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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM51396

Digital Manufacturing using Advanced Learning and Machine Learning (ALML): A Comprehensive Framework for Industry 4.0 Implementation

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM51396

Volume:

9 |

7 |

Jul 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM51396

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.

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Authors

1MM

1Umesh Manikrao Mohod

2JAW

2 Jeevkumar Arjunrao Wakode

3SSD

3 Sagar Shrikrishna Dharamkar

MJSS

Mohammed Juned Shaikh Shabbir

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Abstract

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

Digital Manufacturing Advanced Learning Machine Learning Industry 4.0 Cyber-Physical Systems Digital Twin Predictive Maintenance

Cite This Article

1Umesh Manikrao Mohod, 2 Jeevkumar Arjunrao Wakode, 3 Sagar Shrikrishna Dharamkar, Mohammed Juned Shaikh Shabbir, "Digital Manufacturing using Advanced Learning and Machine Learning (ALML): A Comprehensive Framework for Industry 4.0 Implementation," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 7, Jul 2025. https://doi.org/10.55041/IJSREM51396

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Publication Date

7/16/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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