Linking directed energy deposition process toolpaths to FEM to study the toolpath/geometry role on residual stress formation for multi-layer components
Bibliographic record
Abstract
Purpose This paper aims to investigate the use of the element-birth-and-death technique within the finite element method (FEM) to analyze direct energy deposition (DED) processes, an additive manufacturing (AM) technique. The focus is on understanding the interaction between different geometries and toolpaths in multi-layer depositions and establishing a foundation for generating optimal tool paths. The long-term goal of this research is optimizing thermal and structural performance, minimizing residual stress and improving overall part quality. Design/methodology/approach This study integrates DED toolpath vectors from process planning software into ANSYS to conduct thermal and structural analyses. This novel integration of design and process simulation enables a more accurate prediction of part quality. Four geometries and three toolpath strategies were analyzed, requiring the computational mesh domain to adapt to the toolpath characteristics. Experimental measurements validated the model, and the influence of dwell time intercooling on residual stress formation was explored. The research also compared the effects of various toolpath strategies and deposition patterns on the mechanical properties of the parts, highlighting the importance of toolpath generation for subsequent process certification and qualification activities. Findings The results show that shorter beads with longer dwell times lead to better part quality compared to longer beads with shorter laser-off intervals. A transverse deposition with identical dwell times produced the best part quality, despite requiring longer build times. In addition, frequent laser-off cycles were less effective at reducing high residual stress areas but resulted in lower average tensile stresses at mid-layers, though these cycles pose challenges for powder-based systems. This analysis helps in benchmarking and enhancing DED as an AM process. Originality/value This study offers a novel method of integrating DED toolpath characteristics into FEM-based simulations, providing valuable insights into the thermal and mechanical behavior of parts under various toolpath and geometry configurations. The research contributes to advancing the design-for-AM paradigm by demonstrating the critical role of thermal management and toolpath directionality in minimizing residual stress. This foundational work will support further research into more complex geometries and hybrid manufacturing techniques, aiding in the industrialization and optimization of DED processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".