Today’s business environment sustains mainly those companies committed to a zero-defect policy. Therefore, the prompt detection of rare quality events has become an important issue and create an opportunity for manufacturing companies to move quality standards forward. Machine learning as a tool has been applied in many fields including quality control, which is the task of assuring that all products produced reached a certain standard. On the other hand, industrial robots have been widely applied to the automation of industrial production processes.
This project integrates machine learning with pattern recognition strategy and a robotic system to realize an intelligent part inspection system, in which the main goal is the detection of part defects and different configurations. The project strives to build a successful decision-making procedure and successful implementation of machine learning in part quality control. Overall, this project has demonstrated the potential to vastly improve the efficiency of operations while reducing defects, helping to cut quality, costs and improve customer satisfaction.
The automated system in the project integrates several technology features including the following:
The approaches applied in the project can be widely applied to manufacturing processes to boost the performance of traditional quality methods and potentially move quality standards forward. More specifically, it can be applied in the potential areas such as quality control and management, part feature detection, part configuration, and classification.