Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

Steven Hoedt,El-Houssaine Aghezzaf,Johannes Cottyn,Arno Claeys

doi:10.3390/su12145543

Steven Hoedt, El-Houssaine Aghezzaf + Show 2 more

Open Access

https://doi.org/10.3390/su12145543

Copy DOI

Abstract

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed.

Highlights

Since the raise of mass customization, manual assembly operators are indispensable for many manufacturing companies [1]
As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models
Results show that the MLFCM and the PID model have the greatest potential

Summary

Introduction

Since the raise of mass customization, manual assembly operators are indispensable for many manufacturing companies [1] They provide the necessary flexibility to assembly lines which automated systems cannot. These operators need more support because of the high cognitive load that comes along. Different on-the-job training strategies for supporting operators can be distinguished: providing (digital) work instructions, micro learning moments, expert based training, virtual training, etc. These techniques are highly helpful and can avoid unnecessary cycle time increase or variation and deficiencies on the product’s quality

Objectives

Results

Conclusion