Influence of laser surface polishing on surface topology and residual stress in laser powder bed fusion additively manufactured aluminium‐12 silicon part
Bibliographic record
Abstract
Abstract Laser‐assisted additive manufactured surfaces are more often inherently associated with surface and subsurface defects such as poor surface texture, high surface roughness, high tensile residual stress, porosity etc. Depending on the design and methodology adopted, these factors entail limitations in improving the functional properties of additively manufactured parts. Post‐processing often becomes mandatory to improve surface finish, residual stresses and other surface‐dependent properties. Nowadays, aluminium alloys are widely used for lightweighting in aerospace, aircraft and automotive industries with special emphasis on manufacturing complex design and multi‐functional components employing additive manufacturing routes (both laser and non‐laser based). The present work aims to demonstrate laser surface polishing (by remelting) of laser‐assisted powder bed fusion aluminium‐12silicon additively manufactured parts as a viable post‐processing technique to improve surface properties. Aluminium‐12 silicon cubes printed by laser powder bed fusion at optimum processing conditions having high relative density were stress‐relived and subjected to laser surface polishing employing a multi‐mode square‐beam diode laser under varying energy densities. Results indicated a profound influence of energy density on resulting surface roughness, remelted depth, residual stress and microstructure of laser‐assisted powder bed fusion additive manufactured parts. At optimum energy density, the surface roughness of the additive manufactured part was reduced by 82 % with smoothing of initial chaotic texture and reduction in residual tensile stress to zero‐level. Indeed, laser surface polishing at optimum energy density enhanced surface and subsurface micro‐hardness to 75 HV 0.5 – 100 HV 0.5 from 65 HV 0.5 – 75 HV 0.5 in the initial additive manufactured part on account of refined rapidly solidified structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".