AI-enabled real-time process optimization in smart manufacturing environments
Davinder SINGH, RAJU, Shiva CHOUDHARY, Gyan Chand SHARMA, Amit Kumar YADAV
Abstract. Introduction of Artificial Intelligence (AI) to smart manufacturing has transformed conventional production systems as it allows optimization of processes in real-time. In this paper, the author introduces an extended outline of the AI-based decision-making in manufacturing facilities with the application of real-time sensor data, machine learning, and adaptive control. The offered system will help improve productivity, minimize the downtime, and optimize the product quality with the help of predictive analytics and dynamic optimization. It is experimentally proven that the efficiency, accuracy and operational performance greatly improve when using industrial datasets. The paper puts emphasis on the possibilities of AI-powered systems to reach Industry 4.0 goals.
Keywords
Artificial Intelligence, Smart Manufacturing, Real-Time Optimization, Machine Learning, Industry 4.0, Predictive Maintenance, IoT
Published online 8/15/2026, 7 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Davinder SINGH, RAJU, Shiva CHOUDHARY, Gyan Chand SHARMA, Amit Kumar YADAV, AI-enabled real-time process optimization in smart manufacturing environments, Materials Research Proceedings, Vol. 71, pp 49-55, 2026
DOI: https://doi.org/10.21741/97816449042312-7
The article was published as article 7 of the book AI-Driven Innovations in Mechanical, Manufacturing, and Energy Engineering
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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