Chapter 3 - Impacts of metal additive manufacturing on smart city infrastructure

Abstract

Future smart and adaptive city infrastructure will undoubtedly rely on the integration of ubiquitous sensing strategies with new, high-performance material and structural components. Advanced manufacturing techniques, while still in their relative infancy for civil applications, nevertheless will eventually support the manufacturing of these integrated components, making new designs and functionality possible. Specifically, metal additive manufacturing (MAM) is proving to be one of the most promising advanced manufacturing techniques, with applications aligning with smart city infrastructure such as embedded sensors and targeted structural reinforcement. While reduced part weight, cost, and production time are the benefits of MAM, there are several challenges which have prevented faster adoption by the manufacturing sector. Specifically, part reliability and repeatability are especially poor for MAM-produced components as compared to more mature manufacturing techniques. A variety of defects plague MAM production such as thermal deformation, irregularities in grain structure, and porosity, all of which cause substantial fabrication difficulties when compared to traditional, subtractive manufacturing techniques. Academia, industry, and US Government agencies such as the Department of Energy, Department of Defense, and The National Institute of Standards and Technology have reached a consensus of the widespread need for in situ monitoring systems to detect build-breaking mechanisms uniquely associated to MAM. It undoubtedly follows that the smart cities of tomorrow will require similar standards for process control. In this chapter, we review state-of-the-art MAM monitoring research designed to provide structural health assessments and detect MAM-specific material and structural defects. We provide a case study of the development and design process of a particular structured light monitoring system and the associated measurement model used to bolster its measurement capabilities.