Characterization of Micro-Plasma Wire Arc Additive Manufacturing: Anisotropy and Layer Height Investigation
Bibliographic record
Abstract
Directed energy deposition describes the process of deposition of molten metal in wire or powder form with a focused energy beam source in a layer-by-layer fashion to create a final part. The use of an arc as heat source and wire as feedstock material for directed energy deposition, also known as wire arc additive manufacturing, has become increasingly popular in recent years due to its high productivity, high versatility, availability, cost, and its ability to produce large and complex parts. \nHowever, due to the additive nature of the process and the high heat input involved, anisotropy is a recurring problem arising in printed parts, which leads to different tensile properties in the travel and build directions. Hence, the first section of this work looks into the mechanical properties and microstructures of a thin-walled AISI316LSi austenitic stainless-steel component fabricated by wire arc additive manufacturing using the micro-plasma arc welding process, which is a low heat input process. While properties were mainly uniform, the effect of anisotropy was found to have a significant influence on the modulus of elasticity, with values ranging from 79.5±6.8 GPa along the build direction to 105.2±20.7 GPa in the travel direction. This difference was found to be due to the strong preferential orientation of grains during solidification along the direction corresponding to the build direction, which was also confirmed by electron back scatter diffraction. This was also confirmed by theoretical calculations. \nThe second portion of the work deals with the investigation of the effect of vibratory weld conditioning on the grain size for titanium and stainless-steel layers using the current process. This was motivated by the need to break down the orientation of columnar grains witnessed and transform them into random equiaxed grains. Tests were conducted through the deposition of five layers for each material and the use of a shaker device and a signal generator, which was used to conduct tests based on v-square waves with different amplitudes and frequency ranges. Results revealed that fine grains were achieved when close to the substate, while only frequency was found to have a significant effect on secondary dendrite arm spacing and grain size for stainless-steel and titanium, respectively. \nThe final section of this work deals with correcting layer height deviations, which arise as a result of the heat accumulation of the wire arc additive manufacturing process. The performance of the automatic voltage control, which automatically adjusts the Z-position of the torch during deposition based on arc voltage measured, was initially investigated based on gain and correction speed. Results revealed very high correlations between Z-position and bead height, particularly for a gain of 1.0 (R=0.96) and a max speed of 65 mm/min (0.995). This proved the high reliability of the automatic voltage control when maintaining the voltage measured with the desired voltage but still does not account for surface inconsistencies. Hence, layer height deviations were measured and corrected with an accuracy of 0.03 mm through the modification of the wire feed speed, obtained by determining the exact volume of material added during deposition for different wire feed speeds. Also, in this section, optimal bead overlay parameters were determined based on best fusion and flat surface, revealing to be 15 % for substate welding and 25 % for subsequent layer deposition.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".