Development of a two-dimensional fiber optic position sensor

Dhawan, Ravi; Dikshit, Biswaranjan; Kawade, Nitin

doi:10.1016/j.ijleo.2018.05.067

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…After the region of the light spot is found by the program, the center point of the light spot region is displaced to overlap the CCD image center, and the magnification of the CCD is increased to coincide with the size of the light spot region. The light spot region is thus separated, and the first search is completed [16][17][18]. This system uses a convolutional neural network to search for the laser spot region.…”

Section: Resultsmentioning

confidence: 99%

The Application of Deep Learning and Image Processing Technology in Laser Positioning

et al. 2018

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In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

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Section: Resultsmentioning

confidence: 99%

The Application of Deep Learning and Image Processing Technology in Laser Positioning

et al. 2018

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“…Similarly, Dhawan, Dikshit & Kawade [79] developed a twodimensional fibre optic position sensor. The method is capable to produce vibrations measurements in a 2-D plane for structural health monitoring applications.…”

Section: Optical Methodsmentioning

confidence: 99%

A Literature Review of Non-Contact Tools and Methods in Structural Health Monitoring

Vegas

2021

ETOAJ

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In this review paper, the methodology employed was undertaken according to the following steps. First, the literature was collected from the SCOPUS database due to it indexes the largest number of journals and is commonly used in engineering fields [12,13]. The initial search was performed on the 21st of January 2021 with the keywords "structural health monitoring" and with the aim of obtaining exclusively the non-contact literature, the specific query was: TITLE-ABS-KEY ("structural health monitoring" AND non-contact* OR noncontact* OR contactless). The boolean operators (AND, OR, *) are used to include entirely the non-contact literature and to incorporate potential literature with different spellings of non-contact. An initial 623 results were obtained, a further limit was imposed on only English language documents. The search was not limited to a time frame, resulting in 608 documents from 1994 to 2021 plotted in Figure 1. Then, the 608 articles were browsed and 92 irrelevant documents (e.g., medical fields) were excluded and subsequently were classified according to the main method/tool, Table 1 summarizes it. Figure 1: Research publications on the use of non-contact methods and tools in structural health monitoring.Table 1: Non-contact methods/tools in SHM. Methods Number of Articles Percentage Vision-based 146 28.29% Guided Waves 91 17.64% Ultrasound 49 9.50% Vibration 47 9.11% Magnetic Methods 33 6.40% Acoustic based 31 6.01% Displacement based 28 5.43% Thermal Inspection 18 3.49%

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“…The dynamic displacement sensors are also often used at multiple points of an object(s) to monitor the multi-dimensional or multipoint motion. [12][13][14][15] In such applications, all the sensors should be synchronized. However, as the number of measurement points is increased, the more complex control is required for synchronizing all the sensors.…”

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confidence: 99%

Time division multiplexing for multipoint measurement of dynamic displacement using interferometer with phase-modulated reference light

Furukawa

Tanaka

2019

Jpn. J. Appl. Phys.

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For the purpose of multipoint dynamic displacement measurement, we investigate application of time division multiplexing (TDM) technique to a fiber optic interferometer, in which the phase of the reference light is modulated and the channels of the probe light are switched in synchronization with the modulation frequency. In the proposed TDM method, measurements at multiple points are synchronized without any complicated control. By changing the duty ratio of the switching time, bandwidth allocated for each channel of measurement is changed arbitrarily. Simulation and proofof-concept experiment confirm the principle and the effectiveness of the proposed method.

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Development of a two-dimensional fiber optic position sensor

Cited by 4 publications

References 10 publications

The Application of Deep Learning and Image Processing Technology in Laser Positioning

The Application of Deep Learning and Image Processing Technology in Laser Positioning

A Literature Review of Non-Contact Tools and Methods in Structural Health Monitoring

Time division multiplexing for multipoint measurement of dynamic displacement using interferometer with phase-modulated reference light

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