A hybrid machine learning approach for energy consumption prediction in additive manufacturing

Additive manufacturing (AM), as a fast-developing technology for rapid manufacturing, offers a paradigm shift in terms of process flexibility and product customisation, showing great potential for widespread adoption in the industry. In recent years, energy consumption has increasingly attracted attention in both academia and industry due to the increasing demands and applications of AM systems in production. However, AM systems are considered highly complex, consisting of several subsystems, where energy consumption is related to various correlated factors. These factors stem from different sources and typically contain features with various types and dimensions, posing challenges for integration for analysing and modelling. To tackle this issue, a hybrid machine learning (ML) approach that integrates extreme gradient boosting XGBoost) decision tree and density-based spatial clustering of applications with noise (DBSCAN) technique, is proposed to handle such multi-source data with different granularities and structures for energy consumption prediction. In this paper, four different sources, including design, process, working environment, and material, are taken into account. The unstructured data is clustered by DBSCAN so to reduce data dimensionality and combined with handcrafted features into the XGBoost model for energy consumption prediction. A case study was conducted, focusing on the real-world SLS system to demonstrate the effectiveness of the proposed method