Analysis of Aluminium Alloy Wheels Product Quality Improvement Through DMAIC Method in Casting Process: A Case Study of the Wheel Manufacturing Industry in Indonesia
Bibliographic record
Abstract
The global market demand for automotive wheels with alloy materials is 55%, which is quite high compared to other materials such as steel, magnesium, chromed, and carbon fiber. The high competition of the global alloy wheels market demands to be able to offer quality alloy wheels. The purpose of this research is to reduce the number of defects in the casting process step by using the Define Measure Analyze Improve Control (DMAIC) method. This study shows the systematic approach to find the root cause of major defects in aluminum castings using the defect diagnostic approach as well as cause and effect diagram. Quality improvement using quality tools, namely the Pareto diagram, fishbone diagram. The major defects for the rejections during production were identified as leak defects, porosity motive holes, and oval defects. In determining the proposed quality improvements using the FMEA tool. The results of data processing on the calculation of process capabilities and product performance show improvements after quality improvements in the casting process. Product performance from DPMO = 15.462, sigma level = 3.6 to DPMO = 8.186 and sigma level = 3.9. The effect of decreasing the percentage of defects could save production costs by IDR 413.350.000. Therefore, the application of the DMAIC method can provide a significant improvement in product quality and impact on production cost savings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".