High-speed digital imaging techniques and web measurements were used to investigate the meltblowing (MB) process. We evaluated fiber diameter, fiber orientation, fiber entanglement, fiber velocity and fiber acceleration between the die and collector. Three processing variables were studied: primary air pressure, die-to-collector distance and collector surface speed. Although results of this investigation are somewhat preliminary, they provide fundamental information about the MB process and increase our understanding of it. Introduction Meltblowing (MB) is a fast, chaotic and complicated process. These features make it difficult to study the MB process theoretically as well as experimentally and most researchers have simply studied the effects of resin and process variables on web structure or web properties. Some researchers, however, have reported on-line measurements during MB [1-9]. Bansal and Shambaugh measured fiber temperature during single-hole MB using an infrared camera [1]. Wu and Shambaugh measured fiber velocity using laser Doppler velocimetry during single-hole MB [2]. Shambaugh and others reported experimental measurements of fiber motion and fiber diameter using a single-hole die [1-7]. Multiple-exposed photographs using conventional film were produced with a strobe light in a dark room to study fiber motion and single-exposed photographs were used to estimate fiber diameter. The exposure duration of the strobe light (50 µs), however, was not short enough to eliminate image blur and obtain sharp images so the primary air velocity used during MB was low (17-55 m/s). Milligan and Utsman used a similar film-based photographic technique to investigate MB using a 30-hole die [8]. Bresee and Yan used a video imaging technique to investigate the dynamics of web formation at the collector of a 600-hole MB line [9]. Measurements of the dynamics between the die and collector of a high-speed commercial-like MB process would be expected to be especially desirable for understanding MB. To directly observe dynamic motions during this fast process, it is necessary to use a short exposure time to freeze motion in each Experimental Study of the Meltblowing Process

Due to the space inconsistency between benchmark image and segmentation result in many existing semantic segmentation algorithms for abdominal CT images, an improved model based on the basic framework of DeepLab-v3 is proposed, and Pix2pix network is introduced as the generation adversarial model. Our proposed model realizes the segmentation framework combining deep feature with multi-scale semantic feature. In order to improve the generalization ability and training accuracy of the model, this paper proposes a combination of the traditional multi-classification cross-entropy loss function with the content loss function of generator output and the adversarial loss function of discriminator output. A large number of qualitative and quantitative experimental results show that the performance of our proposed semantic segmentation algorithm is better than the existing algorithms, and can improve the segmentation efficiency while ensuring the space consistency of the semantics segmentation for abdominal CT images.

On-line measurements during melt blowing combined with off-line web analysis are presented to fundamentally describe the commercial melt blowing process.

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