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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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Impact Factor : 8.14
Issue per Year : 12
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| Paper Title: | The Role of Machine Learning in Modern Statistical Process Control |
| Authors Name: | Gitanjali Amol Gadhari , S. M. Nimbale , P. M. Bhosale , P. N. Girigosavi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_208374
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| Published Paper Id: | IJRTI2512047 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i12.208374 |
| Abstract: | Recent advances in integrating Machine Learning (ML) with Statistical Process Control (SPC) have significantly expanded capabilities for process monitoring across industrial, environmental, and reliability domains. A synthesis of 33 studies shows three core objectives: improving sensitivity and timeliness beyond traditional Shewhart/EWMA/CUSUM charts, extending SPC to high-dimensional and nonlinear data such as profiles, images, and signals, and enabling predictive decision support for maintenance, capacity planning, and environmental forecasting. Approaches span kernel methods, tree-based models, deep learning, and hybrid systems that embed ML predictions into adaptive control charts. Empirical evidence demonstrates improved defect detection, reduced false negatives, and operational benefits in manufacturing, renewable energy, water treatment, and food safety. Key challenges include data scarcity, model tuning, transferability across processes, uncertainty quantification, and computational load. Cross-domain analysis highlights research needs in statistically calibrated ML–SPC integration, benchmark datasets, and hybrid frameworks. Practical priorities include adaptive SPC policies using reinforcement learning, ML-enhanced MPC for nonlinear/noisy systems, hybrid classifiers for defect detection, and methods for autocorrelated and out-of-distribution signals. Although empirical studies show gains in early detection and decision support, issues remain around data labeling, computational cost, and reliable uncertainty estimation. The review underscores the transformative potential of ML in SPC and the necessity of balancing statistical rigor with real-world deployment requirements. |
| Keywords: | Machine learning, statistical process control, process monitoring, fault detection, control charts, hybrid methods. |
| Cite Article: | "The Role of Machine Learning in Modern Statistical Process Control", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a380-a397, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512047.pdf |
| Downloads: | 000169 |
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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: IJRTI2512047
Registration ID:208374
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i12.208374
Page No: a380-a397 Country: Kolhapur, Maharashtra, India Research Area: Other Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512047 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512047 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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