Predictive modeling of surface roughness in lenses precision turning using regression and support vector machines

Wang, Xinsheng; Kang, Min; Fu, Xiuqing; Li, Chunlin

doi:10.1007/s00170-013-5231-3

Cited by 24 publications

(10 citation statements)

References 28 publications

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“…In an effort to correlate cutting conditions with cutting forces and machined surface roughness in end milling, Maher et al 18 developed an adaptive neuro-fuzzy inference system model to predict average surface roughness using cutting force data. Wang et al 19 performed lens slow tool servo turning experiments and developed regression and least squares support vector machine (LS-SVM) models to predict the average surface roughness. Their models included five inputs: the tool nose radius, feed rate, depth of cut, discretization angle, and C-axis speed.…”

Section: Machined Surface Roughnessmentioning

confidence: 99%

“…The most significant contribution of the present study is that the effect of the tool edge radius is taken into account in the evaluation and modeling of machined surface roughness. Note that the tool edge radius 1 and tool nose radius 19 are two totally different concepts measured in different planes. Figure 1 shows the difference between the tool edge radius and tool nose radius.…”

Section: The Contribution Of the Present Studymentioning

confidence: 99%

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Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Fang

Pai

2020

Journal of Testing and Evaluation

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Tool edge radius plays a significant role in affecting the surface integrity of machined products. The vast majority of existing research, however, takes no account of the effect of tool edge radius in the evaluation and modeling of machined surface roughness, an essential indicator of surface integrity. The present study fills this important research gap and has performed a total of 45 turning experiments on Unified Numbering System (UNS) A92024-T351 aluminum alloy with carefully selected cutting tools with three levels of tool edge radii. This article describes the experimental setup and measurements of tool edge radius and machined surface roughness. Machined surface roughness was evaluated using five parameters, including average roughness, root-mean-square roughness, peak roughness, maximum roughness height, and five-point average roughness. The experimental evidence presented in this article shows that the tool edge radius has a profound effect on machined surface roughness, cutting forces, and cutting vibrations. Based on the experimental data, three types of predictive models are developed, including a multiple regression model, multilayer perceptron neural network model, and radial basis function neural network model. The prediction accuracy of the three models is compared based on average mean squared errors. The results show that different models lead to different prediction accuracy for different surface roughness parameters.

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Section: Machined Surface Roughnessmentioning

confidence: 99%

Section: The Contribution Of the Present Studymentioning

confidence: 99%

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Fang

Pai

2020

Journal of Testing and Evaluation

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“…The turning parameters were found by using coupled simulated annealing (CSA) method. Wang et al [24] established LS-SVM model with radial basis function and exponential model to predict surface roughness in lenses precision turning. The comparison of the LS-SVM and exponential models was also carried out, and the LS-SVM model was found to be capable of better prediction precision for surface roughness.…”

Section: Introductionmentioning

confidence: 99%

An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes

Lü

Liao

et al. 2019

Complexity

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It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and -insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC-SVM are better than DE-SVM, GA-SVM, and PSO-SVM except three indicator values of DE-SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.

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“…SVM has been widely applied to solve the problem of function fitting [9]. However, the generalization ability of SVM depends heavily on the appropriate parameters, the model's parameters has huge influence on the precision of the model predictions [10][11]. Thus, many optimization algorithms have been adopted to optimize the SVM parameters, like the particle swarm optimization algorithm, genetic algorithm and glowworm swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

Jiang¹,

Lan²,

Jiang³

et al. 2017

Preprint

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Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

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Predictive modeling of surface roughness in lenses precision turning using regression and support vector machines

Cited by 24 publications

References 28 publications

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

Evaluation and Modeling of the Effect of Tool Edge Radius on Machined Surface Roughness in Turning UNS A92024-T351 Aluminum Alloy

An Effective ABC-SVM Approach for Surface Roughness Prediction in Manufacturing Processes

Dynamic Measurement Errors Prediction Model of Sensors Based on NAPSO-SVM

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