Comparison of two methods for predicting surface roughness in turning stainless steel AISI 316L

Morales-Tamayo, Yoandrys; Reyna, Roberto Félix Beltrán; Bustamante, Ringo John López; Hernández, Yusimit Zamora; Cedeño, KimberlyMagaly López; Herrera, Héctor Cochise Terán

doi:10.4067/s0718-33052018000100097

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Much research and development has been carried out in the field of prediction and control of surface roughness using MV. Regarding the way of calculating the roughness parameters, these methods can be divided into analytical methods [7][8][9][10][11], where parameters extracted from images are correlated to the measured roughness by a mathematical function, and methods engaging artificial intelligence (AI) [12][13][14][15][16][17][18][19] to build the roughness predictive models.…”

Section: Related Workmentioning

confidence: 99%

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Evolutionary Design of a System for Online Surface Roughness Measurements

Koblar

Filipič

2021

Mathematics

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Surface roughness is one of the key characteristics of machined components as it affects the surface quality and, consequently, the lifetime of the components themselves. The most common method of measuring the surface roughness is contact profilometry. Although this method is still widely applied, it has several drawbacks, such as limited measurement speed, sensitivity to vibrations, and requirement for precise positioning of the measured samples. In this paper, machine vision, machine learning and evolutionary optimization algorithms are used to induce a model for predicting the surface roughness of automotive components. Based on the attributes extracted by a machine vision algorithm, a machine learning algorithm generates the roughness predictive model. In addition, an evolutionary algorithm is used to tune the machine vision and machine learning algorithm parameters in order to find the most accurate predictive model. The developed methodology is comparable to the existing contact measurement method with respect to accuracy, but advantageous in that it is capable of predicting the surface roughness online and in real time.

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Section: Related Workmentioning

confidence: 99%

“…Morales Tamayo et al [18] used an ANN model to predict the steel surface roughness in the dry turning process of stainless steel. The researchers produced the specimens by varying the cutting parameters during the turning process.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Design of a System for Online Surface Roughness Measurements

Koblar

Filipič

2021

Mathematics

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“…An inverse relationship was observed for the effect of feed rate (FR). Tamayo et al [29] used artificial neural networks and multiple regression to predict the mean roughness of dry turning 316L steel using GC1115 and GC2015 inserts. It was found that decreasing the feed rate resulted in a reduction in the mean roughness Ra.…”

Section: Introductionmentioning

confidence: 99%

Improving the Surface Integrity of 316L Steel in the Context of Bioimplant Applications

2023

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Bioimplants should meet important surface integrity criteria, with the main goal of the manufacturing process to improve wear and corrosion resistance properties. This requires a special approach at the cutting stage. During this research, the impact of the cutting parameters on improving the surface integrity of AISI 316L steel was evaluated. In this context of bioimplant applications, the mean roughness Sa value was obtained in the range of 0.73–4.19 μm. On the basis of the results obtained, a significant effect was observed of both the cutting speed and the feed rate on changes in the microstructure of the near-surface layer. At a cutting speed of 150 m/min, the average grain size was approximately 31 μm. By increasing the cutting speed to 200 m/min, the average grain size increased to approximately 52 μm. The basic austenitic microstructure of AISI 316L steel with typical precipitation of carbides on the grain boundaries was refined at the near-surface layer after the machining process. Changing the cutting speed determined the hardness of the treated and near-surface layers. The maximum value of hardness is reached at a depth of 20 μm and decreases with the depth of measurement. It was also noted that at a depth of up to 240 μm, the maximum hardness of 270–305 HV1 was reached, hence the height of the machining impact zone can be determined, which is approximately 240 μm for almost all machining conditions.

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“…In the search for better prediction values, some studies compare statistical methodologies to ANNs. Authors in [ 37 ] studied the AISI316L steel dry turning surface roughness prediction, by comparing ANNs to multiple regression methods. The results found by the methods indicate a better artificial neural networks accuracy.…”

Section: Introductionmentioning

confidence: 99%

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

et al. 2020

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This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.

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Comparison of two methods for predicting surface roughness in turning stainless steel AISI 316L

Cited by 7 publications

References 16 publications

Evolutionary Design of a System for Online Surface Roughness Measurements

Evolutionary Design of a System for Online Surface Roughness Measurements

Improving the Surface Integrity of 316L Steel in the Context of Bioimplant Applications

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

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