Multisided Quality Inspection in Smart Manufacturing Systems

Abstract

As technology advances so does its adoption in industry and our everyday lives. We use technology to better our daily tasks whether at home or in the workplace. In the workplace there are many benefits of implementing new technologies to improve processes and products while at the same time being cautious about associated costs. Costs associated with incorporating new digital technologies include but are not limited to hardware and software, system integration, testing, and employee training. Now that we have entered the era of the Fourth Industrial Revolution, there are many opportunities for improvements in manufacturing that involve Smart Digital Technologies. Quality Inspection (QI) is a common process in the manufacturing industry. This process serves to identify and locate defects based on preassigned product features. It can be used to find defect causes during the manufacturing process. QI is an established process that is often conducted manually. However, with the opportunity afforded by core Industry 4.0 technologies like Machine Learning (ML), the QI process is increasingly automated. A literature review on the topic of Image Based QI shows that today the designs of fixed single camera based QI systems inspect only one side of a product. In case multiple sides of a product need to be inspected, more datapoints are acquired via either multiple cameras or moving the camera system which affects the length of process, task complexity, cost, and data storage. This thesis explores the application of a mirror system in the design of a QI station to overcome this current limitation and potentially lower the number of processing tasks and increase the inspection ability of one automated station. The images acquired for the QI experiment present a simultaneous, multi-side (five sides) view of a product with similar accuracy. The experiment is set up with three major parts: 1) a physical setup of a mirror-enhanced camera system for part QI; 2) a set of test parts with two types of simulated common surface defects (additional material and scratches) to create the data set for analysis, and 3) a Python-based ML model to analyze the data set and evaluate the system’s ability to correctly identify errors on multiple sides of the specimens. The results show that the setup successfully identified the majority of surface errors on all sides of the part with an accuracy of at least 85% when applying an Artificial Neural Network based supervised ML classification approach. This