It is widely known that the printing quality of fused filament fabrication (FFF) is heavily affected by environmental temperature and humidity, taking the form of warping and porosity. However, there is little understanding about the quantitative relations between environmental conditions, geometry, and the mechanical properties of printed parts. In this study, we systematically investigated those relations using bisphenol A polycarbonate as a model material system. For the environmental temperature, an in-situ infrared imaging analysis revealed the presence of an up to 5.4 °C/mm thermal gradient when printing using an open-chamber printer and a heated build plate. For the environmental humidity, an analysis of X-ray micro-computed tomography (micro-CT) scans showed an up to 11.7% porosity that was brought by polymer water content absorbed from environmental moisture. Meanwhile, tensile tests showed a mechanical performance loss associated with those defects, but, surprisingly, the transverse direction ductility had the potential to increase at a higher porosity. Furthermore, the experimental results were combined with analytical and parametrical studies to elucidate quantitative relations between environmental conditions and printing quality. Based on the results, quantitative guidelines for the estimation of printing quality based on environmental conditions are provided that would also help users to obtain desired printing results with a better understanding of the effects of environmental conditions.

Many workers who engage in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSDs). Quantifying the physical demands of a job is important for identifying high risk jobs and is a legal requirement in the United States for hiring and return to work following injury. Currently, most physical demand analyses (PDAs) are performed by experts using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can be problematic, particularly when identifying restrictions during the return-to-work process. Further, when a worker does return-to-work on modified duty, there is no way to track compliance to work restrictions conflating the effectiveness of the work restrictions versus adherence to them. To address this, we applied a deep learning model to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities. Overall, a 95% accuracy was reached for predicting isolated occupational physical activities. However, when applied to more complex tasks that combined occupational physical activities (OPAs), accuracy varied widely (0–95%). More work is needed to accurately predict OPAs when combined into simulated work tasks.