Operator's Experience-Level Classification Based on Micro-Assembly Steps for Smart Factories

Abstract

Tracking assembly lines in manufacturing to provide assistance is one of the essential requirement in Smart Industry. Nevertheless, these given assistance and guidelines should be offered to operators when needed. Otherwise, it can be deemed patronising in some cases, e.g., experienced operators may require less assistance than junior operators. Therefore, to provide tailored guidance and assistance in assembly lines, the operators' experience-level should be classified at different levels. In this paper, we introduce three scenarios to achieve the classification of operators expert levels in a real case study (micro-step time-series data from a factory assembly line). We implement a Convolutional Neural Network model for time-series classification, using 5 convolutional layers, max-pooling layers and 5 dense layers with dropout to avoid overfitting. We compare the results of our approach with the ground truth and also with other classifiers as K-nearest neighbours, Random Forest and Naive Bayes classifier. Results show an accuracy of 77 to 98% and 71 to 88% for two of considered scenarios.