Session: 03-11-01: Future of Smart Manufacturing

Paper Number: 143240

143240 - Development of a Geometric Accuracy Machine Vision Application for Metal Castings 

Metal casting is a manufacturing process where molten metal is poured into a mold cavity, which contains the desired shape of the final product, and allowed to solidify. Metal castings provide critical products to several industries such as aerospace, medical, and automobile. In automotive industry metal castings are used in engine blocks, cylinder heads, transmission housings, brake components, and other automotive parts. Components for aircraft engines, landing gear, structural parts, and other aerospace applications are often produced through metal casting. A high level of geometric accuracy is required in high-value casting components to ensure an adequate level of quality.  While traditional methods of measuring critical features on metal castings such as micrometers and gauges have proven effective to determine geometric accuracy, these techniques are often time consuming and require costly equipment and labor time. The development of Industry 4.0 has introduced cyber-physical systems capable of performing these measurements. The aspect of geometry measurement in Industry 4.0 involves the application of advanced digital technologies to enhance and streamline the measurement of geometric features in manufacturing processes specifically through machine vision technique. Machine vision systems utilize cameras and image processing algorithms to capture and analyze visual data from manufactured parts. These systems can accurately measure geometric features such as dimensions, shapes, contours, and surface profiles, enabling real-time inspection and quality control. In this study, machine vision applications have been developed to analyze, measure, and evaluate parts based on the critical geometry of a part. The implementation of machine vision would drastically improve the efficiency and speed of geometric accuracy analysis for the metal casting industry. A machine vision application is introduced and utilized to determine the geometric efficiency of castings components. Aluminum and steel cast components in various geometries and having different geometric features such as curves and angles have been manufactured. The geomerty measurements have been done on as-built components using caliper, coordinate measuring machine, and machine vision technique. The results of the machine vision application are compared to measurements using a coordinate measuring machine and manual measurements using a caliper. Conclusions and future work are then presented based on the results of the comparative analysis of the different measuring methods. The study demonstrated the effectiveness of machine vision technology in accurately measuring geometric features of metal casting components. By comparing machine vision measurements with those obtained from traditional methods, the study validated the accuracy and reliability of machine vision systems in the context of metal casting. The study's findings have the potential to inform industry practitioners and decision-makers about the benefits of adopting machine vision technology for geometric accuracy analysis in metal casting processes.  

Presenting Author: Poojith Chigurupati Georgia Southern University

Presenting Author Biography: Michael Jones is a teaching assistant and researcher at Georgia Southern University. His passion for building solutions and problem-solving lead him to a career in engineering. He is currently working on his master's degree in manufacturing engineering intending to graduate in the spring of 2025. His research field focuses on developing a ranking model for non-destructive testing techniques for foundries in the context of Industry 4.0. His current goal is to graduate with a master's degree in manufacturing engineering and enter the industry. While in the industry, He plan on maintaining contacts at his alma mater to assist in other's education as much as he can. After about 25 years in the industry, He plans on returning to university to earn his doctorate and become a professor.

Authors:

Michael Jones Georgia Southern University
Poojith Chigurupati Georgia Southern University
Hossein Taheri Georgia Southern University