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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 10 | Issue 12
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19463
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
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| Paper Title: | Machine Learning for Energy Optimization in Smart factories |
| Authors Name: | Poornima K , Rasika K S , Mohith D , Nigharika S , Rajesh I |
| Download E-Certificate: | Download |
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IJRTI_202128
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| Published Paper Id: | IJRTI2504053 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | The advent of smart factories, driven by Industry 4.0, has transformed traditional manufacturing by integrating advanced technologies such as IoT, AI, and real-time data analytics. This shift enables seamless automation, enhanced decision-making, and improved efficiency in production processes. Existing manufacturing systems rely on conventional monitoring methods, often leading to inefficiencies, unplanned downtimes, and reactive maintenance approaches. Traditional factories struggle with limited real-time insights, making it challenging to optimize resource utilization and predict failures. To address these challenges, smart factory solutions incorporating machine monitoring have been proposed to enhance operational efficiency and productivity. By leveraging interconnected sensors, automated data collection, and predictive analytics, manufacturers can track equipment performance in real time, minimize downtime, and proactively address maintenance needs. These improvements lead to optimized resource allocation, reduced operational costs, and enhanced product quality. Additionally, smart monitoring ensures compliance with safety standards, supports agile decision-making, and improves overall sustainability. The integration of IoT-enabled devices and intelligent analytics enables factories to adapt to dynamic market demands with greater flexibility. As a result, smart factories position themselves at the forefront of industrial innovation, offering a competitive edge by driving efficiency, cost savings, and improved production outcomes. |
| Keywords: | Keywords— Smart factories, Industry 4.0, Real-time utilization, Predictive analytics, Productivity, Safety standards, Operational Costs |
| Cite Article: | "Machine Learning for Energy Optimization in Smart factories", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a367-a372, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504053.pdf |
| Downloads: | 000341 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504053
Registration ID:202128
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a367-a372 Country: Namakkal, Tamilnadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504053 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504053 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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