The Impact of Defects on Tensile Strength and Modulus of 3D Printed Parts Manufactured by Fused deposition Modeling
Bibliographic record
Abstract
Additive manufacturing (AM), 3D printing, is defined as a process of depositing materials layer by layer to create three-dimensional printed models, as opposed to subtractive manufacturing methodologies. It has the potential of revolutionizing field of manufacturing, which allows us to create more complex geometries with lower cost and faster speed in comparison to injection molding, compression forming, and forging. Therefore, 3D printing can shorten the design manufacturing cycle, reduce the production cost, and increase the competitiveness. Due to the improvements of processes and advancements of modeling and design, Fused Deposition Modeling (FDM) technologies, a common 3D printing technique, have been involved in wide various applications in the past three decades and numerous studies have been gathered. This research work studies directional properties of FDM 3D printed thermoplastic parts per ASTM D638. Tensile strength and modulus of the coupons along and perpendicular to the printing direction are evaluated. It is observed that FDM 3D printing introduces anisotropic behavior to the manufactured part, e.g. tensile strength of 57.7 and 30.8 MPa for loading along and perpendicular to the printing direction, respectively. FDM 3D printers are not ideal and introduce defects into the manufactured parts, e.g. in the form of missing material, gap. This study investigates the impact of gaps on tensile strength and modulus of 3D printed parts. A maximum reduction of 20% in strength is found for a gap (missing bead) along the loading direction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".