Authors: Sailesh Banrjee , Ansul Chandra , Dinesh Tiwari
Abstract: In modern manufacturing, achieving high-quality products while maintaining efficiency is a critical challenge. Traditional quality control methods rely heavily on human inspection and periodic sampling, which are often timeconsuming and prone to errors. The emergence of smart manufacturing, characterized by interconnected systems, Industrial Internet of Things (IIoT), and advanced analytics, has enabled a shift towards data-driven quality control (DDQC). By leveraging real-time sensor data, machine learning algorithms, and predictive analytics, manufacturers can detect defects early, optimize production processes, and reduce waste. This paper reviews recent developments in data-driven quality control, discussing sensor integration, data analytics techniques, predictive modeling, and case studies in smart manufacturing. Challenges and future research directions are also highlighted to guide the next generation of quality assurance strategies.
Keywords: Data-driven quality control, smart manufacturing, Industrial IoT, predictive analytics, process optimization, machine learning.
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