Compressive and Tensile Properties of ABS Material as a Function of 3D Printing Process Parameters
Bibliographic record
Abstract
Additive Manufacturing (AM) technologies have been emerged as a fabrication method to obtain engineering components within a short span of time. 3D printing, also referred as additive layer manufacturing technology is one of the powerful methods of rapid prototyping (RP) technique that fabricates three-dimensional engineering components. fused deposition modelling (FDM) is one of the most commonly used additive manufacturing (AM) methods, with applications in modelling, prototyping, and production. Acrylonitrile–butadiene–styrene (ABS) is a widely used industrial thermoplastic that is also the most commonly used material in FDM technology. Understanding the impact of FDM build settings on material characteristics is essential for predicting the behaviour of ABS components. The purpose of this study is to determine the impact of specimen tensile and compressive behaviour on ABS components produced using FDM. The Ultimaker+2 printer is used to create ABS thermoplastic samples for the investigation. The samples are put through their tests using a modified form of ASTM D638 for tensile strength and ASTM D695 for compressive strength. An Instron testing machine is used to put the printed parts to the test. The approach employed was Design of Experiment (DOE). Three primary criteria are used in the plastics experiment: infill density, layer thickness, and infill pattern. We measured the tensile and compressive strengths of zigzag and gyroid specimens, as well as cross specimens. The highest compressive strength at break (25.01 MPa), Young's modulus (2.473 GPa), fracture strength (21.016 MPa), and ultimate tensile stress (23.1 MPa) were all discovered in a sample with 60% infill density, 0.05mm layer thickness, and a GYROID infill pattern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".