Fault Tree Analysis: A Path to Improving Quality in Part Stay Protector A Comp
Bibliographic record
Abstract
This research aims to enhance the quality of automotive components by employing fault tree analysis (FTA) to identify root causes and implement improvements.The FTA method was applied within an Indonesian automotive component manufacturing company specializing in producing Part Stay Protector A Comp.This component is a critical linkage point between exhaust systems and vehicle end caps, playing a crucial role in maintaining vehicle safety and performance.The data analyzed in this study, which led to the root cause determination, primarily originated from customer claims.Among these claims, a notable concern was the off-centre hole in Part Stay Protector A Comp, reflecting one of the Critical To Quality (CTQ) characteristics, with 689 instances of non-conformance.A comprehensive improvement strategy was initiated after identifying root causes through FTA.Utilizing the minimal cut sets (MCSs) method, the analysis underscored that the primary contributor to the fault was identified as (B2) Jig Detection, a critical component within the production process.The Jig Detection system serves as a quality control mechanism, verifying the alignment and conformance of the Part Stay Protector A Comp during production.This strategy focused on minimizing errors in the production method and enhancing the standardization of cup and spot tip classifications to reduce the occurrence of off-centre hole defects.The primary root cause, (B2) Jig Detection, was addressed through an innovative approach that included additional training for labour and introducing a stopper within the Inspection Jig for the Stay Protector A Comp.This strategic enhancement aimed to empower the Inspection Jig to detect off-centre defects, mitigating the recurrence of such issues.Simultaneously, addressing the issue of the nonstandardized cup and spot tip usage classifications aimed to reduce the likelihood of offcentre hole defects reoccurring.The results of these improvements were systematically monitored and controlled over six months, demonstrating a significant reduction in noncentre hole defects.The subsequent results of these improvement efforts profoundly impacted product quality, ultimately reducing the count of off-centre hole defects from 689 to zero, thereby upholding the highest standards in automotive component quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".