Table 2 - uploaded by Hamid Tebassi
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Inconel 718 is among difficult to machine materials because of its abrasiveness and high strength even at high temperature. This alloy is mainly used in aircraft and aerospace industries. Therefore, it is very important to reveal and evaluate cutting tools behavior during machining of this kind of alloy. The experimental study presented in this res...
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... can be shown in Table 2, that the surface roughness Ra was obtained in the range of (0.32-1.64) µm and the material removal rate MRR was obtained, in the range of (800 -7200) mm 3 /min. ...
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Citations
... Behera et al. and Deshpande et al. [8] developed surface roughness prediction models based on cutting parameters, cutting force, sound, and vibration when turning Inconel 718 with cryogenic and non-treated methods. Tebassi et al. [9] modelled (Ra) and (MRR) in turning Inconel 718 (35 HRC) with a ceramic tool (Al2O3+SiC Whisker). ...
Nickel-based alloys, such as Inconel 718, are commonly utilized in various industries including space technology, rocket engines, nuclear reactors, petrochemicals, submarines, power plants, and others. Despite its high mechanical properties and low thermal conductivity, it is challenging to machine. This study aims to model the tool vibration (Vz) and surface roughness (Ra) in relation to various cutting conditions, including cutting speed (Vc), feed rate (f), depth of cut (ap), and tool nose radius (r). Experiments were carried out using dry turning on Inconel 718 with a PVD-coated metal carbide tool, based on the Taguchi L27 design of experiment. The Response Surface Methodology (RSM) and the Analysis of Variance (ANOVA) were applied to develop predictive models.
... Deshpande et al. [11] developed surface roughness prediction models considering cutting parameters, cutting force, sound, and vibration during turning of Inconel 718 with cryogenic and non-treated. Tebassi et al. [12] performed a modeling study of (Ra) and (MRR) when turning Inconel 718 (35 HRC) with the ceramic tool (Al 2 O 3 + SiC Whisker). Thirumalai et al. [13] applied the method (TOPSIS) to optimize the cutting parameters during turning of Inconel 718. ...
Nickel-based alloys such as (Inconel 718) are widely used in the fields of space technology, rocket engines, nuclear reactors, petrochemicals, submarines, power plants, and others. Due to its high mechanical properties, low thermal conductivity, and other characteristics, it is classified as a difficult material to machine. The main objective of this work is to model the tool vibration (Vtng), surface roughness (Ra), cutting force (Fz), power consumption (Pc), and material removal rate (MRR) as a function of different cutting conditions such as cutting speed (Vc), feed rate (f), depth of cut (ap), and tool nose radius (r). Machining tests were performed by dry turning of Inconel 718 with a PVD-coated metal carbide tool, following a Taguchi L27 (3^4) design of experiment. The response surface methodology (RSM) and the analysis of variance (ANOVA) were used to develop prediction models. The desirability function (DF) and the gray relational analysis (GRA) method were used and compared to determine the optimal cutting conditions, in order to minimize the outputs (Vtng, Ra, Fz, and Pc), and maximize the (MRR), according to two study cases encountered in industry.
... Three optimization operations have been carried out in terms of quality, productivity, and qualityproductivity [28][29][30]. The first procedure consists on finding out the minimum of the surface roughness, while the second seeks the maximum of the material remove rate. ...
An experimental investigation is carried out to examine the effects of various cutting parameters on the response criteria when turning EN-AW-1350 aluminum alloy under dry cutting conditions. The experiments related to the analysis of the influence of turning parameters on the surface roughness (Ra) and material removal rate (MRR) were carried out according to the Taguchi L27 orthogonal array (313) approach. The analysis of variance (ANOVA) was applied to characterizing the main elements affecting response parameters. Finally, the desirability function (DP) was applied for a bi-objective optimization of the machining parameters with the objective of achieving a better surface finish (Ra) and a higher productivity (MRR). The results showed that the cutting speed is the most dominant factor affecting Ra followed by the feed rate and the depth of cut. Moreover, the Artificial Neural Network (ANN) approach is found to be more reliable and accurate than its Response Surface methodology (RSM) counterpart in terms of predicting and detecting the non-linearity of the surface roughness and material removal rate mathematical models. ANN provided prediction models with a precision benefit of 8.21% more than those determined by RSM. The latter is easier to use, and provides more information than ANN in terms of the impacts and contributions of the model terms.
