Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Feature-level data fusion for energy consumption analytics in additive manufacturing

Hu, Fu, Liu, Ying, Qin, Jian, Sun, Xianfang and Witherell, Paul 2020. Feature-level data fusion for energy consumption analytics in additive manufacturing. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual, 20-24 August 2020.

[img]
Preview
PDF - Accepted Post-Print Version
Download (460kB) | Preview

Abstract

The issue of Additive Manufacturing (AM) energy consumption attracts attention in both industry and academia, as the increasing trend of AM technologies being employed in the manufacturing industry. It is crucial to analyze, understand and manage the energy consumption of AM for better efficiency and sustainability. The energy consumption of AM systems is related to many correlated attributes in different phases of an AM process. Existing studies focus mainly on analyzing the impacts of different processing and material attributes, while factors related to design and working environment have not been paid enough attention to. Such factors involve features with various dimensions and nested structures that are difficult to handle in the analysis. To tackle these issues, a feature-level data fusion approach is proposed to integrate heterogeneous data in order to build an AM energy consumption model to uncover the energy-relevant information and knowledge. A case study using real-world data collected from a selective laser sintering (SLS) system is presented to validate the proposed approach, and the results indicate that the fusion strategy achieves better performances on energy consumption prediction than the individual ones. Based on the analysis of feature importance, the geometry and temperature relevant features are found to have significant impacts on AM energy consumption.

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Computer Science & Informatics
Engineering
Subjects: T Technology > TJ Mechanical engineering and machinery
Date of First Compliant Deposit: 4 June 2020
Last Modified: 14 Jul 2020 09:26
URI: http://orca.cf.ac.uk/id/eprint/132165

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics