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

Mia, Mozammel; Dhar, Nikhil Ranjan

doi:10.1016/j.measurement.2016.06.048

Cited by 179 publications

(89 citation statements)

References 36 publications

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“…In anticipation of the next sixth technology revolution, it is becoming an increasingly important technique for processing large data sets using artificial intelligence and the integration of artificial intelligence algorithms in automated production. Many previous investigations have been devoted towards developing prediction models for rough turning [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Risbood et al [1] researched and produced models for forecasting roughness and dimensional deviation for dry and wet turning of mild steel rods.…”

Section: Introductionmentioning

confidence: 99%

“…Al Bahkali et al [15] studied the effect of feed, cutting depth, radius of curvature of the tool tip and the cutting speed on surface roughness in turning cast iron. Mia and Dhar [16] developed an artificial neural network (ANN) model to predict the average surface roughness in turning hardened steel EN 24 T. Jurkovic et al [17] compared three machine learning methods for predicting the high-speed turning observed parameters (surface roughness (Ra), cutting force (Fc), and the tool life (T)). Tootooni et al [18] reported surface roughness using a noncontact measurement method during the turning process.…”

Section: Introductionmentioning

confidence: 99%

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Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

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High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of highstrength steel featuring three factors-cutting speed V, feed rate f, and depth of cut t-on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Because surface roughness affects several functional attributes such as corrosion resistance, tribological characteristics, fatigue strength, and wear resistance of machined components, various researchers have employed methods which includes experimental, statistical, and analytical approaches in hard turning operation with various workpiece materials [18CrMo4, 42CrMo4, SEA8620, AISI 1040, 1045 for modeling and optimization using response surface methodology (RSM) (Elbah et al 2013;Hessainia et al 2013;Shihab et al 2014;Azam et al 2015;Meddour et al 2015;Bouzid et al 2015), Taguchi method (Gunay and Yucel 2013;Rashid et al 2016;Zerti et al 2016;Panda et al 2016;Das et al 2017a), ANN (Asiltürk and Çunkaş 2011;Pontes et al 2012;Asiltürk 2012;Mia and Dhar 2016), GRA (Sahoo and Sahoo 2013a;Kacal and Yildirim 2012;Senthilkumar et al 2014), GA (Batish et al 2014;Bouacha and Terrab 2016), and particle swarm optimization (PSO) (Stryczek and Pytlak 2014;Yue et al 2016) to attain the surface quality and dimensional finishing condition similar to costly cylindrical grinding. For example, Hessainia et al (2015) found that response surface methodology represents a powerful approach and can offer to scientific researchers as well industrial metal workers a helpful optimization procedure for various combinations of the workpiece and the cut material tool.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation

Panda

Das

Dhupal

2017

Process Integr Optim Sustain

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The paper addresses the assessment, modeling, and optimization study of surface roughness in finish dry hard turning (FDHT) of AISI 4340 steel with coated ceramic tool by considering the cutting speed, axial feed, depth of cut, and nose radius as machining parameters. Thirty sets of longitudinal turning trials based on central composite (CCD) design of experiments (DOEs) are performed, and response surface methodology (RSM), particle swarm optimization (PSO), and finally, Gilbert's approach are subsequently applied for mathematical modeling, response optimization, tool life estimation, and economic analysis. Additionally, various diagnostic tests have been executed to check the statistical significance and validity, adequacy, effectiveness, and fitness of data of the proposed model using analysis of variance (ANOVA) and Anderson-Darling normal probability test. Results indicated that nose radius and feed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness is considered. The RSM model combined with PSO technique leads to minimum surface roughness value similar to cylindrical grinding of 0.2021 μm, corresponding to optimum process parameters: 220 m/min of cutting speed, 0.05 mm/rev of feed, 0.193 mm of depth of cut, and 1.6 mm of tool nose radius. Finally, under pre-cited optimum machining conditions, tool life was evaluated to perform cost analysis for the economical justification and feasibility of coated ceramic tools in hard turning. The total machining cost per piece is ensued to be lower ($0.34) as a consequence of higher tool life (44 min), reduction in downtime, and enhancement in savings, which finds economical benefits in hard turning. The novelty aspects of the present work are that (i) it demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing coated ceramic tool in hard turning processes considering technological, economical, and ecological aspects, which are helpful and efficient from industrial point of view and (ii) it contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-55 HRC.

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“…Estas son muy populares en la modelación de sistemas debido a su alta eficiencia en la adaptación y en el aprendizaje mediante el reconocimiento de patrones [3].…”

Section: Estrategia De Predicción Usando Redes Neuronales Artificialesunclassified

“…Dentro de ellos, son fáciles de ajustar los pará-metros de corte con el objetivo de lograr el rendimiento esperado [3].…”

Section: Introductionunclassified

Comparación entre redes neuronales artificiales y regresión múltiple para la predicción de la rugosidad superficial en el torneado en seco

Morales-Tamayo

Hernández

Carrera

et al. 2018

ings

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Recibido: 30-11-2017, aprobado tras revisión: 18-12-2017 Forma sugerida de citación: Morales, Y.; Zamora, Y.; Vásquez, P.; Porras, M.; Bárzaga, J.; López, R. (2018). «Com-paración entre redes neuronales artificiales y regresión múltiple para la predicción de la rugosidad superficial en el torneado en seco». Ingenius. N.• 19, (enero-junio Resumen AbstractLos métodos de regresión múltiple y redes neuronales artificiales son técnicas usadas en muchas aplicaciones de la industria. En este trabajo se utilizaron dos métodos de predicción: regresión múltiple y redes neuronales artificiales (perceptrón multicapa) con el objetivo de predecir la rugosidad superficial en el torneado en seco del acero AISI 316l. En su implementación fueron considerados varios parámetros de corte como la velocidad, el avance y el tiempo de mecanizado. Las ecuaciones obtenidas por ambos métodos fueron comparadas desarrollando un diseño factorial completo para aumentar la fiabilidad de los valores registrados de rugosidad superficial. En el análisis se puede comprobar mediante los valores de coeficientes de determinación que los modelos propuestos son capaces de predecir la rugosidad superficial. Los modelos obtenidos demuestran que la técnica de redes neuronales artificiales tiene mejor precisión que la regresión múltiple para este estudio.The simple regression and artificial neural network methods are techniques used in many industrial aplications. This work developed two models in order to predict the surface roughness in dry turning of AISI 316L stainless steel. In its implementation they were considered various cutting parameters such as cutting speed, feed, and machining time. The models obtained by both methods were compared to develop a full factorial design to increase reliability of the recorded values of roughness. The analysis can be corroborated by the values of coefficients of determination that the proposed models are able to predict for surface roughness. The obtained results show that the neural networks techniques are more accurate than the multiple regression techniques for this study.Palabras clave: acero inoxidable AISI 316L, análisis de varianza y regresión, redes neuronales artificiales, rugosidad superficial, torneado de alta velocidad.

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

Cited by 179 publications

References 36 publications

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation

Comparación entre redes neuronales artificiales y regresión múltiple para la predicción de la rugosidad superficial en el torneado en seco

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