Application of the learning from errors principle in tufting machines

TL;DR

This paper introduces a safe, scalable AR-based training system for tufting machine operators, integrating digital twins and Petri Net models to enhance learning from errors without risking safety or equipment.

Contribution

It presents a novel hybrid digital twin and AR framework utilizing Petri Nets for effective, safe operator training in industrial tufting machines.

Findings

Enhanced safety in operator training

Accelerated skill acquisition through AR visualization

Formal process modeling with Petri Nets

Abstract

The principle of learning from errors is pedagogically powerful but often impractical in industrial settings due to risks to safety and equipment. This paper presents an integrated training approach specifically designed for tufting machine operators. It uses hybrid digital twins, augmented reality (AR), and Petri Net-based modelling to apply the learning from errors principle effectively. Operator actions and errors are simulated via experimentable digital twins (EDTs), and the consequences of errors are visualized in AR, enabling safe, experiential learning. A Petri Net model formally represents the process, including typical faults and recovery paths, and is implemented in VEROSIM using SOML++. This hybrid framework provides a scalable foundation for AR-guided training systems that reduce risk and accelerate skill acquisition.