC)进行改进,使其能适用于多学科系统的隐式极限状态方程小失效概率的求解;最后,通过算例分析,表明该方法的精度和计算效率具有较大优势,相比于MCS,该方法抽样2000个样本点精度相对误差仅为3.05%,为复杂系统的可靠性分析提供了一定的参考价值。