Defect Reduction

Defect reduction research focuses on minimizing flaws in various manufacturing and software development processes to improve quality and reduce costs. Current approaches leverage machine learning, including rule-based systems and reinforcement learning, to identify defect-causing factors and develop mitigation strategies, often incorporating explainable AI techniques to enhance transparency and usability. These methods are applied across diverse domains, such as software engineering and additive manufacturing, demonstrating their broad applicability and potential to significantly improve product reliability and efficiency. The ultimate goal is to create proactive, data-driven systems that predict and prevent defects before they impact the final product.