Safian Sharif

Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel

The performance of a multilayer tungsten carbide tool was described using response surface methodology (RSM) when turning AISI 1045 steel. Cutting tests were performed with constant depth of cut and under dry cutting conditions. The factors investigated were cutting speed, feed and the side cutting edge angle (SCEA) of the cutting edge. The main cutting force, i.e. the tangential force and surface roughness were the response variables investigated. The experimental plan was based on the face centred, central composite design (CCD). The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The feed is the most significant factor that influences the surface roughness and the tangential force. However, there are other factors that provide secondary contributions to the performance indicators. In the case of surface roughness, the SCEA2 and the interaction of feed and SCEA provides these contributions whilst for tangential force, the SCEA2, the interaction of feed and SCEA; and the cutting speed provides them.

Cutting performance and wear characteristics of PVD coated and uncoated carbide tools in face milling Inconel 718 aerospace alloy

In this paper, cutting performance and failure characteristics of two PVD TiN coated and an uncoated tungsten carbide grades with identical geometry are presented. Face-milling tests of Inconel 718 superalloy were performed to investigate the effect of cutting speed and feed rate on tools performance under wet conditions. Tools were thoroughly examined under SEM at two stages in order to reveal the failure modes and wear mechanisms. These stages were after cutting for 5 s and when the tool failed. It was noted that the coating resulted in a marginal improvement, as it was delaminated by adhering workpiece material at the beginning of the cut, impeding the performance of the tool for the rest of the experiment. A combination of progressive chipping and flank wear was the general mode of tool failure, former being dominant at high speeds and the latter at the low speed region. Results showed that uncoated tool performed better than coated tools at low cutting speeds while coated tools gave slightly better performance as the speed was raised.

Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA

In this study, Simulated Annealing (SA) and Genetic Algorithm (GA) soft computing techniques are integrated to estimate optimal process parameters that lead to a minimum value of machining performance. Two integration systems are proposed, labeled as integrated SA-GA-type1 and integrated SA-GA-type2. The approaches proposed in this study involve six modules, which are experimental data, regression modeling, SA optimization, GA optimization, integrated SA-GA-type1 optimization, and integrated SA-GA-type2 optimization. The objectives of the proposed integrated SA-GA-type1 and integrated SA-GA-type2 are to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, to estimate the optimal process parameters values that has to be within the range of the minimum and maximum process parameter values of experimental design, and to estimate the optimal solution of process parameters with a small number of iteration compared to the optimal solution of process parameters with SA and GA optimization. The process parameters and machining performance considered in this work deal with the real experimental data in the abrasive waterjet machining (AWJ) process. The results of this study showed that both of the proposed integration systems managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.

Hard Machining of Stainless Steel Using Wiper Coated Carbide: Tool Life and Surface Integrity

Carbide cutting tools, which are inexpensive and widely used by machine shops, are alternatives for performing hard machining, yet limitations due to their low strength requires performance evaluation as well as appropriate selection of cutting parameters. In this study, a carbide tool with TiAlN coating with wiper geometry at the cutting edge is proposed for performing mild range of hard machining of martensitic stainless steel (48 HRC). The tools performance was evaluated based on its tool life and the resulting surface finish when hard machining at various cutting speeds and feeds and at constant depth of cut without using cutting fluid (dry machining). Response surface methodology was used to quantify the effect of cutting speed and feed to the tool life and proposing the optimum cutting parameters. Further observation was made on the worn tool, the machined surface, and the generated chip to observe the process. Results showed that the wiper coated carbide tool are capable of performing particular hard machining.

Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining

In this study, two computational approaches, Genetic Algorithm and Simulated Annealing, are applied to search for a set of optimal process parameters value that leads to the minimum value of machining performance. The objectives of the applied techniques are: (1) to estimate the minimum value of the machining performance compared to the machining performance value of the experimental data and regression modeling, (2) to estimate the optimal process parameters values that has to be within the range of the minimum and maximum coded values for process parameters of experimental design that are used for experimental trial and (3) to evaluate the number of iteration generated by the computational approaches that lead to the minimum value of machining performance. Set of the machining process parameters and machining performance considered in this work deal with the real experimental data of the non-conventional machining operation, abrasive waterjet. The results of this study showed that both of the computational approaches managed to estimate the optimal process parameters, leading to the minimum value of machining performance when compared to the result of real experimental data.

