Selective LASER melting part quality prediction and energy consumption optimization
Bibliographic record
Abstract
Abstract Selective LASER Melting (SLM) popularity is increasing because of its ability to quickly produce components with acceptable quality. The SLM process parameters, such as LASER power and scan speed, play a significant role in assuring the quality of customized SLM products. Therefore, the process parameters must be tuned appropriately to achieve high-quality customized products. Most existing methods for adjusting the SLM’s parameters use multiple inputs and one or two outputs to develop a model for achieving their desired quality. However, the number of the model’s input and output parameters to be considered can be increased to achieve a more comprehensive model. Furthermore, energy consumption is also a factor that should be considered when adjusting input parameters. This paper presents a multi-inputs-multi-outputs (MIMO) artificial neural network model to predict the SLM product qualities. We also try to combine training data from different sources to achieve a more general model that can be used in real applications by industries. The model inputs are LASER power, scan speed, overlap rate, and hatch distance. Moreover, four critical product quality measures: relative density, hardness, tensile strength, and porosity, are used as the model’s outputs. After finding a proper model, an energy optimization method is developed using the genetic algorithm in this paper. The objective of the optimization is to minimize the energy consumption of SLM manufacturing with a less compromised output quality. The results of this study can be used in the industry to decrease energy consumption while maintaining the required quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".