Search for: All records

PurposePowder bed density is a key parameter in powder bed additive manufacturing (AM) processes but is not easily monitored. This research evaluates the possibility of non-invasively estimating the density of an AM powder bed via its thermal properties measured using flash thermography (FT). Design/methodology/approachThe thermal diffusivity and conductivity of the samples were found by fitting an analytical model to the measured surface temperature after flash of the powder on a polymer substrate, enabling the estimation of the powder bed density. FindingsFT estimated powder bed was within 8% of weight-based density measurements and the inferred thermal properties are consistent with literature findings. However, multiple flashes were necessary to ensure precise measurements due to noise in the experimental data and the similarity of thermal properties between the powder and substrate. Originality/valueThis paper emphasizes the capability of Flash Thermography (FT) for non-contact measurement of SS 316 L powder bed density, offering a pathway to in-situ monitoring for powder bed AM methods including binder jetting (BJ) and powder bed fusion. Despite the limitations of the current approach, the density knowledge and thermal properties measurements have the potential to enhance process development and thermal modeling powder bed AM processes, aiding in understanding the powder packing and thermal behavior. 

This paper introduces an extensible framework to predict small-business closures to inform urban planners, lenders, and business owners as to factors to improve business resilience. This paper couples machine learning with two point of interest (POI) datasets and infrastructure data and uses New York State’s COVID-19 PAUSE as a stressor for investigating small-business resiliency. The study included 2537 food-related, non-chain, retail businesses across select New York City zip codes, of which 17.7% closed permanently. Macro-, meso-, and micro-levels of features included the neighborhood profile, street dynamics, and venue-specific, location-related characteristics. A Gaussian Mixture Neural Network model achieved 74.1% precision, 92.5% recall, and an 82.3% F1-score without use of financial data. High-end restaurants located further than average from public transit were most at risk for closure, while non-restaurant, food businesses in commercially diverse areas having higher-than-average social media ratings were least at risk. This paper introduces a model for timely prediction of pandemic-induced, food-related, small-business closures without reliance on private or protected financial data, and provides insights into urban design to promote small, food business survivability. 

In recent decades, more than 100 different mechanophores with a broad range of activation forces have been developed. For various applications of mechanophores in polymer materials, it is crucial to selectively activate the mechanophores with high efficiency, avoiding nonspecific bond scission of the material. In this study, we embedded cyclobutane-based mechanophore cross-linkers (I and II) with varied activation forces (fa) in the first network of the double network hydrogels and quantitively investigated the activation selectivity and efficiency of these mechanophores. Our findings revealed that cross-linker I, with a lower activation force relative to the bonds in the polymer main chain (fa-I/fa-chain = 0.8 nN/3.4 nN), achieved efficient activation with 100% selectivity. Conversely, an increase of the activation force of mechanophore II (fa-II/fa-chain = 2.5 nN/3.4 nN) led to a significant decrease of its activation efficiency, accompanied by a substantial number of nonspecific bond scission events. Furthermore, with the coexistence of two cross-linkers, significantly different activation forces resulted in the almost complete suppression of the higher-force one (i.e., I and III, fa-I/fa-III = 0.8 nN/3.4 nN), while similar activation forces led to simultaneous activations with moderate efficiencies (i.e., I and IV, fa-I/fa-IV = 0.8 nN/1.6 nN). These findings provide insights into the prevention of nonspecific bond rupture during mechanophore activation and enhance our understanding of the damage mechanism within polymer networks when using mechanophores as detectors. Besides, it establishes a principle for combining different mechanophores to design multiple mechanoresponsive functional materials.