In-process surface roughness prediction system using cutting vibrations in turning

Salgado, David Rodríguez; Sánchez, Francisco Javier Alonso; Cambero, I.; Marcelo, Álvaro

doi:10.1007/s00170-008-1698-8

Cited by 114 publications

(57 citation statements)

References 29 publications

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“…For micro and macro milling operations different modeling techniques have been applied for tool wear or part quality estimation and they include response surfaces [8,[22][23][24][25], and Artificial Intelligence (AI) models such as Artificial Neural Networks (ANN) [8, 11-13, 15, 23-27], Adaptive Neuro Fuzzy Inference Systems (ANFIS) [28,29], Fuzzy Systems [12,30,31], Hidden Markov Models (HMM) [24], Bayesian Networks (BN) [32,33] and Least Squares Support Vector Machines (LS-SVM) [34][35][36]. Comprehensive reviews about modeling techniques applied in machining can be found in [19,37,38].…”

Section: Estimation Modulementioning

confidence: 99%

“…Recently, Support Vector Machines (SVM) such as Least Squares Support Vector Machines (LS-SVM) have been successfully applied in machining systems for surface roughness prediction [34,35] and cutting-tool wear [36] and several research works remark the advantages of LS-SVM with respect to ANN in terms of better generalization, global minimum and less overfitting problems [35,36]. In this work, both ANN and LS-SVM models are developed and analyzed in order to apply the most adequate in the ACO system.…”

Section: Training Testing and Validating Ai Modelsmentioning

confidence: 99%

“…The fundamentals of LS-SVM models for surface roughness and tool wear prediction can be found in previous research works such as [34][35][36]. As suggested in these works, the LS-SVM models used are based on Gaussian radial basis functions as nonlinear kernels in order to improve the generalization capability.…”

Section: Training Testing and Validating Ai Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Section: Estimation Modulementioning

confidence: 99%

Section: Training Testing and Validating Ai Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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“…An in-process surface roughness estimation procedure, based on least-squares support vector machines, was proposed in [13]. The cutting conditions (feed rate, cutting speed, and depth of cut), parameters of tool geometry (nose radius and nose angle), and features extracted from the vibration signals constituted the input information.…”

Section: Review Of Literaturementioning

confidence: 99%

Pre-evaluation on surface profile in turning process based on cutting parameters

Chen

et al. 2009

Int J Adv Manuf Technol

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile preevaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.

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“…Normally, R a value is influenced by many factors such as machining parameters, cutting phenomena, workpiece properties and cutting tool properties [15,16]. Based on previous literature, R a is one of the machining performance measurements frequently considered by researchers [4,11,13,14].…”

Section: Introductionmentioning

confidence: 99%

An experimental result of surface roughness machining performance in deep hole drilling

et al. 2016

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Abstract. This study presents an experimental result of a deep hole drilling process for Steel material at different machining parameters which are feed rate (f), spindle speed (s), the depth of the hole (d) and MQL, number of drops (m) on surface roughness, R a . The experiment was designed using two level full factorial design of experiment (DoE) with centre points to collect surface roughness, R a values. The signal to noise (S/N) ratio analysis was used to discover the optimum level for each machining parameters in the experiment.

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In-process surface roughness prediction system using cutting vibrations in turning

Cited by 114 publications

References 29 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Pre-evaluation on surface profile in turning process based on cutting parameters

An experimental result of surface roughness machining performance in deep hole drilling

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