This study presents a detailed analysis of the production efforts for personal protective equipment in makerspaces and informal production spaces (i.e., community-driven efforts) in response to the COVID-19 pandemic in the United States. The focus of this study is on additive manufacturing (also known as 3D printing), which was the dominant manufacturing method employed in these production efforts. Production details from a variety of informal production efforts were systematically analyzed to quantify the scale and efficiency of different efforts. Data for this analysis was primarily drawn from detailed survey data from 74 individuals who participated in these different production efforts, as well as from a systematic review of 145 publicly available news stories. This rich dataset enables a comprehensive summary of the community-driven production efforts, with detailed and quantitative comparisons of different efforts. In this study, factors that influenced production efficiency and success were investigated, including choice of PPE designs, production logistics, and additive manufacturing processes employed by makerspaces and universities. From this investigation, several themes emerged including challenges associated with matching production rates to demand, production methods with vastly different production rates, inefficient production due to slow build times and high scrap rates, and difficulty obtaining necessary feedstocks. Despite these challenges, nearly every maker involved in these production efforts categorized their response as successful. Lessons learned and themes derived from this systematic study of these results are compiled and presented to help inform better practices for future community-driven use of additive manufacturing, especially in response to emergencies.
Build orientation in additive manufacturing influences the mechanical properties, surface quality, build time, and cost of the product. Rather than relying on trial-and-error or prior experience, the choice of build orientation can be formulated as an optimization problem. Consequently, orientation optimization has been a popular research topic for several decades, with new optimization methods being proposed each year. However, despite the rapid pace of research in additive manufacturing, there has not been a critical comparison of different orientation optimization methods. In this study, we present a critical review of 50 articles published since 2015 that proposes a method for orientation optimization for additive manufacturing. We classify included papers by optimization methods used, AM process modeled, and objective functions considered. While the pace of research in recent years has been rapid, most approaches we identified utilized similar objective functions and computational optimization techniques to research from the early 2000s. The most common optimization method in the included research was exhaustive search. Most methods focused on broad applicability to all additive manufacturing processes, rather than a specific process, but a few works focused on powder bed fusion and material extrusion. We also identified several areas for future work including integration with other design and process planning tasks such as topology optimization, more focus on practical implementation with users, testing of computational efficiency, and experimental validation of utilized objective functions.
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