Unveiling the microstructure of LES manufactured parts for sustainable productive business
Bibliographic record
Abstract
The Light Engineering Sector (LES) is regarded as the bloodline of Bangladesh’s economy for simultaneously creating jobs, poverty alleviation, and strengthening foreign currency reserves by reducing dependency on imported products for local industries. But this promising industry is now facing roadblocks mainly due to poor quality under service loading. Though many new and advanced manufacturing techniques are now available, conventional sand casting is the most widely used manufacturing technique utilized by the local spare parts manufacturer. It is well established that the mechanical properties (strength, toughness) of a material are determined by its microstructure. To date, the microstructure characterization of LES products has not been subject to much scrutiny. Therefore, in this work, an attempt has been made to systematically investigate the microstructure of an LES-manufactured marine propeller using an Optical microscope (OM) and Scanning Electron Microscope (SEM) equipped with Energy Dispersive Spectroscopy (EDS). A locally made marine propeller supplied by ACBD company was used for this study. It was found that the microstructure of both materials mainly consists of pores, needle-shaped particles, and Chinese script-like features embedded in the aluminum matrix. Based on EDS results it may be concluded that the needle-shaped particle is mainly composed of Si and Al. Moreover, the average pore size observed was about 5109.5 μm. Use of horse-and-buggy technology, and lack of knowledge of the casting process such as properties of molding sand, the composition of raw material, overall metallurgical control of the casting process, etc. might have resulted in these casting defects. As the conventional particle fracture mechanism for ductile fracture starts once the maximum load is applied, the presence of pores may exhibit premature growth soon after the beginning of plastic deformation. Therefore, the presence of large pores may exhibit premature growth immediately at the beginning of plastic deformation, thereby drastically reducing the mechanical properties under service loading. In summary, both the government and the private sector should involve themselves in strong and focused dissemination research activities with the support of universities so that suitable modifications and modernization can be adopted in the existing system.
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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 | 0.000 |
| 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) | 0.001 | 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".