Title: Detection of workpiece hardness variation from controller signals in milling operations

Authors: Nevzat Bircan Bugdayci; Martin Postel; Konrad Wegener

Addresses: Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48105, USA ' Department of Mechanical and Process Engineering, Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Leonhardstrasse 21, Zurich, 8092, Switzerland ' Department of Mechanical and Process Engineering, Institute of Machine Tools and Manufacturing (IWF), ETH Zurich, Leonhardstrasse 21, Zurich, 8092, Switzerland

Abstract: In this study, an approach to detect and counteract the workpiece material hardness variation in milling operations through the use of controller signals is developed. In order to control the workpiece material hardness variation, steel specimens are subjected to end-quenching, which introduces a controlled hardness gradient to the workpieces. This gradient is then detected from the controller signals without using an additional sensor. It is shown that the process parameters can be adjusted to avoid chatter arising from the increasing hardness, and the productivity can be increased by modifying the process parameters according to the local material hardness.

Keywords: ANNs; artificial neural networks; cutting forces; milling; chatter; process monitoring; hardness variation; controller signals.

DOI: 10.1504/IJMMS.2023.133397

International Journal of Mechatronics and Manufacturing Systems, 2023 Vol.16 No.2/3, pp.301 - 319

Received: 17 Jan 2023
Accepted: 30 Jun 2023
Published online: 14 Sep 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article