Title: Virtual reality training platform for a computer numerically controlled grinding machine tool

Authors: Christian Hirt; Martina Spahni; Yves Kompis; Dominic Jetter; Andreas Kunz

Addresses: Innovation Center Virtual Reality, ETH Zurich, Leonhardstrasse 21, CH-8092, Zurich, Switzerland ' Innovation Center Virtual Reality, ETH Zurich, Leonhardstrasse 21, CH-8092, Zurich, Switzerland ' Innovation Center Virtual Reality, ETH Zurich, Leonhardstrasse 21, CH-8092, Zurich, Switzerland ' Innovation Center Virtual Reality, ETH Zurich, Leonhardstrasse 21, CH-8092, Zurich, Switzerland ' Innovation Center Virtual Reality, ETH Zurich, Leonhardstrasse 21, CH-8092, Zurich, Switzerland

Abstract: In-depth training of machine tool (MT) operators is crucial to avoid machine damage due to faulty operation. However, machine-hours are costly and during the training, the MT is unavailable for regular production purposes. Here, virtual real-size models offer a solution by providing basic operation principles to future operators. In this context, it is yet unknown whether a virtual teaching enhanced by real walking is similar to a real teaching scenario regarding the learning efficiency and long-term memory retention. This paper describes a study comparing the learning efficiency of a virtual training session with traditional instructions on a real MT. The learning success of both training groups is objectively and subjectively assessed on a real MT a week later. In this assessment, the task completion time and the number of errors are recorded. We observed that the virtually taught group slightly outperformed trainees taught in reality regarding both objective measurements.

Keywords: virtual reality training; digital twin; digital manufacturing; machine tool; virtual reality; learning transfer; machine tool training; virtual reality application; industrial application for virtual reality; real walking in virtual reality; immersive virtual reality.

DOI: 10.1504/IJMMS.2021.115460

International Journal of Mechatronics and Manufacturing Systems, 2021 Vol.14 No.1, pp.1 - 17

Received: 26 Jun 2020
Accepted: 25 Oct 2020
Published online: 03 Jun 2021 *

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