scholarly journals Online Prediction of Milling Inner Hole Roundness Error Based on Accurate SSEM Value Extraction

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Zhenbang Hu ◽  
Gedong Jiang ◽  
Xuesong Mei ◽  
Xialun Yun ◽  
Yun Zhang
Keyword(s):  
Prediction Accuracy ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Probe Method ◽  
Machining Process ◽  
Machining Accuracy ◽  
Wavelet Method ◽  
Roundness Error ◽  
Harmonic Wavelet ◽  
Online Prediction

To improve the machining accuracy and production efficiency of precision components with deep hole structures, an online prediction method of the inner hole roundness error, which cannot be directly measured in real time during the machining process, is proposed in this paper. For online prediction of the workpiece roundness error (WRE) during machining, a predictive model based on correlation analysis and a proportional method is proposed according to the spindle synchronous error motion (SSEM) by three-probe method testing. To improve the prediction accuracy of the WRE, a particle swarm optimization (PSO) algorithm is introduced for optimizing a probe mounting angle of a three-probe method, and a harmonic wavelet method for SSEM feature extraction is proposed. Using the PSO algorithm, the optimal probe mounting angle of the three-probe method is obtained, the influence of spindle surface roundness on SSEM is eliminated, and the higher-order harmonic suppression of the three-probe method is avoided effectively. By the harmonic wavelet method, the accurate SSEM extraction is enhanced and the WRE prediction accuracy is promoted. The experiments show that the inner hole roundness error online prediction method proposed in this paper has high prediction accuracy.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

Chatter Prediction Based on NC Physical Simulation in Machining Ti6Al4V Thin-Walled Components

2013 ◽  
Vol 395-396 ◽  
pp. 1008-1014
Author(s):  
Yu Li ◽  
Chao Sun
Keyword(s):  
Tool Path ◽  
Physical Simulation ◽  
Prediction Method ◽  
Cutting Parameters ◽  
Simulation Software ◽  
Cnc Machining ◽  
Machining Process ◽  
Chatter Stability ◽  
Chatter Prediction ◽  
Thin Walled

Chatter has been a problem in CNC machining process especially during machining thin-walled components with low stiffness. For accurately predicting chatter stability in machining Ti6Al4V thin-walled components, this paper establishes a chatter prediction method considering of cutting parameters and tool path. The fast chatter prediction method for thin-walled components is based on physical simulation software. Cutting parameters and tool path is achieved through the chatter stability lobes test and finite element simulation. Machining process is simulated by the physical simulation software using generated NC code. This proposed method transforms the NC physical simulation toward the practical methodology for the stability prediction over the multi-pocket structure milling.

scholarly journals Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 458
Author(s):  
Yanan Zhao ◽  
Zihan Zang ◽  
Weirong Zhang ◽  
Shen Wei ◽  
Yingli Xuan
Keyword(s):  
Temperature Distribution ◽  
Relative Error ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Mobile Sensors ◽  
Average Relative Error ◽  
Contribution Ratio ◽  
Mobile Sensor ◽  
Indoor Climate ◽  
Indoor Temperature

In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

Dynamic optimization method with applications for machine tools based on approximation model

2017 ◽  
Vol 232 (11) ◽  
pp. 2009-2022 ◽  
Author(s):  
TJ Li ◽  
XH Ding ◽  
K Cheng ◽  
T Wu
Keyword(s):  
Machine Tool ◽  
Performance Optimization ◽  
Machine Tools ◽  
Optimization Design ◽  
Natural Frequencies ◽  
Dynamic Performance ◽  
Machining Process ◽  
Machining Accuracy ◽  
Approximation Model ◽  
Dynamic Behaviors

Natural frequencies and modal shapes of machine tools have position-dependent characteristics owing to their dynamic behaviors changing with the positions of moving parts. It is time-consuming and difficult to evaluate the dynamic behaviors of machine tools and their machining accuracy at different positions. In this paper, a Kriging approximation model coupled with finite element method is proposed to substitute the dynamic equations for obtaining the position-dependent natural frequencies of a machine tool, as well as relative positions between the tool and the workpiece during the machining process. Based on the proposed method, dynamic performance optimization design of the machine tool is conducted under the condition of minimum relative positions. Three case studies are illustrated to demonstrate the implementation of the proposed method.

