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A multi-axis extrusion-based 3D printer was developed with two printheads configured orthogonally to add counter-gravity printing capability to conventional gantry-based fused deposition modeling. Process settings, including layer height and print speed, could be customized for each printhead. Printheads could be controlled independently to manufacture products with customized spatial properties. The CAD models of products were sliced into at least two segments to prevent collision between printheads and designs. The primary printhead needed to start to create a part of the design as a substrate for the secondary printhead, which began its action when enough space was provided for its motion. Subsequently, the primary printhead could continue constructing on a part built by the secondary printhead. 

Toolpath design plays a significant role in determining the efficiency of Additive Manufacturing (AM) processes. Traditional toolpath optimization methods frequently depend on empirical methods, which may not adequately account for the complex dynamics of the printing process. This study introduces a novel reinforcement learning (RL) approach, leveraging Proximal Policy Optimization (PPO), to optimize toolpath generation with a particular focus on reducing energy consumption. A custom-built environment was created, simulating the toolpath planning scenario as a discrete grid space, where an RL agent representing the printing nozzle learns to navigate and optimize its path. The RL agent, implemented using Proximal Policy Optimization (PPO), was trained on grids of increasing complexity (10x10 and 25x25) using two reward systems: a default system and an energy-optimized system based on a custom energy model. The energy model penalizes energy-intensive vertical and diagonal movements while rewarding horizontal movements. Results from training showed that the energy-optimized model achieved a significant reduction in energy consumption without compromising toolpath efficiency. On the 10x10 grid, energy consumption decreased from 92.7 𝑘𝑊𝑚𝑠 to 83.5 𝑘𝑊𝑚𝑠, while on the 25x25 grid, it dropped from 400.2 𝑘𝑊𝑚𝑠 to 395.4 𝑘𝑊𝑚𝑠. Statistical analysis using paired t-tests confirmed these reductions with p-values of 0.00, demonstrating the effectiveness of incorporating energy constraints in RL training for AM. This research highlights the potential of RL in improving the sustainability and efficiency of AM processes through intelligent toolpath design. 

Metal additive manufacturing has become integral to the modern aerospace and defense industry. Technologies such as powder bed fusion and direct energy deposition have reshaped these sectors. However, challenges like anisotropy and process-related defects still prevent the direct use of printed parts without post-processing. Electron beam powder bed fusion (EB-PBF) is well known for allowing builds at elevated temperatures and eliminating the need for stress relief. However, EB-PBF parts also experience epitaxial growth in the build direction, which causes anisotropy. This research explores two scanning strategies with spot melting techniques— stochastic and single directional—to fabricate IN718 parts using EB-PBF. After fabrication, the samples were analyzed using EBSD to evaluate grain formation in all directions. The findings suggest that point-based melting, guided by these strategies, can affect the microstructure in the build direction. This advancement offers the potential for tailoring controlled parts in future applications. 

This study compares the fabrication of IN718 alloy using bi-directional raster and stochastic spot melting techniques with the open-source FreemeltOne Electron Beam Melting (EBM) system. The research aimed to produce dense parts using both scanning strategies, employing custom Python code for raster melt beam path generation and PixelMelt software for stochastic spot melting path generation. After optimizing process parameters, 10mm height builds for each scanning strategy were fabricated, and their microstructure, hardness, and density were analyzed using optical microscopy and SEM, Vickers microhardness scale, and a pycnometer. The findings reveal valuable insights into the effects of scanning strategies on the microstructure, hardness, and density of IN718 alloy components, advancing additive manufacturing knowledge.