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).
The wear of cutting tools remains a major obstacle. The effects of wear are not only antagonistic at the lifespan and productivity, but also harmful with the surface quality. The present work deals with some machinability studies on flank wear, surface roughness, and lifespan in finish turning of AISI 304 stainless steel using multilayer Ti(C,N)/Al2O3/TiN coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), and combined effects of two cutting parameters, namely cutting speed and feed rate on lifespan (T), are explored employing the analysis of variance (ANOVA). The relationship between the variables and the technological parameters is determined using a quadratic regression model and optimal cutting conditions for each performance level are established through desirability function approach (DFA) optimization. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. In addition, it is indicated that the cutting time is the dominant factor affecting workpiece surface roughness followed by feed rate, while lifespan is influenced by cutting speed. The optimum level of input parameters for composite desirability was found Vc1-f1-t1 for VB, Ra and Vc1-f1 for T, with a maximum percentage of error 6.38%.
This experimental study is an attempt to model technological parameters such as cutting forces and surface roughness in hard turning of X38CrMoV5-1 hot work tool steel hardened to 50 HRC. This steel is free from tungsten on Cr-Mo-V basis, insensitive to temperature changes and has a high wear resistance. It is employed for the manufacture of helicopter rotor blades and forging dies. The workpiece is machined by a whisker ceramic tool (insert CC670 of chemical composition 75%Al 2 O 3 +25%SiC) under dry conditions. Based on 3 3 full factorial design, a total of 27 tests are carried out. The range of each parameter is set at three different levels, namely low, medium and high. Mathematical models were deduced by applying analysis of variance (ANOVA) and through factor interaction graphs in the response surface methodology (RSM) in order to express the influence degree of each cutting regime element on cutting force components and surface roughness criteria. The results indicate that the depth of cut is the dominant factor affecting cutting force components. The feed rate influences tangential cutting force more than radial and axial forces. The cutting speed affects radial force more than tangential and axial forces. The results also reveal that feed rate is the dominant factor affecting surface roughness, followed by cutting speed. As for the depth of cut, its effect is not very important. These mathematical models would be helpful in selecting cutting variables for optimization of hard cutting process.
In the current paper, cutting parameters during turning of AISI 304 Austenitic Stainless Steel are studied and optimized using Response Surface Methodology (RSM) and the desirability approach. The cutting tool inserts used in this work were the CVD coated carbide. The cutting speed (vc), the feed rate (f) and the depth of cut (ap) were the main machining parameters considered in this study. The effects of these parameters on the surface roughness (Ra), cutting force (Fc), the specific cutting force (Kc), cutting power (Pc) and the Material Removal Rate (MRR) were analyzed by ANOVA analysis.The results showed that f is the most important parameter that influences Ra with a contribution of 89.69 %, while ap was identified as the most significant parameter (46.46%) influence the Fc followed by f (39.04%). Kc is more influenced by f (38.47%) followed by ap (16.43%) and Vc (7.89%). However, Pc is more influenced by Vc (39.32%) followed by ap (27.50%) and f (23.18%).The Quadratic mathematical models, obtained by the RSM, presenting the evolution of Ra, Fc, Kc and Pc based on (vc, f, and ap) were presented. A comparison between experimental and predicted values presents good agreements with the models found.Optimization of the machining parameters to achieve the maximum MRR and better Ra was carried out by a desirability function. The results showed that the optimal parameters for maximal MRR and best Ra were found as (vc = 350 m/min, f = 0.088 mm/rev, and ap = 0.9 mm).
The present paper investigates the cutting parameters pertaining to the turning of X2CrNi18-09 austenitic stainless steel that are studied and optimized using both RSM and desirability approaches. The cutting tool inserts used are the CVD coated carbide. The cutting speed, the feed rate and the depth of cut represent the main machining parameters considered. Their influence on the surface roughness and the cutting force are further investigated using the ANOVA method. The results obtained lead to conclude that the feed rate is the surface roughness highest influencing parameter with a contribution of 89.69%.The depth of cut and the feed rate are further identified as the most important parameters affecting the cutting force with contributions of 46.46% and 39.04% respectively. The quadratic mathematical models presenting the progression of the surface roughness and the cutting force and based on the machining parameters considered (cutting speed, feed rate and depth of cut) were obtained through the application of the RSM method. They are presented and compared to the experimental results. Good agreement is found between the two sections of the investigation. Furthermore, the flank wear of the CVD-coated carbide tool (GC2015) is found to increase with both cutting speed and cutting time. A higher tool life represented by t=44min is observed at cutting speed, feed rate and depth of cut of 280m/min,0.08mm/rev and 0.2mm respectively. Moreover and at low cutting speeds, the formation of micro weld is noticed and leads to an alteration of the surface roughness of the work piece. Finally, optimizing the machining parameters with the objective of achieving an improved surface roughness was accomplished through the application of the Desirability Function approach. This enabled to finding out the optimal parameters for maximal material removal rate and best surface quality for a cutting speed of 350m/min, a feed rate of 0.088 mm/rev and a depth of cut of 0.9mm.
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