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Pathum Thani, Thailand

Kandananond K.,Valaya Alongkorn Rajabhat University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

The capability to optimize the surface roughness is critical to the surface quality of manufactured work pieces. If the performance of the available CNC machine is correctly characterized or the relationship between inputs and output is clearly identified, the operators on the shop floor will be able to operate their machine at the highest efficiency. In order to achieve the desired objective, this research is based on the empirical study which is conducted in such a way that the optimization method is utilized to analyze the empirical data. The focused process in this study is the lathing process with three input factors, spindle speed, feed rate and depth of cut while the corresponding output is surface roughness. Two methods, namely artificial neural network (ANN) and 2k factorial design, are used to construct mathematical models exploring the relationship between inputs and output. The performance of each method is compared by considering the forecasting errors after fitting the model to the empirical data. The results according to this study signify that there is no significant difference between the performance of these two optimization methods. © Springer International Publishing Switzerland 2016. Source

Kandananond K.,Valaya Alongkorn Rajabhat University
Advances in Mechanical Engineering | Year: 2010

The purpose of this paper is to determine the optimal cutting conditions for surface roughness in a turning process. This process is performed in the final assembly department at a manufacturing company that supplies fluid dynamic bearing (FDB) spindle motors for hard disk drives (HDDs). The workpieces used were the sleeves of FDB motors made of ferritic stainless steel, grade AISI 12L14. The optimized settings of key machining factors, depth of cut, spindle speed, and feed rate on the surface roughness of the sleeve were determined using the response surface methodology (RSM). The results indicate that the surface roughness is minimized when the depth of cut is set to the lowest level, while the spindle speed and feed rate are set to the highest levels. Even though the results from this paper are process specific, the methodology deployed can be readily applied to different turning processes. Copyright © 2010 Karin Kandananond. Source

Kandananond K.,Valaya Alongkorn Rajabhat University
International Journal of Quality, Statistics, and Reliability | Year: 2010

The objective of this paper is to quantify the effect of autocorrelation coefficients, shift magnitude, types of control charts, types of controllers, and types of monitored signals on a control system. Statistical process control (SPC) and automatic process control (APC) were studied under non-stationary stochastic disturbances characterized by the integrated moving average model, ARIMA (0, 1, 1). A process model was simulated to achieve two responses, mean squared error (MSE) and average run length (ARL). A factorial design experiment was conducted to analyze the simulated results. The results revealed that not only shift magnitude and the level of autocorrelation coefficients, but also the interaction between these two factors, affected the integrated system performance. It was also found that the most appropriate combination of SPC and APC is the utilization of the minimum mean squared error (MMSE) controller with the Shewhart moving range (MR) chart, while monitoring the control signal (X) from the controller. Therefore, integrating SPC and APC can improve process manufacturing, but the performance of the integrated system is significantly affected by process autocorrelation. Therefore, if the performance of the integrated system under non-stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when integrating SPC and APC. © 2010 Karin Kandananond. Source

Kandananond K.,Valaya Alongkorn Rajabhat University
ICIC Express Letters | Year: 2015

When the behavior of demand data is non-stationary, i.e., the observations have no fixed mean over time, this scenario adds more complexity to the demand forecasting scheme. Since the traditional forecasting technique always relies on the fixed model which lacks the capability to handle the fluctuation of demand. The adaptability of forecasting model to the change in the observations’ pattern is crucial to minimize the prediction error. As a result, Kalman filter algorithm for non-stationary observations is developed as the proposed method in this study because of its advantage which enables the state prediction to be updated after the actual observation is known. However, the in-depth characterization analysis of Kalman filter under the different degrees of non-stationarity is unkown but it is important since the results will lead to the better understanding of how to utilize the Kalman filter at its best. The empirical study is conducted to fulfill the objective and the first step is to simulate the non-stationary observations following the Box-Jenkins’ autoregressive integrated moving average: ARIMA (0,1,1) or IMA (1,1). The 1,000 sets of observations are simulated at each value of MA coefficient, θ, (from 0.1 to 0.9: increased by 0.1). Afterwards, the Kalman filter is applied to the observations and the prediction errors are calculated in terms of the mean absolute percentage errors (MAPE) which is used as the performance index. According to the results, Kalman filter seems to work efficiently when the value of MA coefficient is highly positive since the average MAPE at θ = 0:9 is as low as 7.15%. However, Kalman filter might not be suitable for use for prediction when the values of θ are lowly positive because the average MAPE at θ = 0:1 is as high as 45.71%. © 2015 ICIC International. Source

Kandananond K.,Valaya Alongkorn Rajabhat University
Energies | Year: 2011

Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods-autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)-were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance. © 2011 by the authors. Source

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