Machine Learning Optimization for Robotic Welding Parametrization

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Couto, T (Author) Other; Pedro Luís Cerqueira Gomes da Costa (Author) FEUP; Malaca, P (Author) Other; Tavares, P (Author) Other

Conference proceedings International

Title: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

Pages: 137-142

Indexing

ISI Web of Knowledge - 0 Citations

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Other information

Authenticus ID: P-00V-1VX

DOI: 10.1109/icarsc52212.2021.9429817

Abstract (EN): Welding physics is complex, and therefore the welding parametrization is time-consuming. In manual welding, the "hand", the experience, and the best sensor of all (the eyes) can compensate for the difficulties in finding the right settings (welding parameters, robot posture, speed,...) for a specific weld seam. In robotic welding the robotic arm and the sensors are limited, and the parametrization time escalates. This work aims to develop a flexible welding robotized system, through the introduction of (knowledge-based) decision support for welding parametrization in an advanced robotic work cell, in combination with advanced (collision-free) offline programming and advanced sensing. By selecting a specific application area, structural steel, this work will reduce the degree of complexity during the development, paving the way for the introduction of knowledge-based welding in the robotic arc welding sector.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6