SIMULATED ANNEALING TO ESTIMATE THE OPTIMAL CUTTING CONDITIONS FOR MINIMIZING SURFACE ROUGHNESS IN END MILLING Ti-6Al-4V

This study presents the estimation of the optimal effect of the radial rake angle of the tool, combined with cutting speed and feed in influencing the surface roughness result. Studies on optimization of cutting conditions for surface roughness in end milling involving radial rake angle are still lacking. Therefore, considering the radial rake angle, this study applied simulated annealing in determining the solution of the cutting conditions to obtain the minimum surface roughness when end milling Ti-6Al-4V. Considering a set of experimental machining data, the regression model is developed. The best regression model was considered to formulate the fitness function of the simulated annealing. It was recommended that the cutting conditions should be set at highest cutting speed, lowest feed and highest radial rake angle in order to achieve the minimum surface roughness of 0.1385 µm. Subsequently, it was found that by using simulated annealing, the minimum surface roughness was much lower than the experimental sample data, regression modelling and response surface methodology technique by about 27%, 26% and 50%, respectively.

Robust optimization of ANFIS based on a new modified GA

Adaptive Network-based Fuzzy Inference Systems (ANFIS) is one of the most well-known predictions modeling technique utilized to find the superlative relationship between input and output parameters in different processes. Training the adaptive modeling parameters in ANFIS is still a challengeable problem which has been recently considered by researchers. Hybridizing of a robust optimization algorithm with ANFIS as its training algorithm provides a scope to improve the effectiveness of membership functions and fuzzy rules in the model. In this paper, a new Modified Genetic Algorithm (MGA) by using a new type of population is proposed to optimize the modeling parameters for membership functions and fuzzy rules in ANFIS. As well, a case study on a machining process is considered to illustrate the robustness of the proposed training technique in prediction of machining performances. The prediction results have demonstrated the superiority of the presented hybrid ANFIS-MGA in term of prediction accuracy (with 97.74%) over the other techniques such as hybridization of ANFIS with Genetic Algorithm (GA), Taguchi-GA, Hybrid Learning algorithm (HL), Leave-One-Out Cross-Validation (LOO-CV), Particle Swarm Optimization (PSO) and Grid Partition method (GP), as well as RBFN and basic Grid Partition Method (GPM). In addition, an attempt is done to specify the effectiveness of different improvement rates on the prediction result and measuring the number of function evaluations required. The comparison result reveals that MGA with improvement rate 0.8 raises the convergence speed and accuracy of the prediction results compared to GA.

Integrated ANN–GA for estimating the minimum value for machining performance

In this study, we proposed a new approach in estimating a minimum value of machining performance. In this approach, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated in order to search for a set of optimal cutting condition points that leads to the minimum value of machining performance. Three machining cutting conditions for end milling operation that were considered in this study are speed (v), feed (f) and radial rake angle (γ). The considered machining performance is surface roughness (R a). The minimum R a value at the optimal v, f and γ points was expected from this approach. Using the proposed approach, named integrated ANN–GA, this study has proven that R a can be estimated to be 0.139 µm, at the optimal cutting conditions of f = 167.029 m/min, v = 0.025 mm/tooth and γ = 14.769°. Consequently, the ANN–GA integration system has reduced the R a value at about 26.8%, 25.7%, 26.1% and 49.8%, compared to the experimental, regression, ANN and response surface method results, respectively. Compared to the conventional GA result, it was also found that integrated ANN–GA reduced the mean R a value and the number of iterations in searching for the optimal result at about 0.61% and 23.9%, respectively.

Cutting force and surface roughness characterization in cryogenic high-speed end milling of Ti-6Al-4V ELI

This study investigates the cutting forces induced during high-speed end milling of titanium alloy (Ti–6Al-4V ELI) as well as the surface quality of the milled surfaces. The high-speed machining was performed using carbide tool of coated and uncoated types at three cutting speeds of 200, 250, and 300 mm/min and two feed rates of 0.03 and 0.06 mm/tooth. Surface integrity was characterized in terms of surface roughness (Ra) and morphology. Cutting speed was found to be inversely proportional to the resultant cutting force at any cutting conditions. Cutting force in the X direction displayed higher sensitivity against cutting conditions. The results showed that feed rate is proportional to cutting force in X and Y directions regardless of tool type. Under the fixed feed rate condition, cutting force decreased at higher cutting speed for both tools. It was also found that uncoated tool induces less cutting force compared to coated one. High-speed end milling using uncoated tool provided better surface finish than using a coated carbide tool, especially at lower cutting conditions. However when coated carbide tool was used, surface roughness improved significantly with the increase in cutting speed. In contrast, almost opposite phenomenon was observed when uncoated tool was used.

Integration of simulated annealing and genetic algorithm to estimate optimal solutions for minimising surface roughness in end milling Ti-6AL-4V

In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Twointegration systems are proposed; integrated SA–GA-type1 and integrated SA–GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value ofmachining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA–GA.