An Ensemble of fuzzy Class-Biased Networks for Product Quality Estimation

Factories are increasingly pushing towards automation and data-centric approaches under the current Industry 4.0 standards. Early-stage product quality estimation is identified as one of the solutions that can significantly reduce manufacturing cost and wastage. However, quality estimation is a classification problem that is inherently challenging for traditional data-driven algorithms due to its imbalanced nature. In this paper, a framework is proposed, that combines the feature extraction capabilities of convolutional neural networks and the domain knowledge characteristics of fuzzy systems. The proposed method addresses data imbalance using an ensemble of class-biased individuals, that learn features using a class-weighted loss function. Experiments were conducted using a benchmark dataset and production data acquired from the semiconductor industry. Improvements were noted for G-Mean and ROC-AVC values when compared to existing algorithms.