Automated intelligent manufacturing system for surface finish control in CNC milling using support vector machines

Ramesh, R.; Kumar, K. S. Ravi; Anil, G.

doi:10.1007/s00170-008-1676-1

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…SVM is a novel machine learning method based on the statistical learning theory, which is suitable for classification and regression problems with small numbers of samples, nonlinearity, high dimension, and local minima. 17,18 The lib-SVM tool box 19 is employed due to its high capacity of solving regression problems. The built-in optimization algorithm of this tool box is sequential minimal optimization (SMO), which possesses the property of quick convergence and small storage of kernel matrix.…”

Section: An Svm Modelmentioning

confidence: 99%

A composite model of field reliability based on a generalized Arrhenius model and a support vector machine model for spindle systems

Yang

Chen

et al. 2018

Advances in Mechanical Engineering

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A spindle system is one of the key subsystems of machine tools, whose reliability affects machining precision and production cycle directly. In the field, spindle systems expose to operating environmental conditions and complex working conditions that are referred to as stresses in this article collectively, which can accelerate or decelerate the process of failure. In order to analyze field reliability of spindle systems, the main structure, failure modes of spindle systems are analyzed, and main stresses involved with reliability are determined preliminarily. Then, based on the failure mode and the characteristics of stresses, a linear relationship of stresses and field reliability is built based on generalized Arrhenius models assuming that stresses are independent of each other. The non-linear, coupling relationship of stresses is described by a support vector machine model, whose parameters are selected by cross-validation. Then, two models are integrated to a composite model for minimum assessment error using optimal combined forecasting method. Finally, the proposed model is validated by a real case study, and the assessment errors conform to the production requirement.

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Section: An Svm Modelmentioning

confidence: 99%

A composite model of field reliability based on a generalized Arrhenius model and a support vector machine model for spindle systems

Yang

Chen

et al. 2018

Advances in Mechanical Engineering

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“…This study also proved that using available experimental data, accurate predictive performance metrics can be obtained through a small number of training and testing sessions. Ramesh et al [32] used Support Vector Machine (SVM) methods to predict the surface roughness of end mills on 6061 aluminum. Using feed speed, spindle speed, and cutting depth as input to predict surface roughness, the model had an error rate of 8.34%.…”

Section: Introductionmentioning

confidence: 99%

An integrated curvature surface inspection and prediction system for 5-axis synchronization machining

Kuo

Weng

2021

Int J Adv Manuf Technol

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There is an urgent demand for free form products in industry at the present time because of their superior appearance and the wide variety of functions they perform. Five-axis high-speed CNC machining technology has developed to satisfy this demand, but further improvement in surface quality metric inspection technology is the big challenge it now faces. In this study, the effects of jerk on the performance of five-axis synchronous high-speed CNC ball nose end mills on a freeform turbine mold were investigated. The relationships of characteristics of the images of 14 jerk-cluster finished workpieces with different jerk setting values were established, allowing surface texture features to be analyzed and surface roughness predicted. In addition, machine learning methods were integrated with the surface feature analysis to construct a virtual machining module that acts as a performance prediction system, merging the virtual machine tool functions, surface texture processor and AI roughness prediction processor. Using the geometric information of the workpiece, cutting parameters and machine tool parameters as inputs, product performance metrics combining surface roughness and machining time can be predicted as outputs of the system. The integrated system provides users with a way to evaluate manufacturing performance before performing actual operations and to reduce test time for cutting parameter development. The model is suitable for complex surface finishes as well as for the production of small batches with high parametric variance. In addition, the partial set of image processing and roughness prediction modules can be used alone as an effective intelligent surface quality inspection system.

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“…Data-driven approaches use learning algorithms and experimental data to capture underlying influence of control parameters on outputs and build prediction models so that an in-depth understanding of underlying physical processes 2 Complexity is not a prerequisite [9]. Multivariable regression analysis [10,11], response surface methodology [12,13], artificial neural networks (ANN) [14][15][16], and support vector machine (SVM) [17][18][19] are the most widely data-driven approaches applied for modeling machined surface roughness. Other techniques like ensembles also are used for surface roughness prediction.…”

Section: Introductionmentioning

confidence: 99%

“…The grid search method was used to determine the internal parameters of SVM (penalty factor C and kernel parameter ), but the values of internal parameter gained by grid search method relied on the determination of jumping interval. Ramesh et al [18] used SVM to conduct prediction model of surface finish for end milling on 6061 aluminum. The SVM model can predict with 8.34% error which was a better performance compared to the regression model with 9.71% error.…”

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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Automated intelligent manufacturing system for surface finish control in CNC milling using support vector machines

Cited by 18 publications

References 10 publications

A composite model of field reliability based on a generalized Arrhenius model and a support vector machine model for spindle systems

A composite model of field reliability based on a generalized Arrhenius model and a support vector machine model for spindle systems

An integrated curvature surface inspection and prediction system for 5-axis synchronization machining

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

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