Automated Quality Inspection: An Embedded Machine Vision System for Process Optimization

In this new age of the fourth industrial revolution, the manufacturing market has become more competitive with state-of-the-art technologies. The new technologies are making the manufacturing process more robust and efficient. Process automation is one of the critical areas where industries can give more emphasis to face the growing challenges of the market. Here systems embedded with machine vision can play a pivotal role in quality assurance and efficient process automation. They are also reducing the cost of production significantly in all sorts of manufacturing processes. In this project, an open-source Linux-operated portable credit card-sized computer is used along with its camera module for computational purposes. The camera captures images of objects and passes the information to the computer. The computer analyzes the captured images through a machine learning algorithm and detects unwanted objects to remove them from the production line as part of quality inspection and sorting. The object is defined by its color. Objects having color defined as defective are removed from the conveyor. This study will guide the implementation of automated color error detection-based systems to sort faulty items in many fields where continuous evaluation and quality improvement are demanded.