This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.

This study addresses the need to develop a sustainable manufacturing process in industrial factories, as the industry desires to remain competitive while it is challenged to adopt eco-friendly practices. A Machine Learning-based software is proposed to deal with the environmental issues, aiming to facilitate the monitoring and analysis of industrial machinery, more exactly of CNC woodworking machines. The focus is on two aspects that determine the environmental impact: energy consumption and toxic emissions, which are used to determine the operating modes of the machines and to detect potential working anomalies. This software consists of a pipeline with two main components: the first one aims to categorize the operating modes of the used machines through time series clustering methods, such as Hidden Markov Models. The second component employs Hidden Markov Models again alongside deep learning based Autoencoders, in order to identify huge deviations within the environmental data. For evaluation, a dataset was collected as a time series from a CNC woodworking machine and then the preprocessed data was further analyzed using the implemented software. The experiments have shown that for anomaly detection in machine operating modes, the Hidden Markov Model outperforms the Autoencoder and state-of-the-art models in terms of efficiency and robustness.

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.