Additive manufacturing processing with ultra-short-pulse lasers
Bibliographic record
Abstract
Additive manufacturing (AM) technology has garnered significant attention for its ability to fabricate three-dimensional (3D) products with complex geometries. While continuous wave (CW) lasers have traditionally been used in AM as heat sources, they face limitations in nano and micro-scale fabrication due to the diffusion of thermal energy, which results in heat-affected zones, residual stress, substrate degradation, poor dimensional accuracy, and surface finish issues. To address these challenges, ultra-short pulse lasers, such as femto- and pico-second lasers, have proven effective at micro- and nanometer scales. The ultra-short laser pulses inhibit thermally induced mechanisms, instead generating a dense photo-excited plasma that weakens lattice cohesion, introduces disorder, and increases the mobility of atoms and defects which leads to non-thermal melting, phase transformations, and mechanical effects like Coulomb explosion, along with polymerization via two-photon excitation. These phenomena enhance AM by reducing residual stress, limiting oxidation, minimizing substrate damage, and achieving high dimensional accuracy. Ultra-short pulse lasers have enabled advancements in laser-induced nano-joining, laser powder bed fusion, direct laser writing including laser-induced transfer (LIT), and laser-induced reduction processes (LIR), two-photon polymerization (2PP), marking significant progress in the fabrication of 3D and potentially 4D products. This study reviews ultrafast laser-matter interactions, nano-joining mechanisms, and large-scale sintering processes, highlighting recent developments in selective laser melting for lightweight alloys, refractory metals, and ceramics. Direct laser writing techniques, including 2PP, LIR, and LIT, are also discussed for their role in creating complex microdevices. The paper concludes with insights into future trends and potential advancements in ultra-short-pulsed laser-based AM, emphasizing its role in driving innovation such 4D fabrication and addressing limitations like temperature estimation in non-thermal region for further improvement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".