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@article{189828,
        author = {Dr. Keyur V. Parmar},
        title = {Technical Review on Opportunities of Machine Learning in Manufacturing Industries},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1517-1522},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189828},
        abstract = {Machine learning is rapidly transforming modern manufacturing systems by enabling a shift from rigid, labor-intensive operations to intelligent, data-driven production environments. As a core component of artificial intelligence, machine learning allows manufacturing systems to exchange real-time information, analyze operational data, and continuously improve performance through learning mechanisms. This transformation goes beyond incremental efficiency gains; it significantly enhances productivity, minimizes errors, prolongs equipment lifespan, and improves overall system reliability. The practical impact of machine learning is evident in applications such as predictive maintenance, automated defect detection, and digital twin technologies. These approaches facilitate early fault identification, reduce unplanned downtime, minimize material waste, and support stable production workflows. This review examines the major opportunities for integrating machine learning within manufacturing industries, highlighting how intelligent algorithms support informed decision-making, enhance product quality, and optimize resource utilization. The study emphasizes the role of machine learning as a key enabler for smarter, more resilient, and cost-effective manufacturing systems in the era of Industry 4.0.},
        keywords = {Machine Learning, Manufacturing Automation, Predictive Maintenance, Tool Life Estimation, Smart Factory, Industry 4.0},
        month = {January},
        }