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Nominal elastic modulus assessment in 3D-printed components under varying printing parameters using Bayesian methods

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This study introduces an efficient surrogate model for precisely calibrating elastic properties of 3D printed materials. The non-destructive method ensures component safety and consistency in additive manufacturing.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Additive Manufacturing

Background:

  • 3D printing adoption is growing in advanced manufacturing for complex geometries and waste reduction.
  • Accurate material elastic properties are vital for structural design safety and consistency.
  • Limited non-destructive methods exist for mechanical property assessment in 3D printed components.

Purpose of the Study:

  • To develop an efficient surrogate model for high-precision calibration of material elastic constants in 3D printed parts.
  • To enable non-destructive mechanical property assessment for additive manufacturing components.

Main Methods:

  • Fused Deposition Modeling (FDM) for 3D printing samples.
  • Operational Modal Analysis (OMA) to obtain modal information under cantilever beam conditions.
  • Bayesian model updating combined with a random forest algorithm and Markov Chain Monte Carlo (MCMC) for parameter calibration.

Main Results:

  • Significantly reduced deviation between calculated and measured modal frequencies.
  • Modal Assurance Criterion (MAC) values between updated calculated and measured mode shapes exceeded 0.99.
  • Accurate calibration of structural elastic modulus at the component level was achieved.

Conclusions:

  • The proposed Bayesian surrogate model offers a practical, non-destructive approach for calibrating elastic properties of 3D printed materials.
  • This method enhances component safety and consistency in additive manufacturing.
  • The technology is extendable to various 3D printed product structures.