Applied Computing and AI for Industrial Automation and Smart Systems

This comprehensive review examines the transformative impact of applied computing and artificial intelligence technologies on industrial automation and smart systems, analyzing current trends, technological advances, and implementation strategies across diverse industrial contexts. The research focuses on the seamless fusion of AI with traditional automation systems, covering core machine learning technologies including supervised learning for quality prediction, unsupervised learning for anomaly detection, reinforcement learning for process optimization, and deep learning for complex pattern recognition and decision making. Key technical contributions include computer vision systems for automated quality inspection and robot guidance, cyber-physical systems with AI-driven analytics and edge computing capabilities, and intelligent manufacturing systems with predictive maintenance, adaptive production scheduling, and real-time quality control that enhance operational efficiency and competitiveness.

The practical applications demonstrate significant quantitative benefits across automotive manufacturing and process industries, with documented production efficiency gains, quality improvements, maintenance cost savings, and energy consumption reductions through AI optimization. Industrial case studies include predictive quality control in assembly lines, intelligent scheduling for mixed-model production, autonomous material handling, and comprehensive supply chain optimization with measurable improvements in overall equipment effectiveness (OEE). The work addresses critical implementation challenges including legacy system compatibility, data quality requirements, cybersecurity protection, and workforce training while providing systematic technology adoption frameworks and best practices. The team seeks collaboration opportunities with industry partners and AI research communities to advance the integration of applied computing and artificial intelligence in industrial automation and smart systems applications.

For complete technical details and experimental results, please refer to the original publication: 22-acm-applied-computing-review-louise-xie.pdf