Predictive Model for Specific Energy Consumption in the Turning of AISI 316L Steel

Curra-Sosa, Dagnier-Antonio; Pérez‐Rodríguez, Roberto; Risco-Alfonso, Ricardo del

doi:10.1007/978-3-030-01132-1_6

Cited by 2 publications

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“…Liang et al (Liang, 2018) developed an ANN for the modeling and monitoring of energy consumption. Curra et al (Curra, 2018) obtained a predictive model for SEC in the turning of AISI 316L steel using ANN.…”

Section: Ann Architectures To Estimate the Sec In Dry Hsm Of Aisi 104...mentioning

confidence: 99%

Sinergias en la investigación en STEM

Arias¹,

Vázquez²,

Parejo³

2021

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En este trabajo, se evalúa la impedancia eléctrica de distintas muestras de titanio poroso modificadas superficialmente con un láser de femtosegundo; como método experimental para caracterizar la macroporosidad inherente al proceso de fabricación y el efecto del tratamiento superficial de los discos. El estudio se realiza sobre discos con distinto contenido en volumen y rango de tamaño de poros. Las medidas de impedancia eléctrica se realizaron usando el Hewkett-Packard 4395A de Agilent. Los resultados revelan la potencialidad de esta técnica de medida en términos de ventajas frente a técnicas más engorrosas y costosas, permitiendo de forma semi-cuantitativa relacionar las medidas de impedancia con el contenido y tamaño de los poros, así como detectar el efecto de la modificación superficial generada con el láser de femtosegundo (microporosidad adicional y capa de óxido de titanio).

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Section: Ann Architectures To Estimate the Sec In Dry Hsm Of Aisi 104...mentioning

confidence: 99%

Sinergias en la investigación en STEM

Arias¹,

Vázquez²,

Parejo³

2021

View full text Add to dashboard Cite

show abstract

“…Curra et al [36] developed a model to predict specific energy consumption using an ANN during the turning of an AISI 316L steel and compared dry machining with the minimum quantity of lubricants. The authors used a JX1 ceramic as the cutting tool and up to intermediate cutting speeds.…”

Section: Introductionmentioning

confidence: 99%

Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

Hernández-González

Curra-Sosa

Pérez‐Rodríguez

et al. 2021

TecnoL.

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Cutting forces are very important variables in machining performance because they affect surface roughness, cutting tool life, and energy consumption. Reducing electrical energy consumption in manufacturing processes not only provides economic benefits to manufacturers but also improves their environmental performance. Many factors, such as cutting tool material, cutting speed, and machining time, have an impact on cutting forces and energy consumption. Recently, many studies have investigated the energy consumption of machine tools; however, only a few have examined high-speed turning of plain carbon steel. This paper seeks to analyze the effects of cutting tool materials and cutting speed on cutting forces and Specific Energy Consumption (SEC) during dry high-speed turning of AISI 1045 steel. For this purpose, cutting forces were experimentally measured and compared with estimates of predictive models developed using polynomial regression and artificial neural networks. The resulting models were evaluated based on two performance metrics: coefficient of determination and root mean square error. According to the results, the polynomial models did not reach 70 % in the representation of the variability of the data. The cutting speed and machining time associated with the highest and lowest SEC of CT5015-P10 and GC4225-P25 inserts were calculated. The lowest SEC values of these cutting tools were obtained at a medium cutting speed. Also, the SEC of the GC4225 insert was found to be higher than that of the CT5015 tool.

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Predictive Model for Specific Energy Consumption in the Turning of AISI 316L Steel

Cited by 2 publications

References 10 publications

Sinergias en la investigación en STEM

Sinergias en la investigación en STEM

Modeling Cutting Forces in High-Speed Turning using Artificial Neural Networks

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