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April 15, 20260 citationsOpen Access

Probabilistic Modeling and Mechanical Characterization of PLA Filaments in FDM-Based 3d Printing

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APAkarte Pratik PrakashShivaji UniversityPTPavan Kumar ThimmarajuJRE Group of Institutions

Key Points

  • This research aims to characterize the mechanical properties of PLA filaments used in 3D printing through probabilistic modeling.
  • Tested extruded PLA strands to measure axial tensile modulus, ultimate strength, and failure strain.
  • Utilized different gauge lengths to analyze material properties.
  • Applied the 2-parameter Weibull distribution for probabilistic strength prediction.
  • Assessed samples with and without overlap to determine failure likelihood.
  • The axial tensile modulus and ultimate strength varied significantly across different gauge lengths.
  • Failure strain was dependent on the specific conditions of the extruded strands.
  • Probabilistic modeling successfully predicted material failure at distinct stress levels.

Abstract

Subtractive manufacturing entails subtracting material until the required shape is achieved; in contrast, additive manufacturing entails constructing objects directly from a CAD model by layering materials. One popular method of three-dimensional printing that uses melted material to create successive layers is fused deposition modeling (FDM). We tested the extruded strands, the fundamental pieces, might simplify the time-consuming and expensive process of material characterization of 3D printed structures to acquire characteristics like stiffness and strength. Towards this, single strands the axial tensile modulus, ultimate strength, and failure strain of PLA material with different gauge lengths are tested, as well as those of numerous extruded filaments with or without overlap. This is done using the 2-parameter Weibull distribution in a probabilistic strength prediction model to ascertain the likelihood of extruded strand material failure at a certain stress.

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Cite This Study

Prakash et al. (2025) studied this question.

synapsesocial.com/papers/69df2bece4eeef8a2a6b0d79https://doi.org/10.5281/zenodo.18925457
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