We develop a new processing algorithm for the analysis of high-speed quantitative polarized light microscopy measurements. The measurements are obtained using a high-speed rotating polarizing component and a camera, collecting images at several polarizer angles per rotation. The technique uses data from less than the full quarter-waveplate rotation and then performs an optimal fit of the measured data to the expected response curves. Thus, it allows to increase the effective frame rate of the alignment angle and retardation maps due to the reduction in required images. Due to the complexity of the intensity response curves, a particle swarm optimization method is applied. The algorithm addresses the motion error in high-speed polarization imaging while still using multiple polarization angles for the reconstruction. We apply the algorithm to two example cases: quasi-static loading of a tensile coupon and quality inspection of a polymer fiber during rapid motion. The results demonstrate that increasing the reconstructions per second (i.e., decreasing the number of polarization angles per reconstruction) does not significantly decrease the quality of the reconstructions until ∼2.5 times the increase in reconstructions per second is achieved. Therefore, the developed algorithm is an effective method to increase the effective polarization imaging rate without complex hardware modifications.
Current trends in polymer fiber production for nonwoven material applications focus on increasing production rates and decreasing the fiber thicknesses. The quality of the polymer fibers during the fiber spinning process is influenced by the processing parameters, such as the spinning speed, throughput, and the polymer material. Irregularities in the crystallization process during the extrusion of the fibers can lead to stress concentrations and defects in the fibers that could cause failure of fibers and potential failure of the nonwoven material that is manufactured from those fibers. The ability to recognize these irregularities in fibers using a non-destructive measurement method would reduce the downtimes for production lines as well as provide in-situ quantitative data that could be used for optimization of the production process parameters. In this study, we implemented a high-speed polarization imaging technique that is capable of non-destructive measurement of the internal stress fields as well as detection of defects within a post-fabricated fiber. This imaging technique has been combined with a motion tracking algorithm for accurate alignment of the images corresponding to the same segments of the fiber. The results show that the technique is capable of detecting stress concentration regions in fabricated fibers in static and dynamic testing conditions. The sensitivity of the system also allows to track the changes in the distribution of the internal stress fields in static and dynamic loading. Future studies will apply the technique to the fiber spinning process.
A high-speed polarization imaging instrument is demonstrated to be capable of measuring the collagen fiber alignment orientation and alignment strength during high-displacement rate dynamic loading at acquisition rates up to 10 kHz. The implementation of a high-speed rotating quarter wave plate and high-speed camera in the imaging system allows a minimum measurement acquisition time of 6 ms. Sliced tendon-to-bone insertion samples are loaded using a modified drop tower with an average maximum displacement rate of 1.25 m / s, and imaged using a high-speed polarization imaging instrument. The generated collagen fiber alignment angle and strength maps indicate the localized deformation and fiber realignment in tendon-to-bone samples during dynamic loading. The results demonstrate a viable experimental method to monitor collagen fiber realignment in biological tissue under high-displacement rate dynamic loading.
A spectral profile division multiplexed fiber Bragg grating (FBG) sensor network is described in this paper. The unique spectral profile of each sensor in the network is identified as a distinct feature to be interrogated. Spectrum overlap is allowed under working conditions. Thus, a specific wavelength window does not need to be allocated to each sensor as in a wavelength division multiplexed (WDM) network. When the sensors are serially connected in the network, the spectrum output is expressed through a truncated series. To track the wavelength shift of each sensor, the identification problem is transformed to a nonlinear optimization problem, which is then solved by a modified dynamic multi-swarm particle swarm optimizer (DMS-PSO). To demonstrate the application of the developed network, a network consisting of four FBGs was integrated into a Kevlar woven fabric, which was under a quasi-static load imposed by an impactor head. Due to the substantial radial strain in the fabric, the spectrums of different FBGs were found to overlap during the loading process. With the developed interrogating method, the overlapped spectrum would be distinguished thus the wavelength shift of each sensor can be monitored.
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