Browsing by Subject "Condition monitoring"
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Item Open Access Influence of tool wear on machining forces and tool deflections during micro milling(Springer, 2016) Oliaei, S. N. B.; Karpat, Y.Tool wear on the cutting edges of micro end mills is an important issue affecting process outputs such as tool deflections and surface roughness, especially when difficult-to-cut materials such as titanium alloys, stainless steel, etc. are machined at micro scale. An understanding of the interactions between tool wear, machining forces, tool deflections, and surface roughness is important in order to maintain component quality requirements. However, in literature, the number of studies concerning tool wear in micro end mills is limited. The goal of the paper is to better understand tool wear patterns (flank wear, edge rounding) of micro end mills and their relationship to machining parameters. In this study, first, the influence of tool wear on micro milling forces and surface roughness parameters is analyzed and favorable micro milling process parameters are identified. It is shown that, when machining with worn end mills, forces are affected by the tool wear patterns. Then, the influence of increased milling forces due to tool wear on tool deflections and tool breakage is studied using both experimental techniques and finite element analysis. The finite element model-based tool deflection and tool breakage predictions are validated through experiments. The results of this study can be used in process parameter selection in pocket micro milling operations and tool condition monitoring systems.Item Open Access Real-time fire and flame detection in video(IEEE, 2005) Dedeoğlu, Yigithan; Töreyin, B. Ugur; Güdükbay, Uğur; Çetin, A. EnisThis paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in wavelet domain. Periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.