Abstract

ARTIFICIAL INTELLIGENCE FOR PREDICTIVE QUALITY IN INDUSTRIAL PROCESS CONTROL: A COMPREHENSIVE REVIEW

Bhashkar M. Ingle*, Vijay M. Waghulkar, Monika P. Jadhao, Shailesh G. Jawarkar

Abstract

In the modern era of Industry 4.0, manufacturing and process industries are transforming toward smart, data- driven systems. Artificial Intelligence (AI) has emerged as a key enabler for predictive quality management in industrial process control. Predictive quality refers to the capability of a system to anticipate deviations in product or process quality using realtime data and advanced analytics. AI techniques such as machine learning (ML), deep learning (DL), and digital twins enable predictive models that reduce defects, minimize downtime, and enhance process stability. This review presents an in-depth discussion of AI applications in predictive quality for industrial process control, highlighting methodologies, frameworks, and case studies. Current challenges including data scarcity, model interpretability, and integration with legacy systems are discussed along with future directions such as explainable AI and hybrid intelligent systems.

Keywords: Artificial intelligence, predictive quality, industrial process control, machine learning, deep learning, Industry 4.0, digital twin.