GA-CNN: A Hybrid Approach for Control Chart Pattern Recognition

Control Chart Pattern Recognition (CCPR) is a fundamental task in Statistical Process Control (SPC) that enables the timely detection of process variations and abnormal operating conditions. Accurate recognition of control chart patterns contributes significantly to quality improvement, fault diagnosis, and process reliability. Although Convolutional Neural Networks (CNNs) have demonstrated outstanding performance in pattern classification problems, designing an effective network architecture remains a challenging task that greatly influences recognition accuracy.This paper proposes a hybrid Genetic Algorithm–Convolutional Neural Network (GA-CNN) model for control chart pattern recognition. In the proposed framework, a Genetic Algorithm (GA) is employed to optimize the convolutional feature extraction stage by determining the most suitable filter configuration for the CNN architecture. The optimized configuration guides the design of a deep multi-layer network consisting of five convolutional layers with hierarchical feature extraction capability. To improve training stability and generalization performance, batch normalization, Rectified Linear Unit (ReLU) activation functions, dropout regularization, and global average pooling (GAP) are incorporated into the architecture.The proposed GA-CNN model is evaluated on a control chart pattern dataset containing various normal and abnormal process conditions. Experimental results demonstrate that the optimized architecture effectively captures discriminative pattern characteristics and achieves a classification accuracy of 99.78%. The obtained results confirm that integrating evolutionary optimization with deep convolutional learning can significantly enhance recognition performance and provide a robust solution for intelligent quality monitoring systems.