Investigation of the influential parameters of machining of AISI 304 stainless steel

Mahdavinejad, Ramezanali; Saeedy, Sina

doi:10.1007/s12046-011-0055-z

Cited by 36 publications

(20 citation statements)

References 14 publications

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“…Further, AlCrN/TiAlN coated cutting tools gave lower cutting forces due to the lower friction coefficient of the coating. Mahdavinejad and Saeedy (2011) tried to investigate the influence of cutting conditions on machining of austenitic stainless steel with cutting fluid and in dry conditions. Authors have noted that cutting speed is the main parameter which affect flank wear and as cutting speed increases to 175 m/min, the flank wear decreases.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool

Berkani¹,

Yallese²,

Boulanouar³

et al. 2015

10.5267/j.ijiec

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The present research work investigated the machining of AISI304 austenitic stainless steel in terms of machining force evolution, power consumption, specific cutting force and surface roughness where a factorial experiment design and analysis of variance technique were used and several factors were evaluated for their effects on each level. The case of dry turning process was studied based on design of experiments in order to obtain empirical equations characterizing material machinability according to cutting conditions such as cutting speed, feed rate and depth of cut and the latter ones were put in relationship with the machining output variables (Ra, Fc, Kc and Pc) through the response surface methodology (RSM). Results revealed that feed rate was the most preponderant factor affecting surface roughness (71.04%). However, the depth of cut affects considerably cutting force and cutting power by (60.74% and 67.11%), respectively. In addition, the specific cutting force was found affected significantly by cutting speed with a contribution of 41.43%. The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of machining parameters (104.54 m/min, 0.08 mm/rev and 0.295 mm).

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Section: Introductionmentioning

confidence: 99%

Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool

Berkani¹,

Yallese²,

Boulanouar³

et al. 2015

10.5267/j.ijiec

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show abstract

“…Wang et al [8] was used simulation annealing algorithm to optimize the parameters of support vector machine. Mahdavinejad et al [9] optimized tuning parameters of AISI 304 stainless steel using Design of Experiment and also conducted an ANOVA test to determine the effect of each parameter on surface roughness and tool wear. Results showed that cutting speed has main influence on the flank wear and feed rate on surface roughness.…”

Section: Relational Analysismentioning

confidence: 99%

Multi Objective Optimisation of Turning Process Parameters on EN 8 Steel using Grey Relational Analysis

Sridhar¹,

Venkateswarlu²

2014

IJEM

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The objective of the present paper is to optimize the machining parameters for turning of EN8steel on lathe machine using a combination of Taguchi and Grey Relational Analysis to yield minimum cutting forces and surface roughness. The process parameters such as rotational speed, feed, depth of cut and cutting fluid have been selected. In this study, the experiments were carried out as per Taguchi experimental design and L 9 orthogonal array was used. Analysis of variance (ANOVA) was also used to find out the most influence of processing parameters on the responses. The regression equations were also established between the process parameters and responses. The results indicate that the depth of cut is the most significant factor affecting the cutting force and surface roughness followed by a feed, speed and cutting fluid.

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“…304 stainless steel is mainly used for Heat Exchangers, bushings, springs, bearings, gears, and shafts. Despite the increased usage of 304 stainless steel, they have relatively poor machinability characteristics compared to other metals, because of their poor thermal conductivity [4].…”

Section: Introductionmentioning

confidence: 99%

Effects of High pressure coolant on machining temperature and machinability of AISI 304 stainless steel

Veldhuis¹

2018

Progress in Canadian Mechanical Engineering

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Effective zones of high pressure coolant (HPC) have been identified in turning performing a wide range of machining operations on 304 stainless steel using uncoated carbide inserts. The effects of triple coolant jets on tool-chip interface temperature were studied. In addition, their performance are evaluated in terms of machining parameters by comparing with those of conventional coolant and dry cut. Chipping and catastrophic failure are the dominant factors of insert rejection for dry cut and conventional coolant, whereas progressive flank wear is observed for HPC within its effective zones at lower feed rate and higher cutting speed. It is found that the cutting force is reduced, surface finish is improved, chip width is narrowed and cooling effect is better with the use of HPC. The enhanced heat dissipation by triple jets is accredited as the primary reason for the reduction of cutting forces, surface roughness and tool wear.

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Investigation of the influential parameters of machining of AISI 304 stainless steel

Cited by 36 publications

References 14 publications

Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool

Statistical analysis of AISI304 austenitic stainless steel machining using Ti(C, N)/Al2O3/TiN CVD coated carbide tool

Multi Objective Optimisation of Turning Process Parameters on EN 8 Steel using Grey Relational Analysis

Effects of High pressure coolant on machining temperature and machinability of AISI 304 stainless steel

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