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.

Temperature Field Study of Hole Drilling in Kevlar Composites

2016 ◽  
Vol 1136 ◽  
pp. 215-220 ◽  
Author(s):  
Yong Jie Bao ◽  
Yi Ni Zhang ◽  
Hang Gao ◽  
Xue Shu Liu
Keyword(s):  
Temperature Field ◽  
Heat Source ◽  
Field Model ◽  
Resin Matrix ◽  
Hole Drilling ◽  
Fiber Direction ◽  
Drilling Process ◽  
Matrix Composites ◽  
Cutting Heat ◽  
Temperature Field Model

The cutting heat, during the process of drilling fiber reinforced resin matrix composites, has a significant effect on the quality and the tool wear. In this paper, based on the homogenization hypothesis of the material and the finite difference method, a temperature field model for drilling unidirectional Kevlar composites was developed. During the drilling process, the heat source formed by cutting edge and chisel edge can be seen as a conical heat source. The results show that the temperature field distribution is ellipse away from the drilling zone with the longer axis paralleling to the fiber direction and changes close to the drilling zone.

Study on the Stainless Steel 1Cr18Ni9Ti Micro-Hole Drilling Experiment

2014 ◽  
Vol 596 ◽  
pp. 43-46 ◽  
Author(s):  
Ben Hong Li ◽  
Zhi Liu ◽  
Hao Wang ◽  
Zao Sheng Zhong
Keyword(s):  
Stainless Steel ◽  
High Speed ◽  
Tool Material ◽  
High Speed Steel ◽  
Cutting Parameters ◽  
Hole Drilling ◽  
Analysis Method ◽  
Drilling Force ◽  
Micro Hole ◽  
Hole Machining

Stainless steel is difficult to machine, especially micro-hole machining. In order to obtain the effect of drilling force by tool material and cutting parameters, the drilling experiments on stainless steel 1Cr18Ni9Ti have been done by diameter of 1.2 mm containing cobalt high speed steel and carbide drill. According to the experimental results, analysising the reason of drilling force change under different parameters and establishing a carbide drill drilling force empirical formula which combined with the regression analysis method.

Research on the prediction model of micro-milling surface roughness of Inconel718 based on SVM

2016 ◽  
Vol 68 (2) ◽  
pp. 206-211 ◽  
Author(s):  
Xiaohong Lu ◽  
Xiaochen Hu ◽  
Hua Wang ◽  
Likun Si ◽  
Yongyun Liu ◽  
...  
Keyword(s):  
Surface Roughness ◽  
High Temperature ◽  
Prediction Model ◽  
High Precision ◽  
High Strength ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Support Vector ◽  
Content Type ◽  
Roughness Prediction

Purpose – The purpose of this paper is to establish a roughness prediction model of micro-milling Inconel718 with high precision. Design/methodology/approach – A prediction model of micro-milling surface roughness of Inconel718 is established by SVM (support vector machine) in this paper. Three cutting parameters are involved in the model (spindle speed, cutting depth and feed speed). Experiments are carried out to verify the accuracy of the model. Findings – The results show that the built SVM prediction model has high prediction accuracy and can predict the surface roughness value and variation law of micro-milling Inconel718. Practical implication – Inconel718 with high strength and high hardness under high temperature is the suitable material for manufacturing micro parts which need a high strength at high temperature. Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness prediction model with high precision is helpful to select the cutting parameters for micro-milling Inconel718. Originality/value – The built SVM prediction model of micro-milling surface roughness of Inconel718 is verified by experiment for the first time. The test results show that the surface roughness prediction model can be used to predict the surface roughness during micro-milling Inconel718, and to provide a reference for selection of cutting parameters of micro-milling Inconel718.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

ZrO2f-coated Cf hybrid fibrous reinforcements and properties of their reinforced ceramicizable phenolic resin matrix composites

2021 ◽  
Vol 41 (3) ◽  
pp. 1810-1816
Author(s):  
Zhenyue Zou ◽  
Yan Qin ◽  
Huadong Fu ◽  
Di Zhu ◽  
Zhuangzhuang Li ◽  
...  
Keyword(s):  
Phenolic Resin ◽  
Resin Matrix ◽  
Matrix Composites

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

scholarly journals Temperature monitoring in the subsurface during single lip deep hole drilling

2020 ◽  
Vol 87 (12) ◽  
pp. 757-767
Author(s):  
Robert Wegert ◽  
Vinzenz Guski ◽  
Hans-Christian Möhring ◽  
Siegfried Schmauder
Keyword(s):  
Cutting Parameters ◽  
Drill Hole ◽  
Machining Process ◽  
Hole Drilling ◽  
Drilling Process ◽  
Deep Hole ◽  
Machining Processes ◽  
Deep Hole Drilling ◽  
Feed Force ◽  
Cutting Zone

AbstractThe surface quality and the subsurface properties such as hardness, residual stresses and grain size of a drill hole are dependent on the cutting parameters of the single lip deep hole drilling process and therefore on the thermomechanical as-is state in the cutting zone and in the contact zone between the guide pads and the drill hole surface. In this contribution, the main objectives are the in-process measurement of the thermal as-is state in the subsurface of a drilling hole by means of thermocouples as well as the feed force and drilling torque evaluation. FE simulation results to verify the investigations and to predict the thermomechanical conditions in the cutting zone are presented as well. The work is part of an interdisciplinary research project in the framework of the priority program “Surface Conditioning in Machining Processes” (SPP 2086) of the German Research Foundation (DFG).This contribution provides an overview of the effects of cutting parameters, cooling lubrication and including wear on the thermal conditions in the subsurface and mechanical loads during this machining process. At first, a test set up for the in-process temperature measurement will be presented with the execution as well as the analysis of the resulting temperature, feed force and drilling torque during drilling a 42CrMo4 steel. Furthermore, the results of process simulations and the validation of this applied FE approach with measured quantities are presented.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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