Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Rao, K. Venkata; Murthy, P. B. G. S. N.

doi:10.1007/s10845-016-1197-y

Cited by 104 publications

(51 citation statements)

References 34 publications

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“…The laser-cutting process, roughness optimization according to the laser input parameters and quality evaluation of the cut surfaces were studied in a number of papers. The influence of the laser-cutting parameters on R a was studied by Mesko et al [16] for S235JR steel, by Rao and Murthy [17] for stainless steel, by Adarsha Kumar [18] for AISI 4340 steel. Riveiro et al [19] studied the interaction between the assistant gas and the part as well as the role of the nozzle on R a for AISI 4340 steel.…”

Section: Methods and Experimentalmentioning

confidence: 99%

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mileșan

Girdu

Cîrțînă

et al. 2020

SV-JME

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This study supports the research in the field of laser cutting of steel sheets HARDOX400 (H400), a less well-known and analysed material. An H400 sheet with a thickness of 10 mm was used; a cutting plan was prepared according to Taguchi's method; the input parameters (laser power, assistant gas pressure and cutting speed) were varied according to a specific rule, producing nine pieces. The initial factorial experiment was replicated four times under the same conditions as the initial one. The values for the cutting width between the piece and the plate, the hardness, and the roughness of the 45 resulting pieces were measured. The results were interpreted for each measured parameter, and by using design of experiments (DOE) from the Statistics 7.0 software, we obtained the description of roughness (R a ) in terms of power and speed using Response Surface Method (RSM) for prediction and correlation formula. Highlights• Indication of the mathematical expression of the roughness R a from algebraic and differential points of view. • Description of the cutting parameters of the CO 2 laser installation for cutting a Hardox400 plate according to a cutting plan, consisting of 45 pieces. • The use of the response surface and the three dimensional (3D) graph to determine the roughness R a depending on the input parameters, laser power, and cutting speed. • Establishing the formula for correlating the roughness R a according to the laser power and the cutting speed within the linear and quadric model, respectively the statistical calculation within the linear and quadratic interaction.

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Section: Methods and Experimentalmentioning

confidence: 99%

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mileșan

Girdu

Cîrțînă

et al. 2020

SV-JME

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“…In the present study, analysis of variance (ANOVA) was carried out at 95% of confidence level to analyze the experimental data of Reynolds number, void content, tensile, flexural and impact strengths. The sources having P values less than 0.05 and F values greater than 4 are identified as significant parameters [6]. Table 2 represents ANOVA for Reynolds number, void content, tensile, flexural and impact strengths with linear, square and two factor interaction models.…”

Section: Analysis Of Variancementioning

confidence: 99%

“…RSM was used reliably and exactly to model surface roughness, thrust force and delamination in drilling of carbon/epoxy composites and predict their values [5]. RSM was implemented for metals to make a relationship between cutting parameters, surface roughness and work piece vibration [6]. RSM was used to determine the machining performance under the influence of various parameters of machining.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Kopparthi

Kundavarapu

Dasari³

et al. 2019

SN Appl. Sci.

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The present work is aimed at determining mechanical properties of chopped strand glass fiber reinforced composite laminates manufactured based on the design of experiments by resin transfer molding at various injection pressures with 4, 5 and 6 layers. Response surface methodology was implemented to the experimental data for evaluating the effect of number of layers and resin injection pressure on mechanical properties and void content. Teaching learning based optimization (TLBO) has been proposed to predict optimal (maximum) mechanical properties of composite by optimizing the number of layers and injection pressure. Artificial neural network (ANN) with feed forward back propagation algorithm was also used to predict the responses and compare with experimental and TLBO results. It was found that the predicted values of responses from TLBO and ANN are good in agreement with experimental results.Keywords Artificial neural network (ANN) · Teaching learning based optimization (TLBO) · Glass fiber reinforced plastic (GFRP) · Resin transfer molding (RTM) · Mechanical properties

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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%

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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Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

Cited by 104 publications

References 34 publications

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Mathematical Modelling Study of Hardox400 Steel Parts’ Roughness and Hardness, Cut with CO2 Laser

Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

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

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