| Autor: |
Singh, Swati, Batra, Raman, Rai, Keerti, Sujai, S. |
| Předmět: |
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| Zdroj: |
Proceedings on Engineering Sciences; 2024, Vol. 6 Issue 1, p343-352, 10p |
| Abstrakt: |
The proactive exploration and avoidance of errors or variations from quality standards during the manufacturing process is referred to as "early quality detection" in the manufacturing industry. Post-production inspection, which can be expensive and time-consuming, is used in traditional quality control systems. To overcome this, we proposed a Modified gravitational search algorithm-based decision tree (MGSA-DT) to predict the quality of manufacturing processes at an early stage. We gathered sensors data in the manufacturing industry. In order to prepare the data for principal component analysis (PCA), Z-score normalization is used. Then, the essential features are extracted from the preprocessed data. To assess the effectiveness of the suggested approach in terms of accuracy (98.4%), precision (97.6%) and recall (97.2%), respectively. Implementing early quality detection techniques in manufacturing has demonstrated encouraging outcomes in enhancing the overall quality of products and decreasing production expenses. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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