Pathum Thani, Thailand

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Tansripraparsiri S.,Valaya Alongkorn Rajabhat University
Key Engineering Materials | Year: 2014

The development of pottery products was emphasized as the research and development project. The main ingredient was Nong Suea clay, Pathumthani province. Normally, Nong Suea clay was used for land-fill industries. However, the way to use Nong Suea clay as materials for pottery products could be a value added technology. In addition, the pottery products created from Nong Suea clay were used as the decorative materials. From the line blend method, six mixing ingredients were focused. The researcher found that the suitable mixing ingredient was the third formula that contained Nong Suea clay (80%) and Ranong white clay (20%). This formula was processed into five types of pottery products. Two temperatures were monitored for firing. First, the temperature at 850 °C was used for three different types of pots. This product was normally shaped by hand with a throwing wheel that led to the industrial process level. Second, the temperature at 1,200 °C was used for two different types of vases. The product decorations of these groups were applied by the flowing glaze technique which was suitable for long-term research and development. The craftmen in should be highly skilled, tool uses, and higher temperature capacity of kiln. © (2014) Trans Tech Publications, Switzerland.


Kandananond K.,Valaya Alongkorn Rajabhat University
Key Engineering Materials | Year: 2014

Although the manufacturing businesses have played an important role in generating the highest GDP for Thailand, they also emit more greenhouse gas (GHG) than other sectors. Due to the cap and trade scheme by European Union (EU), the carbon footprint is the GHG emitted by products, organization or persons and it has to be tracked and recorded. Since the ceramic production process also has a major contribution on the emission, its carbon footprint is a piece of product information which cannot be ignored. In this research, the carbon footprint for the whole life cycle of a local ceramic product was recorded and calculated. It is interesting to note that the resource extraction stage has contributed to the highest emission followed by the product use, manufacturing, disposal and distribution. The results from this research are useful for local ceramic manufacturers who want to export their products to the EU countries and it is also important for the customers who are concerned about the environment. © (2014) Trans Tech Publications, Switzerland.


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.


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.


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.


Kandananond K.,Valaya Alongkorn Rajabhat University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

The dynamic characteristic of a drum boiler is complex and this complication leads to the difficulty in controlling the output of the system, i.e., steam pressure. Therefore, this study attempts to investigate the application of two model predictive methods, artificial neural network (ANN) and system identification, in order to assess the performance of each method. According to the system, the inputs are feed water flow rate and applied heat while the output is the steam pressure. The ANN method used is based on a training algorithm, Levenberg-Marquardt back propagation. On the other hand, the optimal model of system identification method is the output error (OE). The performance measurement is compared by considering the mean squared error (MSE) after fitting the simulated prediction from each model to the observation. The results show that ANN slightly outperforms the system identification technique. Moreover, another finding is that ANN method is capable of identifying the outlier among the observations so it is robust to the disturbances. © Springer International Publishing Switzerland 2015.


Kandananond K.,Valaya Alongkorn Rajabhat University
Applied Mechanics and Materials | Year: 2014

Product demands are known to be serially correlated. In this research, a first order autoregressive model, AR (1), is utilized to simulate product demand processes whose behavior are stationary. Since demand forecasting is important to the efficiency improvement of product supply chain system, different forecasting techniques are utilized to predict product demand. In this research, Kalman filter is deployed to forecast demand simulated by AR (1) model. Product demands are simulated at the different degrees of autoregressive coefficients. After the application of Kalman filter to the designated data, the forecasting errors are calculated and the results indicate that Kalman filter is an efficient technique to predict demands in the future. © (2014) Trans Tech Publications, Switzerland.


Kandananond K.,Valaya Alongkorn Rajabhat University
Procedia Computer Science | Year: 2013

The performance of artificial neural network (ANN) and support vector machine (SVM) method for forecasting time series data is still an open issue for discussions among many authors in the literature. Hence, the purpose of this study is to characterize the capability of these two methods under the autocorrelation structure of time series and the most appropriate model is chosen. In this research, the performance of ANN and SVM is compared with respect to the autoregressive integrated moving average (ARIMA) structure. Two classes of ARIMA models, ARMA (1, 1) and IMA (1, 1), are utilized to represent stationary and non-stationary processes while the performance index of each learning method is the forecasting errors computed after each learning cycle. In order to deliver the right conclusions, the statistical analysis is conducted and the conclusions are drawn by utilizing the factorial design of experiment. The results indicate that these two machine learning methods have a different performance under the specific scenario of autocorrelation. When processes are stationary, the ANN might be a better choice than the SVM method. However, it turns out to be that the SVM has obviously outperformed the ANN for non-stationary cases. © 2013 The Authors.


Kandananond K.,Valaya Alongkorn Rajabhat University
Procedia Engineering | Year: 2014

The implementation of statistical control charts under autocorrelated situations is a critical issue since it has a significant impact on the monitoring capability of manufacturing processes. The objective of this study is to assess the performance of control charts under different scenarios and to optimize the design of control charts to best deal with autocorrelated processes. To achieve the proposed objective, two autoregressive integrated moving average models, ARIMA (1, 0, 1) and ARIMA (0, 1, 1), are utilized to characterize stationary and non-stationary processes. These process models were simulated to achieve the response, average run length (ARL), which is the performance measure of this study. The factorial design of experiment was conducted to quantify the effect of critical factors, i.e., ARIMA coefficients, types of charts (exponentially weighted moving average: EWMA and moving range: MR) and shift sizes on the ARL. The experimental results show that EWMA chart is the most appropriate control chart to monitor autocorrelated observations. Additionally, both AR and MA parameters along with shift sizes have a significant effect on the performance of control charts. Therefore, this study has pointed out a suitable tool for use under the different scenarios of autocorrelation. The validation of the above experimental results was conducted on another ARIMA model, ARIMA (1, 0, 0). If the performance of control charts under autocorrelated disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when deploying SPC. © 2014 The Authors. Published by Elsevier Ltd.


Kandananond K.,Valaya Alongkorn Rajabhat University
Procedia Engineering | Year: 2014

Green supply chain is a supply chain system focusing on environmental impacts and the efficiency of energy used. A green supply chain will be achieved if a system is able to track down all information regarding the environmental influence. However, a green supply chain will not be possible without enterprise resource planning (ERP) implementation in organizations. ERP is the integrated information system overlooking manufacturing processes from raw materials to finished products. However, the successful implementation of ERP depends on four critical factors: defining business cases, prepare system and users, stabilizing to obtain normal operations and maintaining and upgrading. Moreover, learning organization (learning from own experience and learning from others) is another key ingredient for the successful implementation. Last but not least, process mapping from "As- Is" to "To-Be" models is also a powerful technique which facilitates the implementation by identifying the process models of current and future ones. Moreover, the featured functions of ERP for a green supply chain should include the capability to keep and track the environmental data of raw materials from suppliers, to prepare an environmental report for each product from raw materials to finished products, to keep the environmental data regarding logistics and transportation and to comply with the ERP software used by third-party manufacturers. © 2014 The Authors. Published by Elsevier Ltd.

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