Azlan Mohd Zain

Evolutionary techniques in optimizing machining parameters

Highlights? Several evolutionary techniques are reviewed to optimize machining parameter. ? It was found that genetic algorithm was widely applied by researchers. ? The most employed machining operation was multipass-turning. ? The most considered machining performance was surface roughness. In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.

Multi-objective hybrid evolutionary algorithms for radial basis function neural network design

This paper presents new multi-objective evolutionary hybrid algorithms for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithms are memetic Pareto particle swarm optimization based RBFN (MPPSON), Memetic Elitist Pareto non dominated sorting genetic algorithm based RBFN (MEPGAN) and Memetic Elitist Pareto non dominated sorting differential evolution based RBFN (MEPDEN). The proposed methods integrate accuracy and structure of RBFN simultaneously. These algorithms are implemented on two-class and multiclass pattern classification problems with one complex real problem. The results reveal that the proposed methods are viable, and provide an effective means to solve multi-objective RBFNs with good generalization ability and simple network structure. The accuracy and complexity of the network obtained by the proposed algorithms are compared through statistical tests. This study shows that the proposed methods obtain RBFNs with an appropriate balance between accuracy and simplicity.

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.

Fuzzy logic for modeling machining process: a review

The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process.

Cuckoo Search Algorithm for Optimization Problems: A Literature Review and its Applications

Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This article describes an overview of CS, which is inspired by the life of a bird family, as well as an overview of CS applications in various categories for solving optimization problems. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. The categories reviewed are Engineering, Pattern Recognition, Job Scheduling, Networking, Object-Oriented Software (Software Testing), and Data Fusion in Wireless Sensor Networks. From the reviewed literature, CS is mostly applied in the engineering area for solving optimization problems. The objective of this study is to provide an overview and to summarize the review of applications of the CS.

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.

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.