Big data configuration for Industry 4.0 Robotics

This project relates to the configuration of data generated by several robotic arms during the manufacture of an Airbus A350. Robots generate an enormous amount of data during these type of processes, and the data is crucial for future analysis of how to improve the process. In order to be analyzed, the data must first be annotated. The annotation is currently a manual task, which is tedious and time-consuming. The objective of the project is to create a tool which can be used for automatic annotation and configuration of the data output from the manufacturing process. The proposed data configuration tool could take one of a few different forms. In the simpler form, the tool might assist an operator in configuring the data in a smart way. For example, instead of having to decide how to store each individual dataset, the tool might be a user interface that suggests similarly configuring a large amount of data that comes from the same source. A more advanced assistance tool might configure the data automatically, and could learn to adjust the configuration based on operator corrections.

Faculty Supervisor:

Homayoun Najjaran

Student:

Partner:

Universität Augsburg

Discipline:

Engineering

Sector:

Advanced Manufacturing; Aerospace

University:

The University of British Columbia - Okanagan

Program: