Deep Fusion for Energy Consumption Prediction in Additive Manufacturing

• DOI: 10.1016/j.procir.2021.11.317
• Publication Date: 2021-11-26T16:11:05Z
• Authors: Fu Hu; Jian Qin; Yixin Li; Ying Liu; Xianfang Sun
• Keywords: Consumption; Manufacturing; Sensor Fusion

Owing to the increasing trend of additive manufacturing (AM) technologies being employed in industry, issue AM energy consumption attracts attention both industry and academia. The systems is affected by various factors. These factors involve features with different dimensions structures which are hard tackle analysis. In this work, a data fusion approach proposed for prediction based on CNN-LSTM (convolutional neural network long short-term memory) model. A case study was conducted an SLS system using methodology, achieving RMSE 8.143 Wh/g prediction.