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 dataset contains all experimental data that is shown within the paper "Anisotropic Damping and Stiffness of laminated steel parts using adhesive bonding - an empirical influence study".
Bending beams manufactured by Layer Laminated Manufacturing (LLM) were investigated in order to analyze the influence of different manufacturing parameters on the resulting part properties. For each investigated parameter combination, three redundant specimens are manufactured and examined.
Experimental modal analysis (EMA) and static bending test are used as examination methods in order to obtain the eigenfrequencies, the modal dampings and the bending stiffness of the beams.
The data is summarized within the file data.xlsx, which contains the following spreadsheets:

• EMA: Eigenfrequencies and modal dampings of the first three eigenmodes for all investigated specimens, identified by experimental modal analysis.
• EMA - mean: Mean eigenfrequencies and modal dampings of the first three eigenmodes for all investigated parameter combinations, averaged over three redundant specimens that were manufactured for each parameter combination
• bending: Displacements for three different applied weights (5, 10, 15 kg) and the resulting bending stiffness for each specimen
• bending - mean: Mean displacements for the three different weights and the resulting bending stiffness for each parameter combination.
• ratios normal-parallel: As a anisotropy measure, all part properties normal and parallel to the slicing direction are set in relation to each other.
• parameter combinations: Nomenclature for the parameter combinations used within the study

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).