Read the full-text of this article for free

Title: Enhancing power skiving tool longevity: the synergy of AI and robotics in manufacturing automation

Authors: Daniel Kiefer; Florian Grimm; Tim Straub; Günter Bitsch; Clemens Van Dinther

Addresses: ESB Business School, Reutlingen University, Alteburgstraße 150, 72762, Reutlingen, Germany ' ESB Business School, Reutlingen University, Alteburgstraße 150, 72762, Reutlingen, Germany ' ESB Business School, Reutlingen University, Alteburgstraße 150, 72762, Reutlingen, Germany ' ESB Business School, Reutlingen University, Alteburgstraße 150, 72762, Reutlingen, Germany ' Karlsruhe Institut of Technology (KIT), Institute of Information Systems and Marketing (IISM), Kaiserstraße 89, 76133, Karlsruhe, Germany

Abstract: In gear manufacturing, the longevity and cost-effectiveness of power skiving tools are essential. This study presents an innovative approach that combines artificial intelligence and robotics in manufacturing automation to prevent tool breakage to improve the remaining useful life (RUL). Using a robotic cell, the system captures six images per tooth from different angles. An unsupervised generative deep learning model approach is used because it is more suitable for industrial application as it can be trained with a small number of defect-free images. It is used in a first step as a classifier and, in a second step, to segment tool wear. This approach promises economic benefits by reducing manual inspection and, through automated tool inspection, detecting wear earlier to prevent tool breakage.

Keywords: power skiving; RUL; remaining useful life; artificial intelligence; robotics; anomaly detection; deep learning; economic efficiency; industrial applications.

DOI: 10.1504/IJMMS.2024.143059

International Journal of Mechatronics and Manufacturing Systems, 2024 Vol.17 No.2, pp.201 - 224

Received: 15 Feb 2024
Accepted: 04 May 2024
Published online: 02 Dec 2024 *