For most of the twentieth century and well into the early 2000s, quality control in manufacturing was understood in a relatively narrow and reactive way: as the final inspection stage at the end of a production line. Parts were sampled or fully measured using tools like gauges and coordinate measuring machines (CMMs) to determine whether they conformed to specifications. If a bad part was found, it was sorted, scrapped, or reworked, and the inspection team logged the deviations. This traditional

Automated quality control and inspection are transforming how modern industries ensure product quality, replacing many traditional manual methods that often suffer from variability, human error, and slow speeds. The shift toward automation reflects wider digital transformation in manufacturing and regulated sectors, where meeting high accuracy, compliance, and speed demands is essential for competitiveness and customer trust. At its core, automated quality control uses technologies such as machine vision, artificial intelligence (AI), machine learning, IoT sensors, robotics, and data analytics to