Statistical quality improvement techniques such as design of experiments (DOE) and Taguchi methods form an essential part of the search for improved product performance. This paper applies both the Taguchi and full factorial design techniques to highlight the application and to compare the effectiveness of the Taguchi and full factorial design processes as applied on surface roughness. Besides that, to determine the optimal parameter setting for each factor in surface roughness. For this study, we used two different probes of Mahr Surf XR20 which was MFW 250 tracing arm 6851804 (25μm) and tracing arm 6851806 (50μm). The main effect and interaction plot had been analyzed by using MINITAB (software). The experiment result showed that full factorial design performs better than Taguchi method.

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As many researches focused on application of non-destructive testing, very less concerned on the critically to reduce the inspection time rely on decrease cost operation. Liquid penetration testing has been broadly used for over 30 years in creation industrial operation. The need for reducing time in liquid penetration testing process is crucial to ensure the inspection systems vigour towards achieving master production schedule and customers satisfaction. This paper investigates and experimentally, the influence of penetration dwell time on development time in liquid penetration testing. Also considering value of hardness and surface roughness as the factor, the obtained results confirm that high penetration dwell time show strongly change the development time of liquid penetration time in directly decrease the inspection time.

Support Vector Machine (SVM) is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in river stream flow forecasting. In this paper, SVM is proposed for river stream flow forecasting. To assess the effectiveness SVM, we used monthly mean river stream flow record data from Pahang River at Lubok Paku, Pahang. The performance of the SVM model is compared with the statistical Autoregressive Integrated Moving Average (ARIMA) and the result showed that the SVM model performs better than the ARIMA models to forecast river stream flow Pahang River.