Effect of very high cutting speeds on shearing, cutting forces and roughness in dry turning of austenitic stainless steels

Fernández-Abia, A.I.; Barreiro, J.; Lacalle, Luís Norberto López de; Martı́nez, Silvia

doi:10.1007/s00170-011-3267-9

Cited by 87 publications

(41 citation statements)

References 23 publications

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“…The chip produced from the cutting process in trial 7 was also examined using the SEM detector, as shown in Figure 6(b). The produced chip was found to have the same structure (shear lamellae) that appeared in the BUE microstructure which resulted due to the slipping mechanism, shearing, and fracturing behaviour during the cutting process, as reported in other studies [24,25]. However, the adhering wear presented by the formation of the BUE on the rake face of the cutting tool leads to the formation of the crater wear (CW), flank wear (abrasion wear), and chipping when the machining process proceeds, as the BUE particles are not stable and can easily be subtracted from the hosting surfaces [26].…”

Section: Tool Wear and Built-up Edge Investigation Using Scanningsupporting

confidence: 76%

Impacts of Wear and Geometry Response of the Cutting Tool on Machinability of Super Austenitic Stainless Steel

Alabdullah

Polishetty

Littlefair

2016

International Journal of Manufacturing Engineering

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This paper presents a study of tool wear and geometry response when machinability tests were applied under milling operations on the Super Austenitic Stainless Steel alloy AL-6XN. Eight milling trials were executed under two cutting speeds, two feed rates, and two depths of cuts. Cutting edge profile measurements were performed to reveal response of cutting edge geometry to the cutting parameters and wear. A scanning electron microscope (SEM) was used to inspect the cutting edges. Results showed the presence of various types of wear such as adhesion wear and abrasion wear on the tool rake and flank faces. Adhesion wear represents the formation of the built-up edge, crater wear, and chipping, whereas abrasion wear represents flank wear. The commonly formed wear was crater wear. Therefore, the optimum tool life among the executed cutting trails was identified according to minimum length and depth of the crater wear. The profile measurements showed the formation of new geometries for the worn cutting edges due to adhesion and abrasion wear and the cutting parameters. The formation of the built-up edge was observed on the rake face of the cutting tool. The microstructure of the built-up edge was investigated using SEM. The built-up edge was found to have the austenite shear lamellar structure which is identical to the formed shear lamellae of the produced chip.

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Section: Tool Wear and Built-up Edge Investigation Using Scanningsupporting

confidence: 76%

Impacts of Wear and Geometry Response of the Cutting Tool on Machinability of Super Austenitic Stainless Steel

Alabdullah

Polishetty

Littlefair

2016

International Journal of Manufacturing Engineering

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“…The optimization methodology has to consider that the hourly cost for modern machining centers is considerably higher than that of the cutting tools. This supports the application of HSM, regardless the expected short tool life obtained at the very high cutting speeds [31].…”

Section: Experimental Worksupporting

confidence: 69%

“…The range of the cutting speeds at which the cutting force components decrease, needs to be explored. The exploration of this range is essential to have the beneficial effects of HSM [31]. Chip compression ratio can be used to find the effect of the used machining conditions on the coefficient of friction at the tool-chip interface.…”

Section: Introductionmentioning

confidence: 99%

New Observations on High-Speed Machining of Hardened AISI 4340 Steel Using Alumina-Based Ceramic Tools

Shalaby

Veldhuis

2018

JMMP

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High-speed machining (HSM) is used in industry to improve the productivity and quality of the cutting operations. In this investigation, pure alumina ceramics with the addition of ZrO 2 , and mixed alumina (Al 2 O 3 + TiC) tools were used in the dry hard turning of AISI 4340 (52 HRC) at different high cutting speeds of 150, 250, 700 and 1000 m/min. It was observed that at cutting speeds of 150 and 250 m/min, pure alumina ceramic tools had better wear resistance than mixed alumina ones. However, upon increasing the cutting speed from 700 to 1000 m/min, mixed alumina ceramic tools outperformed pure ceramic ones. Scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) were used to investigate the worn cutting edges and analyze the obtained results. It was found that the tribo-films formed at the cutting zone during machining affected the wear resistances of the tools and influenced the coefficient of friction at the tool-chip interface. These observations were confirmed by the chip compression ratio results at different cutting conditions. Raising cutting speed to 1000 m/min corresponded to a remarkable decrease in cutting force components in the dry hard turning of AISI 4340 steel.

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“…The austenitic stainless steels are considered difficult materials to machine, a related feature with their low thermal conductivity, high thermal expansion coefficient, high ductility and high hardening by distortion. The finishing operations, in these kinds of steel, are carried out commonly with coated carbide inserts, the range of the recommended speeds are very conservative (200-350 m min -1 ) [4].…”

Section: Introductionmentioning

confidence: 99%

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

Morales-Tamayo

Reyna

Bustamante

et al. 2018

Ingeniare. Rev. chil. ing.

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The present study aimed to explore various models to predict the surface roughness in dry turning of AISI 316L stainless steel. Multiple Regression Methods and Artificial Neural Networks Methods were implemented to study the effect of cutting speed, feed, and machining time. In order to increase the reliability and soundness of the registered surface roughness values, a complete Factorial Design was implemented. A statistical comparison of the resultant models was performed. The results produced by both methods show that the surface roughness can be predicted. Results of the Artificial Neural Networks models show a better accuracy than those derived from the Multiple Regression models.Keywords: AISI 316L stainless steel, analysis of regression, artificial neural network, surface roughness, dry turning. RESUMEN

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Effect of very high cutting speeds on shearing, cutting forces and roughness in dry turning of austenitic stainless steels

Cited by 87 publications

References 23 publications

Impacts of Wear and Geometry Response of the Cutting Tool on Machinability of Super Austenitic Stainless Steel

Impacts of Wear and Geometry Response of the Cutting Tool on Machinability of Super Austenitic Stainless Steel

New Observations on High-Speed Machining of Hardened AISI 4340 Steel Using Alumina-Based Ceramic Tools

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

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