Polymer Additive Manufacturing JSON-LD

Hein Htet Aung, Roger H. French, Laura S. Bruckman


Polymer AM JSON-LD Description

In Polymer Additive Manufacturing (AM) JSON-LD template, the sample information (sample id) is related to printing parameters to produce the part, the build geometry of the part, material properties of the part and the characterization techniques (in-situ and ex-situ) performed on the part.

More detailed structure is shown in the schema diagram below.

Creating JSON-LD for Polymer AM in R


# An example data frame for polymer AM
polymerAM_data <- data.frame(
  'sampleID' = c('sa12345', 'sa24682'),
  'printMethod' = c('FDM', 'FDM'),
  'manufacturer' = c('A', 'B'),
  'material' = c('ABS', 'PLA'),
  'surfaceRoughness' = c(10, 5),
  'flowRate' = c(8, 12)

# This will generate json-ld files for the example data.
polymer_output <- fairify_data(polymerAM_data, domain = 'PolymerAM', saveLocal = TRUE)

Creating JSON-LD for Polymer AM in Python

from fairmaterials.fairify_data import *
import pandas as pd

# Create an example data frame for Polymer AM
data = pd.DataFrame(
  'sampleID' = ['sa12345', 'sa24682'],
  'printMethod' = ['FDM', 'FDM'],
  'manufacturer' = ['A', 'B'],
  'material' = ['ABS', 'PLA'],
  'surfaceRoughness' = [10, 5],
  'flowRate' = c[8, 12]

# This will generate JSON-LD file for the example data
output <- fairify_data(data, domain = 'PolymerAM')

Polymer AM schema diagram

Polymer AM schema diagram

Polymer AM schema diagram


This material is based upon work supported by the Department of Energy (National Nuclear Security Administration) under Award Number(s) DE-NA0004104.