Optimization of surface finish in end milling Inconel 718

Alauddin, M.; Baradie, M.A. El; Hashmi, M.S.J.

doi:10.1016/0924-0136(95)01820-4

Cited by 94 publications

(60 citation statements)

References 5 publications

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“…In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity as it is mentioned by Lynch [1]. Certainly, this type of alloy is difficult to machine for the following reasons as it is presented by Alauddin [2]: High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines and others.…”

Section: Introductionmentioning

confidence: 99%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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IntroductionThe Inconel 718 is one of the most important materials used in modern industries. In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity as it is mentioned by Lynch [1]. Certainly, this type of alloy is difficult to machine for the following reasons as it is presented by Alauddin [2]: High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines and others.In order to respond to the requirements of those applications, it is very important to forecasting surface roughness and cutting force. Consequently, it is necessary to search the best modeling approach of these output parameters. To obtain this objective, several approaches can be used as well as response surface methodology (RSM) and artificial neural network (ANN). Response surface methodology (RSM) is considered as a quick and useful procedure for the investigation and optimization of complex processes as well as modeling machining output parameters. Certainly, Davoodi and Eskandari [3] found that response surface methodology represents a better approach to predict tool life and productivity when turning of N-155 iron-nickel-base superalloy. Shihab et al. [4], investigated cutting temperature during hard turning of AISI 52100 alloy steel using multilayer coated carbide insert; they concluded that the developed RSM model is able to predict cutting temperature for different combination of input parameters very close to experimental values. Arokiadass et al. [5], used response surface methodology to modeling tool flank wear when end milling of LM 25 Al/SiCp with carbide tool. They concluded that the developed relationship can be effectively used to predict flank wear of carbide tool at a confidence level of 95%. Sahoo et al. [6], confirmed this conclusion when studying the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment.

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Section: Introductionmentioning

confidence: 99%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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“…1. Czynniki wpływające na kształtowanie mikrostruktury geometrycznej powierzchni wg [7] Autorzy pracy [2] opracowali zestawienie czynników wpływających na SGP uwzględniające czternaście zmiennych, które zostały przyporządkowane do trzech grup sklasyfikowanych jako czynniki wejściowe, które powiązane są z obrabiarką, materiałem obrabianym oraz narzędziem. Do grupy czynników związanych z obrabiarką autorzy zakwalifikowali parametry skrawania, sposób chłodzenia zastosowany podczas obróbki oraz sztywność obrabiarki, która będzie wpływała na drgania podczas procesu frezowania.…”

Section: Wstępunclassified

The analysis of factors impacting the geometrical structure of machined surfaces

Nowakowski

Miko

2015

Mechanik

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ANALIZA CZYNNIKÓW WPŁYWAJĄCYCH NA STRUKTURĘ GEOMETRYCZNĄ POWIERZCHNI PODDANYCH OBRÓBCE SKRAWANIEM StreszczenieArtykuł zawiera przegląd odnalezionych w literaturze zestawień czynników mających wpływ na proces generowania struktury geometrycznej powierzchni poddanych obróbce skrawaniem. Na podstawie przeprowadzonej analizy literatury i własnych doświadczeń autorzy opracowali nowe zestawienie czynników, które oddziałują na proces kształtowania struktury geometrycznej powierzchni. Ta klasyfikacja uwzględnia 32 czynniki podzielone na pięć grup związanych z obrabiarką, narzędziem i właściwościami materiału obrabianego, ze zjawiskami obróbkowymi towarzyszącymi procesowi skrawania oraz z czynnikiem ludzkim. Słowa kluczowe: obróbka skrawaniem, chropowatość powierzchni, parametry procesu skrawania THE ANALYSIS OF FACTORS IMPACTING THE GEOMETRICAL STRUCTURE OF MACHINED SURFACES AbstractThe article presents an overview of comparisons of factors impacting the process of generation of the geometrical structure of machined surfaces, as found in the literature. On the basis of the conducted analysis of literature and own experiences, the authors have developed a new comparison of factors impacting the process of forming of the surface geometrical structure. This classification includes 32 factors, divided into five groups related to the machine tool, the tool and the properties of the workpiece, with the machining phenomena accompanying the cutting process, and with the human factor.

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“…In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity (Lynch, 1989). Generally, this type of alloy is difficult to machine for the following reasons (Alauddin et al, 1996): High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718

Tebassi¹,

Yallese²,

Khettabi³

et al. 2016

10.5267/j.ijiec

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Nickel based super alloys are excellent for several applications and mainly in structural components submitted to high temperatures owing to their high strength to weight ratio, good corrosion resistance and metallurgical stability such as in cases of jet engine and gas turbine components. The current work presents the experimental investigations of the cutting parameters effects (cutting speed, depth of cut and feed rate) on the surface roughness, cutting force components, productivity and power consumption during dry conditions in straight turning using coated carbide tool. The mathematical models for output parameters have been developed using Box-Behnken design with 15 runs and Box-Cox transformation was used for improving normality. The results of the analysis have shown that the surface finish was statistically sensitive to the feed rate and cutting speed with the contribution of 43.58% and 23.85% respectively, while depth of cut had the greatest effect on the evolution of cutting force components with the contribution of 79.87% for feed force, 66.92% for radial force and 66.26% for tangential force. Multi-objective optimization procedure allowed minimizing roughness Ra, cutting forces and power consumption and maximizing material removal rate using desirability approach.

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Optimization of surface finish in end milling Inconel 718

Cited by 94 publications

References 5 publications

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

The analysis of factors impacting the geometrical structure of machined surfaces

Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718

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