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Fabrication settings such as printing speed and nozzle temperature in fused deposition modeling undeniably influence the quality and strength of fabricated parts. As available market filaments do not contain any exact information report for printing settings, manufacturers are incapable of achieving desirable predefined print accuracy and mechanical properties for the final parts. The purpose of this study is to determine the importance of selecting suitable print parameters by understanding the intrinsic behavior of the material to achieve high-performance parts.

Two common commercial polylactic acid filaments were selected as the investigated samples. To study the specimens’ printing quality, an appropriate scaffold geometry as a delicate printing sample was printed according to a variety of speeds and nozzle temperatures, selected in the filament manufacturer’s proposed temperature range. Dimensional accuracy and qualitative surface roughness of the specimens made by one of the filaments were evaluated and the best processing parameters were selected. The scaffolds were fabricated again by both filaments according to the selected proper processing parameters. Material characterization tests were accomplished to study the reason for different filament behaviors in the printing process. Moreover, the correlations between the polymer structure, thermo-rheological behavior and printing parameters were denoted.

Compression tests revealed that precise printing of the characterized filament results in more accurate structure and subsequent improvement of the final printed sample elastic modulus.

The importance of material characterization to achieve desired properties for any purpose was emphasized. Obtained results from the rheological characterizations would help other users to benefit from the highest performance of their specific filament.

This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.

The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.

The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.

To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.