Mehmet Çunkaş

Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method

Research highlights? The surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut by full factorial experimental design. ? Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. ? The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model.

Color image segmentation based on multiobjective artificial bee colony optimization

A new color image segmentation method based on Improved Bee Colony Algorithm for Multi-Objective Optimization (IBMO) is presented.The proposed method is applied on several natural images obtained from Berkeley segmentation database.The obtained results are compared the ones obtained from Fuzzy C-means which is one of the most popular methods used in image segmentation, Nondominated Sorting Genetic Algorithm, and Nondominated Sorted Particle Swarm Optimization.The comparative results of performance metrics show that the adapted version of IBMO is a promising method for color image segmentation. This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.

Efficiency determination of induction motors using multi-objective evolutionary algorithms

This paper introduces a method based on multi-objective evolutionary algorithms for the determination of in-service induction motor efficiency. In general, the efficiency is determined by accumulating multiple objectives into one objective by a linear combination and optimizing the resulting single-objective problem. The approach has some drawbacks such that exact information about solution alternatives will not be readily visible. In this paper the multi-objective evolutionary optimization algorithms, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm-2 (SPEA2), are successfully applied to the efficiency determination problem in induction motor. The performances of algorithms are compared on the basis of the obtained results.

Multiobjective genetic estimation to induction motor parameters

In order to simplify the offline identification of induction motor parameters, a method based on optimization using a multiobjective genetic algorithm is proposed. The non- dominated sorting genetic algorithm (NSGA-II) is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.

A COMPARATIVE STUDY ON PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHMS FOR TRAVELING SALESMAN PROBLEMS

This article deals with a performance evaluation of particle swarm optimization (PSO) and genetic algorithms (GA) for traveling salesman problem (TSP). This problem is known to be NP-hard, and consists of the solution containing N! permutations. The objective of the study is to compare the ability to solve the large-scale and other benchmark problems for both algorithms. All simulation has been performed using a software program developed in the Delphi environment. As yet, overall results show that genetic algorithms generally can find better solutions compared to the PSO algorithm, but in terms of average generation it is not good enough.

A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.

Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.

PERFORMANCE EVALUATION OF SUGAR PLANTS BY FUZZY TECHNIQUE FOR ORDER PERFORMANCE BY SIMILARITY TO IDEAL SOLUTION TOPSIS

This study presents the performance evaluation of sugar plants using the technique for order performance by similarity to ideal solution (TOPSIS) under a fuzzy environment. First, the decision criteria used to evaluate the performances are determined, and then the data from financial statements are collected from sugar plants. Accordingly, the ratings of various alternatives under various criteria and the importance weights of various criteria are assessed by evaluators using linguistic terms. The data obtained are converted into a fuzzy triangular number system and then the fuzzy TOPSIS method is applied to make a final decision. According to the closeness coefficients, the sugar plants are ranked from strong to weak. A real case study involving eight evaluation criteria and nine sugar plants assessed by nine evaluators is provided to illustrate the proposed method. The results show that this method is an effective tool for evaluating investment risks based on the heuristic knowledge acquired from experts.

Fuzzy logic-based induction motor protection system

The protection is very important to detect abnormal motor running conditions such as over current, over voltage, overload, over temperature, and so on. When a failure is sensed by the protection system, a time delay should be specified to trip the motor. In the classical systems, motors are stopped with the time delay, which is adjusted constantly without considering the fault level. This paper presents a fuzzy logic-based protection system covering six different fault parameters for induction motors. This paper focuses on a new time-delay calculation for stopping induction motor and improves the overall detection performance. The time delay is computed by fuzzy logic method according to various fault parameters when one of the failures occurs on the motor. This system is successfully tested in real-time faults on the motor, and it shows that it provides sensitive protection by fuzzy rules.

A comparative study of artificial neural network and ANFIS for short term load forecasting

Short term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season and temperature data of Turkey between years of 2009-2011, the prediction is carried out for 2012. The mean absolute percentage errors for ANFIS and ANN models are found as 1.85 and 2.02, respectively in all days except holidays of 2012.