Emerging Trends in Computer Applications for Industrial Automation: A Mini-Review

Abstract

Industrial automation is moving away from rule based programmable logic control to autonomous data driven cyber physical environments. This paper looks at computer applications—spanning edge cloud machine learning, real time optimization and the industrial internet of things (IIoT)—while identifying important directions for future research. We identify four core research pillars: (1) deterministic edge intelligence balancing low latency control with neural network inference, (2) explainable AI (XAI) for safety critical root cause debugging, (3) converged IT/OT architectures dissolving hierarchical automation pyramids and (4) human centric cobot symbiosis under Industry 5.0 frameworks. Synthesis of multi sector deployments shows that while algorithm accuracy often exceeds 95% in controlled settings overall system robustness, semantic interoperability and zero trust industrial cybersecurity remain major obstacles. This paper also presents a phased research roadmap that prioritizes semantic modelling (OPC UA extensions) lightweight federated learning, for privacy preserving fleet learning and energy aware autonomous orchestration.