Eventos Anais de eventos
COBEM 2021
26th International Congress of Mechanical Engineering
GETTER: An Innovation Applied to Manufacturing Environments
Submission Author:
Roger Tarso , MG , Brazil
Co-Authors:
Roger Tarso, Lucas Takara, Guilherme van der Laars Ribeiro, Rufo André Paganini
Presenter: Guilherme van der Laars Ribeiro
doi://10.26678/ABCM.COBEM2021.COB2021-1125
Abstract
Industry 4.0 term originated in 2011 from a German government high-tech strategy project. This expression was first noticed publicly in the industrial trade fair Hannover Messe in Germany in the same year. This information technology-based revolution has ignited a vision about a new upcoming industrial revolution that would consist in promoting intensive computerization of manufacturing. Since then, disruptive and emerging new technologies such as Artificial Intelligence, Computer Vision, and Internet of Things have been successfully applied into manufacturing environments providing tools to many industries to improve their operating efficiency, productivity, safety, and quality. Open Source Computer Vision Library (OpenCV) is an open-source computer vision and machine learning library of programming functions mainly aimed at image processing, video capture, and analysis including features such as face and object detection. Extensively used in companies, research groups, and by governmental bodies, it includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. OpenPose is an efficient method for multi-person pose estimation with competitive performance on multiple public benchmarks. This technique detects real-time human body, hand, facial, and foot key points on single images. Given such definitions, this article proposes an application of the GETTER Artificial Intelligence approach in the manufacturing environment. Having such advanced computer vision libraries at its core, this solution is guided by Lean and World Class Manufacturing principles. Through extensive use of heatmap images, production cycle time analysis charts, workers movement (spaghetti chart), and displacement amount, GETTER provides insightful information that improves the operator’s life quality, health, and safety by supporting their productivity enhancement. Besides, the solution provides the complete worker’s postural screening analysis, having as scientific basis its respective clients' ergonomics protocol as European Avalanche Warning Services adopted. The relevant generated data is presented in operational dashboards, showing strategic key performance indicators to support Data-Driven decisions and Business Intelligence directions.
Keywords
Lean Production, Manufacturing, Productivity, Artificial Intelligence, ergonomics, industry 4.0
