Study of face milling of hardened AISI D3 steel with a special design of carbide tools

Siller, Héctor R.; Vila, Carlos; Rodrı́guez, Ciro A.; Abellán, J. V.

doi:10.1007/s00170-007-1309-0

Cited by 47 publications

(27 citation statements)

References 22 publications

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“…However, for high quality machining operations with CBN cutting tools, both equations may not provide a good estimation. The main reason is that additional mechanisms such as vibrations, engagement of the cutting tool or built up edge influence the surface roughness generation when machining at very low feed speeds (Siller et al, 2008) . Furthermore, CBN tools have a different wear process than traditional cutting-tools such as high speed steels, so equation (6) may not be directly applied (Trent and Wright, 2000).…”

Section: Objective Functionsmentioning

confidence: 99%

Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

Abellán-Nebot

2010

IJMMS

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In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user's experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The optimisation procedure presented is composed of two steps: (1) modelling the process variables of interest and (2) optimising a multi-objective function to reach a trade-off among production rate, cutting costs and part accuracy. The first step applies an Adaptive Neuro-fuzzy Inference System (ANFIS) to model the process by extracting rules from experimental data. The second step applies Genetic Algorithms to optimise the multi-objective function which is defined by an overall desirability function approach. The proposed methodology increased the machining performance in 6.1% and improved the understanding of the machining operation through the ANFIS models.

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Section: Objective Functionsmentioning

confidence: 99%

Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

Abellán-Nebot

2010

IJMMS

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“…The traditional method of machining the hardened materials includes rough turning, heat treatment followed by the grinding process. Turning of hardened material eliminates a series of operations required to produce the component and thereby reducing the cycle time and hence resulting in productivity improvement [1,2]. Turning of hardened material is an alternative to conventional grinding process; it is a flexible and economic process for hardened steels [3].…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of Material Removal Rate and Tool wear in turning of hardened AISI52100 steel using Taguchi Technique

H.K¹

2014

IOSRJEN

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-The objective of this paper is to investigate experimentally the optimum process parameters (cutting speed, feed rate and depth of cut) resulting in an optimization of material removal rate (MRR) and tool wear while turning of hardened AISI 52100 steel under dry cutting conditions using Taguchi method. A L9 orthogonal array, signal-to-noise(S/N) ratio and analysis of variances (ANOVA) are applied with the help of Minitab.v.16.2.0 software to study performance characteristics of process parameters with consideration of Material Removal Rate (MRR) and Tool wear. The results obtained from the experiments are changed into signal-to-noise ratio(S/N) ratio and used to optimize the value of MRR and tool wear The ANOVA is performed to identify the importance of parameters. The conclusions arrived at are considerably discussed at the end.

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“…The analysis results showed that finish hard milling can be an alternative to grinding process in the die and mold industry. Siller et al [9] studied the impact of a special carbide tool design on the process viability of the face milling of hardened AISI D3 steel in terms of surface quality and tool life. It was found that surface roughness Ra values from 0.1 to 0.3 μm can be obtained in the workpiece with an acceptable level of tool life.…”

Section: Introductionmentioning

confidence: 99%

Surface roughness and chip formation in high-speed face milling AISI H13 steel

Cui

Zhao

Jia

et al. 2011

Int J Adv Manuf Technol

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Many previous researches on high-speed machining have been conducted to pursue high machining efficiency and accuracy. In the present study, the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra high-speed face milling of AISI H13 steel (46-47 HRC) are experimentally investigated. It is found that the ultra high cutting speed of 1,400 m/min can be considered as a critical value, at which relatively low mechanical load, good surface finish, and high machining efficiency are expected to arise at the same time. When the cutting speed adopted is below 1,400 m/min, the contribution order of the cutting parameters for surface roughness Ra is axial depth of cut, cutting speed, and feed rate. As the cutting speed surpasses 1,400 m/min, the order is cutting speed, feed rate, and axial depth of cut. The developing trend of the surface roughness obtained at different cutting speeds can be estimated by means of observing the variation of the chip shape and chip color. It is concluded that when low feed rate, low axial depth of cut, and cutting speed below 1,400 m/min are adopted, surface roughness Ra of the whole machined surface remains below 0.3 μm, while cutting speed above 1,400 m/min should be avoided even if the feed rate and axial depth of cut are low.

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Study of face milling of hardened AISI D3 steel with a special design of carbide tools

Cited by 47 publications

References 22 publications

Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

Cutting parameters optimisation in milling: expert machinist knowledge vs. soft computing methods

Experimental investigation of Material Removal Rate and Tool wear in turning of hardened AISI52100 steel using Taguchi Technique

Surface roughness and chip formation in high-speed face milling AISI H13 steel

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