Direct compensation of the spindle rotation error with an active hydrostatic journal bearing system

2021 ◽  
pp. 095440622110263
Author(s):  
Chunwang Xu ◽  
Shujiang Chen ◽  
Changhou Lu ◽  
Kang Wang ◽  
Jiaheng Sun
Keyword(s):  
Journal Bearing ◽  
Machining Process ◽  
Machining Accuracy ◽  
Accuracy Improvement ◽  
Servo Valve ◽  
Rotation Error ◽  
Spindle Rotation ◽  
Direct Compensation ◽  
Simulation And Experiment ◽  
Bearing System

Spindle rotation accuracy is important in machining process. Indirect compensation of spindle rotation error has been widely adopted in the field of machining accuracy improvement. However, there are some limitations on indirect compensation, and a little research on direct compensation can be found. This article utilizes active lubrication technology to improve the spindle rotation accuracy. Hydrostatic journal bearing with control recesses and servo valve drove by piezoelectric ceramics are adopted to compose the compensation element. The simple control strategy PID is adopted to provide control signal for servo valve. Both simulation and experiment are designed and conducted. The results show that proposed bearing system has the ability to improve the spindle rotation accuracy.

Full-factor matrix model of accuracy of dimensions performed on multi-purpose CNC machines

2021 ◽  
Vol 23 (4) ◽  
pp. 6-20
Author(s):  
Nizami Yusubov ◽  
◽  
Heyran Abbasova ◽  
Keyword(s):  
Matrix Models ◽  
Cutting Conditions ◽  
Machining Process ◽  
Technological System ◽  
Design System ◽  
Machining Accuracy ◽  
Factor Matrix ◽  
Cnc Machines ◽  
Plane Parallel ◽  
Base Points

Introduction. One of the main reasons that modern multi-purpose CNC machines do not use the capabilities of multi-tool processing is the lack of recommendations for design in this direction and, accordingly, for adjustment schemes. The study of the possibilities of multi-tool processing on multi-purpose machines is the subject of the work. The purpose of research: The problem of developing full-factor matrix models of dimensional accuracy and its sensitivity to the machining process is considered to increase the machining efficiency while ensuring machining accuracy using the technological capabilities of multi-tool machining on modern multi-purpose CNC machines. For this purpose, full-factor matrix models of the size scattering fields performed on multi-tool double-carriage adjustments have been developed, taking into account the cases of processing parts with dimensions that differ sharply in different directions, which are often encountered in practice, and in this case, the significant influence of the turns of the workpiece on the processing error, especially in directions with sharply different overall dimensions. Results of research: The developed accuracy models make it possible to calculate not only plane-parallel displacements of the technological system for double-carriage adjustments, but also angular displacements around base points, take into account the combined effect of many factors – a complex characteristic of the subsystems of the technological system (plane-parallel matrix of compliance and angular matrix of compliance), the geometry of the cutting tool , the amount of bluntness of the tool, cutting conditions, etc. As a result, based on the developed accuracy models, it is possible to obtain several ways to control multi-tool machining, including improving the structure of multi-tool adjustments, calculating the limiting values of cutting conditions. Based on the developed full-factor matrix models, it became possible to develop recommendations for the design of adjustments and the creation of an automated design system for multi-tool machining for a group of modern multi-purpose CNC lathes. Scope of the results: The results obtained can be used to create mathematical support for the design of operations in CAD-systems provided for multi-tool multi-carriage machining performed on multi-purpose machines. Conclusions: The developed models and methodology for simulating the machining accuracy make it possible to increase the accuracy and efficiency of simultaneous machining, to predict the machining accuracy within the specified conditions.

scholarly journals An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2946 ◽  
Author(s):  
Wangyang Wei ◽  
Honghai Wu ◽  
Huadong Ma
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Smart Cities ◽  
Prediction Method ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

scholarly journals Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Bilin Shao ◽  
Maolin Li ◽  
Yu Zhao ◽  
Genqing Bian
Keyword(s):  
Prediction Accuracy ◽  
Short Term Memory ◽  
High Reliability ◽  
Moving Average ◽  
Arima Model ◽  
Pso Algorithm ◽  
Metal Exchange ◽  
Nickel Metal ◽  
Learning Factor ◽  
Improved Pso

Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market’s participants; yet, traditional forecast methods are defective in prediction accuracy and applicability. Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long-short-term memory (LSTM) neural networks, for higher reliability. This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM. Nickel metal’s closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA). The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively. In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers.

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