In the Industry 4.0 scenario, collaborative robots have been strongly employed for complex processes and customized production activities. Interaction-based technologies have characterized this approach assisting the operator in several process workflows. In this paper, a haptic-based touch detection strategy is described and tested to assist, in real-time, the operator using a collaborative system in a real industrial scenario, namely the welding process. To assess the performance, two main criteria were analyzed: the 3-Sigma rule and the Hampel identifier. Experimental results showed better performance of the 3-Sigma rule in terms of precision percentage (mean value of 99.9%) and miss rate (mean value of 10%) with respect to the Hampel identifier. Results confirmed the influence of the contamination level related to the dataset. This algorithm adds significant advances to enable the use of light and simple machine learning approaches in real-time applications.

在工业4.0场景中，协作型机器人已被广泛用于复杂的流程和定制的生产活动。基于交互的技术已将这种方法称为特征，可帮助操作员完成多个过程工作流。在本文中，描述并测试了基于触觉的触摸检测策略，以在实际的工业场景（即焊接过程）中实时协助操作员使用协作系统。为了评估性能，分析了两个主要标准：3-Sigma规则和Hampel标识符。实验结果表明，相对于Hampel标识符，3-Sigma规则在精度百分比（均值99.9％）和未命中率（均值10％）方面表现更好。结果证实了与数据集有关的污染水平的影响。