Hsw embroidery machine in chennai , perambur, mylapore, madipakkam - VS SewingMachine
Hsw embroidery machine in chennai , perambur, mylapore, madipakkam - VS SewingMachine
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The measurement of vibrations is an important technique for the in-service evaluation and monitoring of machines and civil structures. For such measurement, optical fiber sensors are very promising candidates as optical fibers are electromagnetic interference- and hazard-free and their use enables the sensors to be operated remotely with the electronics and electrical connections situated away from the sensing sites, which is a major advantage in many applications. In this work, multimode optical fiber sensors are used to monitor the vibrations of an industrial sewing machine. The measurement is based on vibration sensing using multimode interference in a proposed sensor configuration which is simple in construction and straight forward to operate. The principle of operation, the results obtained and suggested further investigations of the proposed sensors are discussed.
In garment manufacturing, an automatic sewing machine is desirable to reduce cost. To accomplish this, a high-speed vision system is required to track fabric motions and recognize repetitive weave patterns with high accuracy, from a microperspective near a sewing zone. In this letter, we present an innovative framework for real-time texture tracking and weave pattern recognition. Our framework includes a module for motion estimation using blob detection and feature matching. It also includes a module for lattice detection to facilitate the weave pattern recognition. Our lattice-detection algorithm utilizes blob detection and template matching to assess pair-wise similarity in blobs' appearance. In addition, it extracts information of dominant orientations to obtain a global constraint in the topology. By incorporating both constraints in the appearance similarity and the global topology, the algorithm determines a lattice that characterizes the topological structure of the repetitive weave pattern, thus allowing for thread counting. In our experiments, the proposed thread-based texture tracking system is capable of tracking denim fabric with high accuracy (e.g., 0.03° rotation and 0.02 weave-thread translation errors) and high speed (3 frames per second), demonstrating its high potential for automatic real-time textile manufacturing.
