In‐depth investigation and industry plan for enhancing surface finishing of <scp>3D</scp> printed polymer composite components: A critical review
Bibliographic record
Abstract
Abstract Additive manufacturing (AM) is pivotal in modern manufacturing, allowing diverse materials to create functional parts. Polymer composites, with superior properties like enhanced mechanics and conductivity, are highly regarded. Fused deposition modeling (FDM) is a favored, cost‐effective AM technique. Despite popularity, FDM struggles with poor surface quality, impacting final part properties. This challenge affects composite part performance, prompting a need for surface quality enhancements in AM processes, like FDM. The review explores essential post‐treatments required to optimize composite parts for various applications. It examines surface finishing techniques for FDM 3D printed polymeric composites, categorizing them into chemical, thermal, and physical methods. Recent research is extensively analyzed, offering a comprehensive guide for academia and industry. The review covers diverse aspects, including properties, surface morphology, electrical conductivity, and mechanical properties pre‐ and post‐finishing methods. Additionally, it summarizes leading companies worldwide providing surface finishing services for 3D printed polymer parts. Discussions on future trends, challenges, and research gaps in this field are included, emphasizing its significance in refining surface finishing methods for FDM 3D‐printed polymeric composites and encouraging broader industrial adoption of AM. This overview serves as a crucial roadmap for engineers, scientists, customers, and companies interested in surface finishing services.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".