Advanced manufacturing with machine learning: enhancing predictive maintenance, quality control, and process optimization

This study examined the integration of machine learning (ML) techniques into advanced manufacturing processes to enhance predictive maintenance, quality control, and process optimization. The review covers various aspects of advanced manufacturing, including predictive maintenance, quality control, process optimization, supply chain management, inventory control, energy efficiency, human-machine collaboration, data security, scalability, regulatory compliance, and future trends. The results showed that ML algorithms offer capabilities to analyze large volumes of data, including sensor readings, production metrics, and historical performance data, enabling manufacturers to extract Vital understandings and optimize their operations. Predictive maintenance powered by ML facilitates proactive equipment maintenance, reducing downtime and preventing costly breakdowns. ML-based quality control systems enhance product consistency and reliability by detecting defects and deviations in real-time. Additionally, process optimization through ML techniques enables continuous improvement and adaptation to dynamic production environments. However, the adoption of ML in manufacturing also poses challenges related to data privacy, security, and regulatory compliance, which must be addressed to ensure responsible deployment. Overall, the integration of ML into advanced manufacturing holds immense potential for driving efficiency, productivity, and competitiveness in the industry.