A prediction model of drilling force in CFRP internal chip removal hole drilling based on support vector regression

2021 ◽  
Author(s):  
Chengyang Xu ◽  
Songyang Yao ◽  
Gongdong Wang ◽  
Yiwen Wang ◽  
Jiazhong Xu
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Hole Drilling ◽  
Support Vector ◽  
Drilling Force ◽  
Chip Removal

scholarly journals Research on prediction model of drilling force in CFRP internal chip removal hole drilling based on SVR

2021 ◽  
Author(s):  
Chengyang Xu ◽  
Yao Songyang ◽  
Wang Gongdong ◽  
Wang Yiwen ◽  
Xu Jiazhong
Keyword(s):  
Prediction Model ◽  
Cutting Parameters ◽  
Resin Matrix ◽  
Hole Drilling ◽  
Support Vector ◽  
Matrix Composites ◽  
Drilling Force ◽  
Unknown Parameters ◽  
Drilling Quality ◽  
Chip Removal

Abstract Drilling force is the main factor affecting the drilling quality and tool wear of carbon fiber reinforced resin matrix composites (CFRP), selecting the appropriate process parameters can effectively control the drilling force, improve the drilling quality and tool life. In this paper, in order to accurately predict and effectively control the drilling force under the process of internal chip removal hole drilling: Firstly, based on the application of support vector regression (SVR) in data analysis, the theory of the prediction model of drilling force in CFRP is given; Secondly, on the basis of the above theories, the experiment of chip removal in CFRP is designed and completed, designed and completed the CFRP internal chip removal processing drilling experiment, it provides preparation for the solution of parameters in the subsequent model; Again, based on the above theoretical analysis and experimental data, under the premise of choosing the appropriate kernel function and loss function, the sequential minimum optimization (SMO) algorithm is applied to solve the unknown parameters in the model, to complete the construction of the SVR-based CFRP internal chip removal machining drilling force prediction model; Finally, using the constructed predictive model, it is predicted that when CFRP internal chip removal hole machining is studied, The relationship between cutting parameters (speed, feed), tool parameters (drill diameter, peak angle, relief angle) and suction parameters (negative pressure) and axial force.

SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL

2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG
Keyword(s):  
Fuel Cell ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.

scholarly journals Life prediction of slewing bearing based on isometric mapping and fuzzy support vector regression

2019 ◽  
Vol 42 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Weigang Bao ◽  
Hua Wang ◽  
Jie Chen ◽  
Bo Zhang ◽  
Peng Ding ◽  
...  
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Life Prediction ◽  
Feature Fusion ◽  
Prediction Method ◽  
Support Vector ◽  
Isometric Mapping ◽  
Slewing Bearing ◽  
Time Frequency ◽  
Life Prediction Model

The monitoring data of slewing bearing is massive. In order to establish accurate life prediction model from complex vibration signal of slewing bearing, a life prediction method based on manifold learning and fuzzy support vector regression (SVR) is proposed. Firstly, the multiple features are extracted from time domain and time-frequency domain. Then isometric mapping (ISOMAP) is used to reduce high-dimensional features to low-dimensional features that can reflect degeneration of slewing bearing well. Finally, the fuzzy SVR is used to predict the life degradation trend of slewing bearing. The results show that: (1) Multi-feature fusion after ISOMAP can obtain more comprehensive degradation indicator. (2) The complexity of the life prediction model is simplified and the real-time life degradation trend of slewing bearing can be well predicted by fuzzy SVR, so it is very suitable to predict life degradation trend of slewing bearing based on massive data well. The time of prediction on average is reduced by 72.7%. The mean absolute error (MAE) and root mean square error (RMSE) of prediction are reduced by 73% and 59% respectively compared with traditional methods. The accuracy of prediction is greatly improved.

Online Detecting of Inter-Turn Short-Circuit in Generator Rotor Winding Relying on ν-SVR Machine

2021 ◽  
pp. 2150026
Author(s):  
Feng Pan ◽  
Xiansheng Guo ◽  
Shengwang Pan
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Dynamic Simulation ◽  
Short Circuit ◽  
Back Propagation ◽  
Synchronous Generator ◽  
Support Vector ◽  
Diagnosis Method ◽  
Rotor Winding ◽  
Online Detecting

To probe an accurate diagnosing approach for synchronous generator (SG) with rotor winding inter-turn short-circuit, a novel online monitoring and detecting method relying on the [Formula: see text]-support vector regression ([Formula: see text]-SVR) machine was proposed, and its effectiveness was further verified by the micro-synchronous generator dynamic simulation. Terminal voltage, active and reactive power of SG were selected as input variables for a novel prediction model based on the [Formula: see text]-SVR, and field current was selected as an output variable of the prediction model. The structures and parameters of the field current prediction model were optimized with the particle swarm optimization (PSO) algorithm and training samples, then the prediction model was established and the field current prediction got under way. By comparing the predicted field current with the corresponding online measured field current, inter-turn short-circuit of rotor winding in SG could be detected sensitively once its absolute value of the prediction relative error exceeded a specific threshold. The micro-synchronous generator dynamic simulation indicated that the proposed online detecting approach based on the [Formula: see text]-SVR machine overcame the shortage of the back-propagation (BP) diagnosis method for misdiagnosis, and its accuracy, sensitivity and threshold setting range of the diagnosis method was the most prominent among these diagnosis methods such as the BP diagnosis method, the Bayesian regularization back-propagation (BRBP) diagnosis method and the [Formula: see text]-support vector regression ([Formula: see text]-SVR) diagnosis method.

scholarly journals Very Short-Term Load Forecasting Using Hybrid Algebraic Prediction and Support Vector Regression

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Marlon Capuno ◽  
Jung-Su Kim ◽  
Hwachang Song
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Peak Load ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Warning Signal ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting

This paper presents a model for very short-term load forecasting (VSTLF) based on algebraic prediction (AP) using a modified concept of the Hankel rank of a sequence. Moreover, AP is coupled with support vector regression (SVR) to accommodate weather forecast parameters for improved accuracy of a longer prediction horizon; thus, a hybrid model is also proposed. To increase system reliability during peak hours, this prediction model also aims to provide more accurate peak-loading conditions when considerable changes in temperature and humidity happen. The objective of going hybrid is to estimate an increase or decrease on the expected peak load demand by presenting the total MW per Celsius degree change (MW/C°) as criterion for providing a warning signal to system operators to prepare necessary storage facilities and sufficient reserve capacities if urgently needed by the system. The prediction model is applied using actual 2014 load demand of mainland South Korea during the summer months of July to September to demonstrate the performance of the proposed prediction model.

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