scholarly journals Multivariate empirical modeling of interaction effects of machining var-iables on surface roughness in dry hard turning of AISI 4140 steel with coated CBN insert using Taguchi design

Mechanika ◽  
2017 ◽  
Vol 23 (5) ◽  
Author(s):  
Ahmet Cakan ◽  
Fatih Evrendilek
Measurement ◽  
2013 ◽  
Vol 46 (9) ◽  
pp. 3041-3056 ◽  
Author(s):  
Mohamed Elbah ◽  
Mohamed Athmane Yallese ◽  
Hamdi Aouici ◽  
Tarek Mabrouki ◽  
Jean-François Rigal

2017 ◽  
Vol 15 (1-2) ◽  
Author(s):  
Santosh V. Bhaskar ◽  
Hari N. Kudal

<p>Components of forming tool dies such as draw ring, ejector pin use AISI 4140 as material for their manufacturing. The integrity of the die cutting tools is essential to achieve adequate product quality. In present study, the influence of plasma nitriding (PN) on the wear behav-iour of AISI 4140 steel was investigated. Full factorial experimental design technique was used to study the main effects and the interaction effects between operational parameters and the response variable. The control factors at their two levels (-1 and +1) were: applied load (4.905N and 14.715N), sliding speed (3.14 m/s and 5.23 m/s), and sliding distance (500m and 1000m).The parameters were coded as A, B, and C, consecutively, and were investigated at two levels (-1 and +1). Response selected was Wear Volume Loss (WVL). The effects of in-dividual variables and their interaction effects for dependent variables, namely, WVL were determined. The process of selecting significant factors, based on statistical tools, is illustrat-ed. Analysis of Variance (ANOVA) was performed to know the impact of individual factors on the WVL. Untreated and PN treated AISI 4140 specimens were investigated using field emission Scanning Electron Microscope (SEM) equipped with Energy Dispersive X-ray (EDX) analyzer. Finally diagnostics tools were used to check adequacy of the model in terms of assumptions of ANOVA. ‘Design Expert-7’ and ‘Minitab 17’ softwares were used in the study. Results of statistical analysis indicate that the most effective parameters in the WVL were load and sliding speed. The interaction between load and sliding speed was the most influencing interaction. Results of regression analysis indicate regression coefficient (R2) to be above 90% which suggests good predictability of the model. ‘Predicted-R2’ and ‘Adjusted-R2’, found to be in good agreement with R2, for both the materials under investigation. More-over, results of SEM microscopy suggest PN to be an effective technique to reduce wear.</p>


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