Kaveh Rashvand

This study presents the development of a predictive model to determine the tensile strength of continuous fiber thermoplastic composites produced through 3D printing. The proposed model utilizes the Curtin model to predict composite strength by incorporating statistical Weibull parameters related to composite filament strength and fiber volume fraction. Moreover, the model incorporates a welding function that captures the effect of the temperature history on the tensile strength, which is influenced by the interfacial shear strength at the filament-matrix interface. The proposed model provides a direct link between the printing process parameters and the tensile strength of the 3D-printed composite, while the spatial variation of strength is captured. The development of the model is based on a comprehensive experimental campaign, which includes single-fiber tensile tests to determine the fiber stiffness, tensile tests on composite filaments with different gauge lengths to extract the Weibull parameters of filament strength, and tensile tests on annealed 3D-printed composite samples to determine the welding function parameters. To quantify the thermal history during the 3D printing process and subsequently calculate the welding function, a finite element model is implemented in the commercial FEM software Abaqus through the utilization of advanced subroutines. The accuracy of the strength predictive model is validated against tensile tests conducted on 3D-printed composite coupons. The framework was calibrated and validated for PolyLite PETG reinforced with Anisoprint CCF 1.5 K carbon-fiber composite filament. The fitted welding-function parameters were A = 6.41 × 10 ^− 11 s and E _a =11.202 kJ mol ^− 1 . For the withheld validation conditions, the model predicted the tensile strength with RMSE = 5.67 MPa and MAPE = 0.57%. This model, therefore, enables a cost- and time-efficient optimization of the manufacturing process to achieve the desired mechanical performance, reducing costly trial-and-error-based 3D printing campaigns.

The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.