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.

中文翻译：

---

中断后手动装配任务周期时间预测的学习遗忘模型的实时实现

工业 4.0 提供了来自工作场所的巨大数据潜力。对于制造公司而言，这些数据对于支持装配操作员非常有用。在文献中，可以找到很多贡献，这些模型可以描述手动组装操作的学习和遗忘效果。在这项研究中，比较了不同的现有模型，以预测休息后的循环时间。由于这些模型不是为实时预测目的而创建的，因此提出了一些调整以提高模型的鲁棒性和效率。结果表明，MLFCM（修正的学习-遗忘曲线模型）和PID（功率积分扩散）模型具有最大的潜力。将进行进一步研究以测试这两种模型并实施上下文因素。此外，由于这些模型仅考虑一项固定的重复性任务，因此它们不针对混合模型装配操作。执行每个组装任务对其他任务执行的学习和遗忘效果因任务之间的工作相似性而异。列出了实现这种工作相似性的进一步研究机会。