Application of Quality 4.0 (Q4.0) and Industrial Internet of Things (IIoT) in Agricultural Manufacturing Industry
Bibliographic record
Abstract
The objective of this research is to apply Quality 4.0 (Q4.0) concept in Agriculture 4.0 (A4.0) to digitize the traditional quality management (QM) system and demonstrate the effectiveness of zero-defect manufacturing (ZDM) in the agricultural part manufacturing industry. An autonomous quality management system was developed based on the ZDM system using the Industrial Internet of Things (IIoT). Both traditional and autonomous quality management systems were evaluated using six-sigma quality indicators and machining and inspection cost analysis. The ZDM resulted in a significant improvement in the quality of CARD148 manufacturing, increasing the manufacturing process from a low level of sigma to a high level of sigma (0.75 to 5.10 sigma). The component rejection rate was reduced by a high percentage, leading to significant economic benefits and a significant reduction in machining cost. The process yield was also increased to a high percentage. The developed ZDM was found to be consistent in improving the quality of the turning process, with notable increases in tool life and reduction in inspection cost. The total component cost was reduced significantly, while the PPM value increased notably. While this study focuses on agriculture-related manufacturing organizations, the developed ZDM has potential for other machining industries to improve sigma levels, particularly in industries such as automotive and medical.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".