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Engineering Science in
Additive Manufacturing Additive manufacturing of EH36 steels

such as PBF-EB, 16,19 binder jetting, and cold spray, identifying strategies to enhance its corrosion resistance.
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hold significant potential but remain underexplored. For example, modifying inclusions through treatments
PBF-EB offers better control over residual stresses due such as Mg-Ce refinement could improve the material’s
to its high-temperature environment, binder jetting resistance to localized corrosion, as demonstrated in
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provides high production rates with low material conventional EH36 steel. In addition, advanced surface
wastage, and cold spray enables material deposition at coatings, such as 5083Al or Zn15Al arc-sprayed layers,
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lower temperatures, preserving mechanical properties. could be explored for AM EH36 steel components to
However, these techniques face challenges, such as higher provide enhanced barriers against chloride penetration.
equipment costs, limited material compatibility, and a Combining AM techniques with protective coatings
lack of optimized process parameters for AMed EH36 or cathodic protection systems may offer a synergistic
steel, hindering their broader adoption. Expanding the approach to improving both corrosion and fatigue
application of AMed EH36 steel beyond traditional uses resistance. The lack of standardized testing protocols and
in shipbuilding and offshore structures is critical. The certification processes for AMed EH36 steel remains a
development of in situ formed composite or multimaterial significant barrier to industrial adoption. Safety-critical
components, hybrid manufacturing approaches, and novel applications, such as shipbuilding and offshore structures,
repair techniques is essential for achieving more versatile, require strict regulatory compliance, yet existing standards
cost-effective, and sustainable solutions. 87-89 For instance, are inadequate for AM components. 100,101 Collaborations
hybrid AM, 90,91 which integrates AM and conventionally between research institutions, classification societies, and
manufactured components, offers a balanced approach industry stakeholders are essential to establish robust
to material efficiency and structural complexity. Future guidelines for the mechanical, microstructural, and
research should focus on understanding the mechanical corrosion testing of AMed EH36 steel.
behavior of interfaces between AM and traditional
materials, as these are critical for ensuring structural 7.4. Integration of advanced technologies
integrity under cyclic marine loading conditions. Similarly, With the emergence of advanced technologies, AM of
in situ repair techniques using DED-LB and DED-Arc hold EH36 steel also benefits from the integration of advanced
significant promise for large-scale marine and offshore numerical simulations, digital twin technologies, and
components. 92,93 These methods allow for localized machine learning-driven optimization to enhance process
restoration, reducing downtime and costs compared to efficiency and reliability. Digital twins, which are virtual
full-component replacement. Further optimization replicas of physical manufacturing systems, leverage
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of these techniques, particularly regarding interfacial real-time data, simulations, and predictive modeling
adhesion, post-processing treatments, and long-term to optimize production processes by continuously
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performance in harsh marine environments, will enhance synchronizing with the physical system, enabling real-
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their industrial applicability. time process control, reducing variability, and improving
defect detection. 102,103 In AM, digital twins incorporate
7.3. Corrosion behavior and protection strategies finite element analysis to predict material behavior under
for AMed EH36 steel varying thermal and mechanical conditions, facilitating
The investigation of corrosion properties in AMed EH36 proactive mitigation of defects such as residual stress,
steel remains a largely unaddressed area, despite its critical porosity, and deformation. 104,105 Finite element simulations,
importance for marine and offshore applications. Studies such as those implemented in ABAQUS, have been used
on conventionally manufactured EH36 steel have revealed to analyze thermal history, phase transformations, and
significant susceptibility to pitting corrosion in marine stress evolution in DED-Arc fabricated EH36 steel,
environments, as evidenced by Li et al., who reported showing that scanning patterns, particularly zigzag
increasing pit depths over prolonged exposure to 3.5% patterns, significantly reduce residual stress distribution.
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NaCl solutions. Corrosion products, such as β-FeOOH, On the other hand, machine learning algorithms, such
Fe₃O₄, and α-FeOOH, were found to be the main cause as supervised deep neural networks and reinforcement
of material deterioration. These findings underscore learning, have been deployed to refine process parameters
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the need to explore whether similar corrosion behaviors such as laser power, scanning speed, hatch spacing, and
persist in AMed components, particularly given their deposition rate, ensuring consistent melt pool formation
unique microstructural features, such as finer grain and microstructural integrity across different printed
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sizes and potential inclusions 34,71 introduced during AM parts. 107,108 Recent advancements in Bayesian optimization
processes. Future research could focus on characterizing and reinforcement learning have further improved
the corrosion mechanisms in AM EH36 steel and parameter tuning by adaptively adjusting process settings

Volume 1 Issue 1 (2025) 10 doi: 10.36922/ESAM025060005