Aslan Deniz Karaoglan

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Genetic algorithms (GAs) are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP-hard problems. Genetic algorithm includes a number of parameters whose different levels strictly affect the performance of the algorithm. The general approach to determine the appropriate parameter combination of GA depends on too many trials of different combinations, and the best one of them that produces good results is selected for the programme, which would be used for problem solving. A few researchers studied on the parameter optimisation of GA. In this paper, response surface-dependent parameter optimisation is proposed to determine the optimal parameters of GA. Results are tested for benchmark problems that are most common in mixed-model assembly line balancing problems of type-I.

Optimization of Cutting Parameters in Face Milling with Neural Networks and Taguchi based on Cutting Force, Surface Roughness and Temperatures

Prediction of cutting parameters as a function of cutting force, surface roughness and cutting temperature is very important in face milling operations. In the present study, the effect of cutting parameters on the mentioned responses were investigated by using artificial neural networks (ANN) which were trained by using experimental results obtained from Taguchi’s L8 orthogonal design. The experimental results are compared with the results predicted by ANN and the Taguchi method. By training the ANN with the results of experiments which are corresponding with the Taguchi L8 design, with only eight experiments an effective ANN model is trained. By using this network model the other combinations of experiments which did not perform previously, could be predicted with acceptable error.

Optimal Design of C-type Passive Filters Based on Response Surface Methodology for Typical Industrial Power Systems

Abstract In this article, a response surface methodology based approach is proposed for the optimal design of C-type passive filters that can be employed in typical industrial power systems. The purpose of the optimization process is to minimize voltage and current total harmonic distortions. According to IEEE Standard 519-1992, both indices and displacement power factor are handled as constraints for the optimal filter design problem. To show the validity of the proposed approach, numerical results are presented.

Optimal design of single-tuned passive filters using Response Surface Methodology

This paper presents an approach based on Response Surface Methodology (RSM) to find the optimal parameters of the single-tuned passive filters for harmonic mitigation. The main advantages of RSM can be underlined as easy implementation and effective computation. Using RSM, the single-tuned harmonic filter is designed to minimize voltage total harmonic distortion (THDV) and current total harmonic distortion (THDI). Power factor (PF) is also incorporated in the design procedure as a constraint. To show the validity of the proposed approach, RSM and Classical Direct Search (Grid Search) methods are evaluated for a typical industrial power system.

Optimizing Karnopp friction model parameters of a pendulum using RSM

Abstract Accurate mathematical models of physical systems are essential for understanding the behaviour of actual systems under different operating conditions and for designing control systems. In mechanical systems, difficulty of the exact determination of friction force parameters adversely affects the accuracy of the models. In this study, Karnopp friction model is chosen in order to model the friction parameters of the bob of a pendulum. The parameters are determined by sectioning the speed regions and then using “Response Surface Methodology” (RSM) by sectioning the speed regions. Proposed method has produced accurate yet simple model of the friction parameters.

ARL performance of residual control charts for trend AR(1) process: A case study on peroxide values of stored vegetable oil

For the purpose of process control, quality assurance engineers in a vegetable oil factory wonder the performance of the Shewhart, CUSUM, and EWMA residual control charts for peroxide values that show both serial autocorrelation between adjacent observations (autocorrelation) and upward linear trend. To deal with autocorrelated process data, a primary method is to apply these charts to the uncorrelated residuals of an appropriate time series model fitted to the data. In the relevant literature, although performances of the residual charts have been widely studied for autocorrelated processes, there exists no study that shows how these charts’ performances change by the addition of a particular type of trend in the autocorrelated data. In the present paper, average run length performances of these charts are computed for peroxide data from two batches, for which trend stationary first order autoregressive (trend AR(1) for short) model is a representative model.

A regression control chart for autocorrelated processes

In this study, we present a new regression control chart which is able to detect the mean shift in a production process. This chart is designed for autocorrelated process observations having a linearly increasing trend. Existing approaches may individually cope with autocorrelated and trending data. The proposed chart requires the identification of trend stationary first order autoregressive (trend AR(1)) model as a suitable time series model for process observations. For a wide range of possible shifts and autocorrelation coefficients, performance of the proposed chart is evaluated by simulation experiments. Average correct signal rate and average run length are used as performance criteria.

Flow time and product cost estimation by using an artificial neural network (ANN): A case study for transformer orders

ABSTRACT In electromechanical industrial corporations, determining the production cost of the orders according to the technical specifications demanded by the customer has great importance in giving an accurate price offer. Labor cost is one of the important and most variable cost components that must be estimated in order to give an accurate price offer. In this study, a feed-forward back-propagation artificial neural network (FF-BPN) is used to predict the flow times of power transformer orders of a transformer producer according to the technical specifications given by the customer. The results of this study show that the prediction capability of an artificial neural network is very good for this type of problem and results in better cost estimation than current company practice. A case study is carried out for a manufacturer of electrical transformers in Turkey.

A new painting process for vessel radiators of transformer: wet-on-wet

The painting process of corrugated wall radiators of a distribution transformer is performed by a flow-down painting technique in the industrial field. This study has been prepared in accordance with ISO 12944-5. Correspondingly, this work is motivated by Epoxy 2-pack paints (4.3.4.2) to obtain minimum requirements for C3 atmospheric corrosivity categories (5.1.1). This standard requires from the vertical surface of the vessel of the transformer to be painted with epoxy paints that contain anti-corrosive pigments with a minimum of 100 µm dry film thickness. In the present study, a new production methodology called wet-on-wet (WOW) painting is developed which has never been used in industry. In addition, a modified response surface methodology (RSM) is proposed for designing, modeling, and optimizing the proposed process under unsteady environmental effects. The results indicate that the WOW painting can be applied to real industrial systems successfully by the aid of the proposed new RSM algorithm and provide remarkable time and cost savings.

Parameter Optimization of Fractional Order PI λ D μ Controller Using Response Surface Methodology

This chapter presents optimization of fractional order PI λ D μ control parameters by using response surface methodology. The optimization process is observed on a fractional order diffusion system subject to input hysteresis which is defined with Riemann–Liouville fractional derivative. The system is transferred to a fractional order state space model by using eigenfunction expansion method and then Grunwald–Letnikov approximation is applied to solve the system numerically. The necessary data for response surface analysis are read from the obtained numerical solution. Finally, second-order polynomial response surface mathematical model for the experimental design is presented and the optimum control parameters are predicted from this response surface model. The proposed optimization method is compared with the technique of minimization of integral square error by means of settling time and the results are discussed.