Optimising Additive Manufacturing of NiTi and NiMnGa Shape Memory Alloys: A Review
Bibliographic record
Abstract
NiTi and NiMnGa stand out as prime thermal and magnetic shape memory alloys (SMAs), possessing a superior shape memory effect (SME) and superelasticity (SE). These alloys have crucial current and potential future applications across industries. Additive manufacturing (AM) offers a transformative approach to fabricating these materials into complex geometries; however, the quest to create integral additively manufactured structures with reliable thermal or magnetic shape memory properties remains a recent and fast-emerging research frontier. This article provides a comprehensive review on (i) the intricate principles giving rise to the thermal SME and SE in NiTi, and the magnetic SME in NiMnGa alloys, emphasising their specific relevance in the realm of AM, and (ii) the latest developments, recent findings, and ongoing challenges in the AM of NiTi- and NiMnGa-based SMAs, including their functional lattice structures. Based on this review, for the first time, novel, empirically derived AM process design maps tailored to maximise SME and SE in laser powder bed fusion- and directed-energy deposition-processed NiTi structures are proposed. Similarly, promising avenues to resolve the key challenges regarding the AM of NiMnGa magnetic SMAs are suggested. This article concludes by outlining the most promising future research directions shaping the trajectory of AM of these SMAs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".