Procedia CIRP

This dataset is related to the article “Battery Tray Fixture Stiffness and Damping Modelling for Surface Quality Prediction “ (https://doi.org/10.1016/j.procir.2023.03.026). The dataset is organised in accordance with the article sections. Some data are used on several sections, thus the following “table of content” of the dataset is given to guide users. The file "00-data_informations.pdf" gives a description of the dataset.

These files are the resources of the figures in this paper.

This study aimed at developing a machine learning algorithm for characterization and estimating the energy consumption of various stages in 3D printing. The machine learning model is developed using a long short-term memory algorithm and trained, validated, and deployed to classify various stages during 3D printing. Furthermore, energy consumption in each stage is estimated based on Simpson’s rule. Characterizing stages is useful for understanding the energy consumption in each stage during the 3D printing process and providing decision support to practitioners in improving the areas of energy and time inefficiencies.

Excel file corresponding to training data and results in conference paper - applied in MATLABImages: Figures 1-4 as in conference paperVideo: 3D plot rotationVideo: Conference presentation

The Integrated Carbon Metrics (ICM) Embodied Carbon Life Cycle Inventory (LCI) Database (ICM Database) provides Australian-specific Carbon Footprint Intensities for around 700 construction and building materials, as well as built environment-related products and processes, based on a hybrid life cycle assessment methodology. The ICM Database is an output of the Integrated Carbon Metrics project (number RP2007) supported by the CRC for Low Carbon Living (CRCLCL).