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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM51396
Volume:
9 |
7 |
Jul 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Digital manufacturing has evolved as a transformational paradigm in contemporary industrial operations, merging cyber-physical systems with conventional manufacturing processes. This study introduces a thorough framework for the implementation of Advanced Learning and Machine Learning (ALML) approaches in digital manufacturing settings. The proposed methodology exhibits substantial enhancements in production efficiency, quality control, and predictive maintenance capabilities. Experimental validation across several manufacturing settings resulted in a 34% decrease in production downtime, a 28% increase in quality metrics, and a 42% improvement in predictive maintenance accuracy. The amalgamation of ALML with digital twin technologies establishes a solid framework for autonomous production systems proficient in real-time optimization and decision-making.
KEYWORDS
Publication Date
7/16/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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