Braided Fabrication of a Fiber Bragg Grating Sensor

Abstract

Our objective was to construct textile braiding manufacturing methods to facilitate high precision and accurate measurements using optical fiber Bragg grating sensors for various structures. We aimed to combine three-dimensional (3D) braiding processing with the optical Bragg grating sensor’s accurate metrology. Outside the limits of the sensor’s epoxy attachment methods, the textile braiding method can diversify the scope of application. The braiding process can be used to design a 3D fabric module process for multiple objective mechanical fiber arrangements and material characteristics. Optical stress–strain response conditions were explored through the optimization of design elements between the Bragg grating sensor and the braiding. Here, Bragg grating sensors were located 75% away from the fiber center. The sensor core structure was helical with a 1.54 cm pitch, and a polyurethane synthetic yarn was braided together with the sensor using a weaving machine. From the prototype results, a negative Poisson’s ratio resulted in a curled braided Bragg grating sensor. The number of polyurethane strands was studied to determine the role of wrap angle in the braiding. The 12-strands condition showed an increase in double stress–strain response rate at a Poisson’s ratio of 1.3%, and the 16-strands condition was found to have noise affecting the sensor at a Poisson’s ratio of 1.5%. The findings suggested the application of braiding fabrication to the Bragg grating sensor could help to develop a new monitoring sensor.

Towards personalised early prediction of Intra-Operative Hypotension following anesthesia using Deep Learning and phenotypic heterogeneity

“…To do so, the authors trained several machine learning models with data recorded from the start of anesthesia induction to right before intubation. By adopting a feature engineering approach, a Random Forest (RF) approach combined with feature selection showed the highest accuracy, followed by CNN and XGBoost (15). Similarly, (4) developed a prediction model for anesthesia-induced hypotensive events with the same time constraints.…”

“…Supervised Machine Learning methods provide an opportunity to exploit large amounts of sequential data and are therefore increasingly utilized in training and analysing physiological timeseries for prediction. Deep learning (DL) methods also represent an important alternative to assist clinicians as they can learn more complex relationships of the input data without the need of human interpretation (22, 23, 20, 24, 25). In the field of anaesthesiology, the development of algorithms predictive of intra-operative physiological alterations is showing encouraging results, often outperforming traditional modelling (26, 27, 28).…”

“…It was utilised for the real-time prediction of a IOH at 5, 10 and 15 minutes in advance using combined biosignal waveforms (acquired using one-point SBP/DBP, photoplethysmography, capnography and electrocar-diography) and may provide a more reliable prediction for patients than the more invasive arterial catheterisation. Another study (22) aimed at predicting hypotensive events occurring between tracheal intubation and incision. To do so, the authors trained several machine learning models with data recorded from the start of anesthesia induction to right before intubation.…”

Opto-Mechatronics System for Train-Track Micro Deformation Sensing

“…It was not until 1989 that Meltz, an American scholar, and his colleagues successfully made FBG using the side-exposure method with high-intensity ultraviolet (UV) light, after which grating production efficiency was greatly improved, laying a technical foundation for engineering mass production [ 10 ]. After decades of gradual development, the mass-production technology of FBG has become quite mature, and the production cost has been greatly reduced, which has greatly promoted the rapid development of FBG sensors in various industries [ 11 , 12 , 13 , 14 , 15 ].…”

Strain-sensing fiber with a core–sheath structure based on carbon black/polyurethane composites for smart textiles

“…9 Flexible sensors can be fabricated into a fiber-like shape so that they can be manufactured on a large scale, like regular polymeric fibers, using wellestablished textile processing technologies. 10 The fiber-shaped flexible sensors can then be blended with traditional textile fibers, which are characterized as soft, lightweight, durable, and permeable to form sensing yarns or fabrics using spinning, 11 weaving, 12 knitting, 13 or braiding 14 technologies to improve the wearing comfort and durability. The yarn or fabric can then serve as the sensing component of a textilebased smart system.…”