On the Post‐Processing of Complex Additive Manufactured Metallic Parts: A Review
Bibliographic record
Abstract
Additive manufacturing (AM) is gaining more attention due to its capability to produce customized and complex geometries. However, one significant drawback of AM is the rough surface finish of the as‐built parts, necessitating post‐processing for achieving the desired surface quality that meets application requirements. Post‐processing of complex geometries, such as parts with internal holes, lattice structures, and free‐form surfaces, poses unique challenges compared to other components. This review classifies various post‐processing methods employed for complex AM parts, presenting the experimental conditions for each treatment alongside the resulting improvement in surface roughness as a success criterion. The post‐processing methods are categorized into four groups: electrochemical polishing (ECP), chemical polishing (CP), mechanical polishing, and hybrid methods. Notably, mechanical methods exhibit the highest roughness improvement at 69.9%, followed by ECP (59.9%), hybrid methods (47.4%), and CP (49.5%). Nevertheless, mechanical post‐processing techniques are less frequently utilized for lattice parts, making chemical or electrochemical methods more promising alternatives. In summary, all four categories of post‐processing methods can improve the internal surfaces quality of AM holes. While mechanical methods offer the most substantial roughness improvement overall, chemical and electrochemical methods show particular potential for addressing the challenges associated with complex geometries.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.002 |
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".