Enhancing Product Quality Using Artificial Neural Networks and Genetic Algorithms

This study introduces an integrated framework combining Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to optimize quality in multi-component product assemblies. The novelty of this work lies in its closed-loop structure: ANN is used to predict product performance indices based on component configurations, and these predictions are directly used as the fitness criteria in GA-based optimization. Unlike traditional isolated models, the framework enables predictive, data-driven decision-making to improve product quality and resource efficiency in manufacturing environments.