Artificial Intelligence in Advanced Process Optimization and Smart Manufacturing Systems
Year: 2024
Type: article
Abstract: Industrial systems engineering is undergoing a significant transformation driven by the advent of intelligenttechnologies that enable smarter, more efficient, and sustainable operations. In the context of Industry 4.0,advanced computational models are reshaping traditional methods, addressing challenges in scalability,adaptability, and operational precision. This article explores the integration of data-driven solutions into industrialprocesses, focusing on their impact on predictive analytics, autonomous production, and adaptive quality controlframeworks. From a broader perspective, the fusion of advanced technologies has elevated decision intelligenceacross sectors. Predictive analytics has emerged as a cornerstone, utilizing machine learning techniques toanticipate equipment failures, optimize resource allocation, and enhance supply chain efficiency. Autonomousproduction systems leverage real-time decision-making through reinforcement learning and neural networks,enabling cost reductions and scalable operations. Digital twin technologies, bridging the physical and virtualrealms, allow iterative design, testing, and scenario planning to reduce risk and improve resource utilization.Narrowing the scope, adaptive quality control frameworks integrate computer vision and IoT-enabled systems toidentify defects and ensure consistency across manufacturing processes. Beyond individual applications, thesetechnologies foster cross-sector interoperability, creating resilient supply chains and supporting sustainablepractices. Challenges related to data integrity, transparency, and workforce integration are addressed, highlightingthe importance of ethical implementation and regulatory compliance. This comprehensive analysis underscoresthe transformative potential of integrating intelligent systems into industrial operations. By emphasizingcollaboration, scalability, and sustainability, this article provides a roadmap for stakeholders to adopt forwardlooking strategies, enabling more innovative and responsible industrial ecosystems
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893237495