Tool Wear and Surface Roughness in Machining AISI D2 Tool Steel

Kundor, Nik Faizu; Awang, Normah; Berahim, Nawi

doi:10.17485/ijst/2016/v9i18/88731

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…When measured tool life was indicated as y i = logT i with variables of cutting speeds being x i = log v ci , then the determined linear regression of a single parameter is expressed in the form [9]:…”

Section: Experimental Procedures and Used Methodsmentioning

confidence: 99%

Experimental Investigation into Wear and Tool Life of Milling Cutter PVD Coated Carbide Inserts While Armox 500 Steel Hard Milling

et al. 2018

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The goal of the submitted paper is to evaluate the dependence of tool life T = f(v c) in hard rough face milling process of the Armox 500 steel through the PVD coated cemented carbide cutting material with SNHF 1204EN-SR-M1 tool insert geometry. Practical tests of choice material were performed in the milling machine tool with vertical axis. The effect of applied cutting conditions on the surface finish, tool life, wear of cutting tool, such as flank wear and surface structure of applied cutting inserts, was investigated during the performed experiments. The goal of this article is to research the impact of various cutting speeds values during the process of hard face rough milling. Experiments are carried out with an aim to study tool life, surface finish and flank wear prediction on the PVD coated cemented carbide cutting material in hard milling processes. This has been observed through the REM and mathematical method of least squares for this characterization. The presented approach and acquired results will prove themselves helpful in understanding the machinability of Armox 500 steel during the hard milling process.

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Section: Experimental Procedures and Used Methodsmentioning

confidence: 99%

Experimental Investigation into Wear and Tool Life of Milling Cutter PVD Coated Carbide Inserts While Armox 500 Steel Hard Milling

et al. 2018

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“…This approach can be applied for specific cases in which only several factors were considered in the investigation of their influence on the tool wear and surface roughness [7][8][9]. Therefore, many studies focused on experimental modeling methods with a limited amount of input parameters [15][16][17][18][19]. In the experimental modelling method, many approaches were applied to model the tool wear and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

“…In which, several studies were performed to model the tool wear and surface roughness based on the cutting time or the number of machining strokes. The results from these studies showed that depending on the machining time or the number of machining strokes, the tool wear and surface roughness also changed [16,17,20].…”

Section: Introductionmentioning

confidence: 99%

“…The response surface method was applied to analyze the effect of the technological parameters on the tool wear and surface roughness. Besides, using this method, the tool wear and surface roughness were also modeled [15][16][17][18][19]. Taguchi method was used to design the experimental matrix, to investigate the influence of technological parameters and cutting conditions on the tool wear and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

et al. 2021

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In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

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“…This study was carried out to verify the change of machining surface roughness due to increasing tool wear. [13,14]. The neural approach was applied to investigate the influence of cutting condition of tool wear and average surface roughness in turning process under minimum quantity lubrication (MQL) environment.…”

Section: Introductionmentioning

confidence: 99%