... Due to its mechanical and physical properties, such as high strength [3] and creep resistance at high temperatures, it is widely used in the aircraft and aerospace industries, as well as in gas turbines and nuclear reactors [4][5][6][7]. The numerous advantages of this alloy result in it being used in extraordinarily severe conditions, such as those inside the combustion chamber of an aircraft engine [8,9], where heat resistance [10,11] and precision of the manufacturing of parts are extremely important, because they influence the safety of passengers during flight. Due to the price of Inconel and machining difficulties [12,13], it is used only where conditions require it. ...
This paper presents the results of investigation that was performed on shafts composed of Inconel 718. Tests were performed in dry and wet conditions. Cutting parameters, such as feed and depth of cut, were constant. The cutting speed was changed. The investigation was performed for various shaft shapes: cylindrical, taper 30°, taper 45°, and sphere. For that reason, the value of the angle between the machined surface and the cutting edge changed. The lowest values of the roughness parameters, Ra and Rz, were obtained for a larger value of the angle between the machined surface and cutting edge. The investigation showed that cutting speed, machining conditions (dry and wet machining), and the variable angle between the machined surface and the cutting edge influenced the surface roughness. Application of a higher cutting speed resulted in lower roughness values. Lower values of roughness parameters were obtained by wet machining.
... The class of materials which can be machined using abrasive water jet are glass, composites, metals, superalloys, etc. Inconel 625 is nickel-based superalloys which consist of molybdenum, chromium and niobium as main alloying elements [14,15]. Inconel 625 is extensively used for sea-water applications because of its unique properties of high corrosion-fatigue strength and chloride-ion stress-corrosion resistant [16]. ...
Present work attempts to optimise the significant process parameters to enhance the surface properties and productivity during
machining of marine grade Inconel. Taguchi-based Grey analysis
was applied to optimise the parameters of abrasive water jet
machining (AWJM) for minimum surface roughness and higher
material removal rate (MRR). Three most influential input parameters were taken, i.e. standoff distance (SOD), abrasive flow rate
(AFR) and jet traverse speed (JTS). The three levels of input parameters of SOD, AFR and JTS were 1, 2, 3 mm, 300, 350, 400 g/min
and 20, 28, 36 mm/min, respectively. MRR was maximum in case of
machining performed at SOD of 3 mm, AFR of 350 g/min and JTS of
36 mm/min. Machining at an input value of SOD (1 mm), AFR
(400 g/min) and JTS (20 mm/min) exhibited minimum surface
roughness. Grey relation grades (GRG) were obtained and it was
found that SOD was the most significant parameter. To understand
the surface morphology of the machined surface, scanning electron
microscope (SEM) of machined surface was done.
... Two numerical illustrations with sensitivity analyses were given to demonstrate the applicability of the model. Additional works [19][20][21][22][23][24][25][26][27][28][29][30][31][32] investigated the impact of breakdown and rework issues on different aspects of unreliable fabrication-inventory systems. This study aims to help producers obtain competitive advantage by ensuring the timeliness and quality of product deliveries given unreliable machine and limited capacity. ...
... By substituting Eqs. (12), (23), and (25) in Eq. (24), and with extra efforts in derivations, E[TCU(T 1π )] is derived as follows (see Appendix B for details): ...
To reduce the order response time or achieve optimal utilization, manufacturers often include an outsourcing alternative in their production plans. Further, undesirable machine breakdowns and nonconforming items produced during the in-house processes must also be effectively managed/corrected to adhere to the fabrication schedules and meet the desired quality level. This study addresses the aforementioned concerns by examining a hybrid finite production rate (FPR) system featuring: breakdown that follows the Poisson distribution, and rework/repair of all the nonconforming goods produced. A portion of the batch size is outsourced to an external supplier, who guarantees quality but at a higher unit cost than that incurred in-house. A mathematical model is explicitly built to represent the features of the proposed hybrid FPR system. We use the optimization approach and an algorithm to determine the optimal replenishing runtime that minimizes total costs; results are demonstrated through a numerical example and sensitivity analyses. Diverse crucial system information available to assist manufacturers in production planning includes the individual and joint influence of rework, outsourcing, and breakdown on: (a) variable rework cost; (b) the optimal runtime; (c) total system cost; (d) utilization; (e) detail of system's cost elements; and (f) other important system parameters.
... The values of desirability functions (d i ) are between 0 and 1. The Fig. 9 Comparison between numerical and predicted responses value of 0 (zero individual desirability) indicates that one or more responses are outside acceptable limits, and the value of 1 represents the maximum desired performance for the considered factors [42][43][44]. ...
