Effect of Cryogenic Treatment on Tool Life of HSS Tool (S400) and Surface Finish of the Material in Turning of SS304

Pradeep, A.V.; Prasad, Somuri V.; Suryam, L.V.; Rao, Y Kesava; Kesava, D.

doi:10.1088/1757-899x/149/1/012010

Cited by 4 publications

(2 citation statements)

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“…Ra decreases with an increase in cutting speed and decrease in feed rate and depth of cut. It is observed that the deep cryogenically treated tool obtained less contribution percentage than the untreated tool because of less distortion on the cutting edge due to enhanced hardness through the deep cryogenic treatment [38]. From the ANOVA, it is clear that the cutting speed is the most significant factor followed by feed rate and depth of cut.…”

Section: Analysis Of Variance For Surface Roughnessmentioning

confidence: 95%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Raja¹,

Malayalamurthim²,

Sakthivel³

2019

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This work depicts the effects of deep cryogenically treated high-speed steel on machining. In recent research, cryogenic treatment has been acknowledged for improving the life or performance of tool materials. Hence, tool materials such as the molybdenum-based high-speed tool steel are frequently used in the industry at present. Therefore, it is necessary to observe the tool performance in machining; the present research used medium carbon steel (AISI 1045) under dry turning based on the L 9 orthogonal array. The effect of untreated and deep cryogenically treated tools on the turning of medium carbon steel is analyzed using the multi-input-multi-output fuzzy inference system with the Taguchi approach. The cutting speed, feed rate and depth of cut were the selected process parameters with an effect on surface roughness and the cutting tool edge temperature was also observed. The results reveal that surface roughness decreases and cutting tool edge temperature increases on increasing the cutting speed. This is followed by the feed rate and depth of cut. The deep cryogenically treated tool caused a reduction in surface roughness of about 11% while the cutting tool edge temperature reduction was about 23.76% higher than for an untreated tool. It was thus proved that the deep cryogenically treated tool achieved better performance on selected levels of the turning parameters.

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Section: Analysis Of Variance For Surface Roughnessmentioning

confidence: 95%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Raja¹,

Malayalamurthim²,

Sakthivel³

2019

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“…Despite of its advantages, high hardness of the material makes it difficult to machine. [1] Hence it is dire necessary to find out suitable parameters during machining.Several parameters like tool nose radius, feed rate, depth of cut, cutting velocity etc., determine the performance of machining. In the recent years, lot of attention is paid towards the influence of the above parameters over Output responses like cutting force, Material Removal Rate (MRR), Surface finish, Tool wear etc [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimization for Machining Parameters in Turning of AISI 52100 Steel for Manufacturing Services-Genetic Algorithm Method

Pradeep,

Suryam,

Prasad

et al. 2020

IJITEE

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Because of its high wear and corrosion resistance, AISI 52100 steel is mostly used in automobile and aerospace applications. It is a high strength chromium steel with hardness usually greater than 45HRC.The prime objective of this study is to identify the influential machining parameters affecting metal removal rate (MRR) and cutting forces. During turning of AISI52100 steels, a central composite design (CCD) through response surface methodology (RSM) was employed to generate a model for predicting the MRR and cutting forces. The significant parameters and their contribution percentage were identified using analysis of variance (ANOVA). Furthermore, the affect of machining parameters in the MRR and cutting forces were examined and validated for identifying the accuracy of the predicted model. To conclude, the optimum values of MRR and cutting force along with the optimum parameters were ascertained using genetic algorithm (GA).

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