Selectively embedding multiple spatially steered fibers in polymer composite parts made using vat photopolymerization

Fiber-Reinforced Polymer Composite (FRPC) parts are mostly made as laminates, shells, or surfaces wound with 2D fiber patterns even after the emergence of additive manufacturing. Making FRPC parts with embedded continuous fibers in 3D is not reported previously even though topology optimization shows that such designs are optimal. Earlier attempts in 3D fiber reinforcement have demonstrated additively manufactured parts with channels into which fibers are inserted. In this paper, we present 3D printing techniques along with a printer developed for printing parts with continuous fibers that are spatially embedded inside the matrix using a variant of vat photopolymerization. Multiple continuous fibers are gradually steered as the part is built layer upon layer instead of placing them inside channels made in the part. We show examples of spatial fiber patterns and geometries built using the 3D printing techniques developed in this work. We also test the parts for strength and illustrate the importance of spatially embedding fibers in specific patterns.Comment: 9 pages and 8 figure

Robot-aided selective embedding of a spatially steered fiber in polymer composite parts made using vat photopolymerization

Fiber-Reinforced Polymer Composite (FRPC) parts are predominantly laminates, shells, or surfaces wound with 2+D fiber patterns even after the emergence of additive manufacturing. Making FRPC parts with embedded continuous fibers in 3D is not reported previously even though topology optimization demonstrates that such designs are optimal. Earlier attempts in 3D fiber reinforcement include making parts with channels into which fibers are inserted or coextruding fiber with resin. In this work, A Vat-Photopolymerization Machine, and a process for concurrent embedding of spatially steered continuous fibers inside the matrix is developed. A single continuous fiber was embedded spatially using a robot to gradually steer the fiber as the part is built layer upon layer. An example of a fiber embedded along a helix in a cylindrical matrix is included in this work. Furthermore, a hinge effect was demonstrated when a fiber was embedded at a place that has substantial bending about the axis of the fiber.Mechanical Engineerin

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

The present article proposes the deep learning concept termed ―Faster-Region Convolutional Neural Network‖ (Faster-RCNN) technique to detect cracks on road for autonomous cars. Feature extraction, preprocessing, and classification techniques have been used in this study. Several types of image datasets, such as camera images, faster-RCNN laser images, and real-time images, have been considered. With the help of GPU (graphics processing unit), the input image is processed. Thus, the density of the road is measured and information regarding the classification of road cracks is acquired. This model aims to determine road crack precisely as compared to the existing techniques