The use of finite element analyses in the design of geotechnical problems is mainly related to the choice of mechanical characteristics (parameters) of soil. Therefore, the optimization of these parameters is considered as the most important step in geotechnics. The main objective of this work is to identify the mechanical parameters based on Mohr–Coulomb model, by conducting pressuremeter tests on synthetic clayey soil, using Taguchi’s experimental design method. The Taguchi L16 orthogonal array is widely adopted as a modeling support for the simulation of the pressuremeter test, using the finite element software Plaxis. Then, a statistical treatment of the obtained results is carried out based on the concept of the analysis of variance and the development of a mathematical model based on the response surface methodology, in order to identify the significant factors of the model and their interactions. Finally, the optimal values of factors might be chosen using result analysis of Taguchi’s experimental design method, combined with the desirability function approach. This study has shown that the proposed approach is an efficient and effective tool for the identification of soil parameters.
... Essas características contribuem para um aumento nas taxas de desgastes de flanco e entalhe, enquanto a presença de carbonetos abrasivos promove um desgaste prematuro da ferramenta de corte por abrasão. Devido às reações químicas que ocorrem em altas temperaturas, tem-se alta taxa de desgaste de cratera por difusão, além de adesão, atrittion e oxidaçãoprincipal responsável pelo desgaste de entalhe (Cantero et al., 2013;D'addona et al., 2017;Lima et al., 2017;Tebassi et al., 2017). ...
... In order to monitor the combined effect of cutting parameters on surface integrity characteristics, different prediction models have been developed for turning Inconel 718 based on artificial neural network (ANN) [31][32][33][34], genetic programming [35], fuzzy inference systems (FIS) [35], and response surface methodology (RSM) [36][37][38][39][40]. The latter is considered as one of the most economical methods for modeling machining-induced surface characteristics (as outputs) when several cutting parameters (as inputs) potentially influence these aspects simultaneously. ...
An original approach is introduced to discriminate the effects of residual stress and surface roughness on turned Inconel 718 alloy parts. This could be used for assessing or studying the performance of these specimens during fatigue tests. This method consists of building surface integrity maps in terms of surface roughness parameters and residual stress in order to select specific turning conditions with adequate machining parameters. The surface integrity maps were generated by using response surface methodology based empirical models and precision turning tests on coupons. The proposed approach was therefore successfully applied to manufacture rotating bending fatigue specimens with the surface axial residual stress varying from − 239 to 446 MPa while keeping the arithmetic average roughness and the surface hoop residual stress constant. The effect of the surface axial residual stress on the fatigue life could thus be discriminated from the other surface integrity characteristics effects. The proposed methodology is independent of the manufacturing process and can be extended to other machining processes such as milling, grinding, and broaching.
... Equation 1 represents the relation between the input parameters (Vc, f and t) and the output function; in our case, it can be VB, Ra or Fz [26]. ...
Mechanical manufacturing companies are required to produce parts with high quality, greater accuracy and high productivity to be competitive. For this purpose, the present work develops predictive models for arithmetic surface finish (Ra), flank wear (VB) and tangential force (Fz). The optimization was based on the desirability function (DF). The machining tests were carried out by hard turning the X210Cr12 hardened steel (56 HRC) using a coated ceramic tool (CC6050), according to the Taguchi L27 experimental plan. ANOVA was employed to determine the influence of cutting parameters (cutting speed -Vc-, feed rate -f- and machining time -t-) on the output parameters (VB, Ra and Fz). Moreover, the (RSM and the ANN) methods were used to model the tech-nological parameters. The desirability function approach was used to determine the optimal machining condi-tions minimizing simultaneously (VB, Ra and Fz). The results show that (VB) is mainly influenced by (Vc) (Cont. %=39.96) followed by (f) (Cont. % =35.36). In addition, it was indicated that (f) and (t) have been found as dominant factors affecting (Ra) with contributions of 31.71% and 23.78% respectively. However, (t) and (f) are the main factors affecting (Fz) with contributions of 75.74% and 22.66% successively. On the other hand, (ANN and RSM) models correlate very well with experimental data. However, ANN approach shows better accuracy and the capability for predicting cutting process parameters than RSM. The optimum machining setting for mul-ti-objective optimization corresponds to (Vc=80 m/min, f=0.08mm/rev and t=4 min).
