Enhancing Additive-Subtractive Hybrid Manufacturing: Addressing the Lubrication Challenge in the Subtractive Process for Improving the Surface Integrity of AISI H13
Bibliographic record
Abstract
As a type of steel with notable resistance to thermal fatigue, cracking, and abrasion, AISI H13, is known for its machining ease, high hardness, and consistent heat treatment response. These qualities make AISI H13 a preferred choice for fabricating dies, moulds, and tools exposed to high temperatures, rendering it popular in metalworking. Enhanced cooling technology in die and casting moulds necessitates intricate geometries achievable through additive manufacturing (AM). To further advance part complexity and surface finish in inaccessible regions, the industry is increasingly adopting additive-subtractive hybrid manufacturing (ASHM), showing promising growth. There are, however, some concerns associated with this process, especially with the subtraction part, which is done through machining. These include the low machinability of the workpiece due to the higher hardness of additively manufactured parts compared to conventional methods. These issues are more pronounced in ASHM processes because liquid coolants or lubricants cannot be easily used, nor can the parts be heat-treated before machining. As a first step in this study, we performed a comprehensive literature review on solid lubricants used in machining so far. Their effectiveness, mechanisms, challenges, and recent developments were discussed in detail. The main purpose of this study was to introduce and investigate the effectiveness of our novel metallic solid lubricant coating for machining of AISI H13 additive manufactured parts in ASHM processes. Since the goal of this study was to mimic the ASHM process, the lubricant coating was used in dry machining, without the application of any liquid lubricant or coolant. Moreover, the workpiece was not heat treated and the tests were performed on an as-built additively manufactured AISI H13 part. A detailed study was performed to assess the impact of these lubricant coatings on AISI H13 surface integrity. The findings showed that the soft metallic lubricant coating considerably reduced the machining force and improved the surface roughness subsurface properties of the part. This method proved to be an effective solution for avoiding lubricants in ASHM processes, while significantly improving the machinability of AISI H13 parts in ASHM processes.
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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 | 0.001 | 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".