Surface Roughness Prediction using Artificial Neural Network in Hard Turning of AISI H13 Steel with Minimal Cutting Fluid Application

Beatrice, B. Anuja; Kirubakaran, E.; Thangaiah, P. Ranjit Jeba; Wins, K. Leo Dev

doi:10.1016/j.proeng.2014.12.243

Cited by 60 publications

(27 citation statements)

References 11 publications

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“…The R-value is found to be 0.99845 when the average surface roughness is predicted using '4-7-1' neural network (RMSE = 0.0294). This R-value is greater than the R-value (0.95962) found by Beatrice et al[26] when predicted Ra for minimum quantity lubrication assisted turning of steel. The points at the beginning have shown good fit then it slightly started deviating from the dashed line (actual value = predicted value).…”

contrasting

confidence: 61%

Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network

2016

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contrasting

confidence: 61%

Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network

2016

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“…They checked the capability of the ANN -GA approach for prediction as well as for optimization by use of real data set which had obtained from real machining experiment. [5]developed an Artificial neural network based predictive model to simulate hard turning of AISI H13 steel with minimum cutting fluid application to predict surface roughness of machined surface in a reference of cutting parameters. They trained networks using different set of training data for a fixed number of cycles and tested them using a set of input or output data reserved for that purpose.…”

Section: Literature Survey  Girish Kant and Kuldip Singhmentioning

confidence: 99%

Prediction of Surface Roughness on CNC Machine: A Review

Kumar¹,

Sarathe²

2016

IJERT

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In present rapidly changing scenario in production industries, application of predictive techniques is essential for metal cutting industry. It is a challenge for every industry to achieve predefined performance parameter in first trial.Predictive model is very essential in production industry to respond efficiently to severe competitiveness and continuously increasing demand of quality product with minimum cost in the market. Predictive models in metal cutting industries are considered as a vital tool for continuous improvement of product quality as well as minimizing the product cost. Optimization can be carried out with the help of predictive model. Determination of optimum input parameter for predefined performance parameter through cost-effective mathematical model is a very complex task. However, over the years, the predictive techniques have undergone various development and expansion. In this review paper, various predictive techniques like ANN, ANFIS, fuzzy logic, response surface method, taguchi method which are used for prediction of surface roughness has been critically appraised.A comparative study is carried out in tabular form of these predictive models. Surface roughness is taken as performance parameter for this paper.

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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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Surface Roughness Prediction using Artificial Neural Network in Hard Turning of AISI H13 Steel with Minimal Cutting Fluid Application

Cited by 60 publications

References 11 publications

Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network

Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network

Prediction of Surface Roughness on CNC Machine: A Review

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

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