Accurate inverse process optimization framework in laser directed energy deposition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In additive manufacturing (AM), particularly in laser-based metal AM, process optimization is crucial to the quality of products and the efficiency of production. The identification of optimal process parameters out of a vast parameter space, however, is a daunting task. Despite advances in simulations, the process optimization for specific materials and geometries is developed through a sequential and time-consuming trial-and-error approach and often lacks the versatility to address multiple optimization objectives. Machine learning (ML) provides a powerful tool to accelerate the optimization process, but most current studies focus on simple single-track prints, which hardly translate to manufacturing 3D bulk components for engineering applications. In this study, we develop an A ccurate I nverse process optimization framework in laser D irected E nergy D eposition (AIDED), based on machine learning models and a genetic algorithm, to aid the process optimization in laser DED. Using AIDED, we demonstrate the following: (i) Accurate prediction of the area of single-track melt pool ( R 2 score 0.995), the tilt angle of multi-track melt pool ( R 2 score 0.969), and the cross-sectional geometries of multi-layer melt pool (1.75% and 12.04% errors in width and height, respectively) directly from process parameters; (ii) Determination of appropriate hatch spacing and layer thickness for fabricating fully dense (density > 99.9%) multi-track and multi-layer prints; (iii) Inverse identification of optimal process parameters directly from customizable application objectives within 1-3 hours. We also validate the effectiveness of the AIDED experimentally by solving a multi-objective optimization problem to identify the optimal process parameters for achieving high print speeds with small effective track widths. Furthermore, we show the transferability of the framework from stainless steel to pure nickel using a small amount of additional data on pure nickel. With such transferability in AIDED, we pave a new way for “aiding” the process optimization of the laser-based AM processes that is applicable to a wide range of materials. • Inversely identifies optimal process parameters from customizable objectives. • Accurately predicts melt pool geometries directly from process parameters. • Finds optimal hatch spacing and layer thickness to make fully dense prints. • Transferable to new materials systems and optimization objectives with a small amount of extra data.
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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) | 0.000 | 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 it