DOI: 10.5061/dryad.stqjq2c9v
title: Detecting anomalies in melt-extruded 3D printed parts using in situ data

description: The data in this repository was gathered from a study to collect
 real-time, in situ data from polymer melt extrusion (ME) 3D printing,
 using a set of sensors to non-destructively identfy printed parts that
 contain defects. The data underwent variance analysis to determine an
 "acceptable" range of filament diameters and non-destructivley
 identify spatial regions of printed cylinders in multi-part builds that
 contain defects. The data consists of two folders and a log meant to track
 procedural adherence for each cylinder printed, the introduced defects, or
 lack thereof, and the pressurization of the part. The "Final Build
 Logs" spreadsheet contains information regarding the two locations of
 the deformations along the 56 meters of filament needed to have no more
 than three anomalous cylinders out of the six printed cylinders, the date
 of the applied deformations to the filament, the initials of the
 researcher applying the deformations, the date that the build was printed
 along with the initials of the researcher who printed it, the part number,
 researcher initials, and date of the pressurization test for each cylinder
 within the build, and a comment describing any deviations from the
 procedure that play into the random error of the statistical analysis for
 each cylinder.  The "Pressure Test Data" folder
 contains a folder for each build. Within these folders are .tdms files
 containing metadata on the measurement system in the header and
 tab-delimeted values for the columns. The columns of interest to the study
 are X_Value, representing time elapsed, and pressure, which we evaluated
 on the values' exponential decay rate. The files also contain
 supplemental information such as a column for temperature (celsius), and
 the flow rate (SLPM Normalized). The "Build Data" folder
 contains in situ data from the sensor-equipped printer in a .csv file, the
 STL file for the build, the gcode file from the applied slicer settings,
 the AMRP file stores printer settings, and a .pdf file for the setup
 specifications.
description: The methodology involved an introduction of a material defect by
 varying the filament diameter of PETG filament and measuring the filament
 diameter values for the length of filament needed (9m) for a full cylinder
 three times before and after the defect was introduced using two
 orthogonally-positioned hall-effect sensors and a digital rotary encoder
 used to track the position along the filament of the cross-sectional
 diameter measurements. The data collected from the aforementioned process
 is not included in this repository, but the process preceded the creation
 of a defect that was both detectable in the in situ data and resulted in a
 printed cylinder with a statistically significant increase in its pressure
 decay rate. Following this, six cylinders were printed
 on the same build plate and real-time in situ data from various sensors
 equipped on the desktop printer were gathered with the objective to map
 sensor data onto 3D coordinates to identify on which parts the identified
 anomalies landed. Determining the effect of filament diameter on the print
 quality involved pressurizing the printed cylinders with the introduced
 defect to 1 PSI, then evaluating for statistically signifcant increases in
 pressure decay rate of the cylinders.  As multiple
 parts were printed together layer-by-layer (opposed to part-after-part),
 the size of the anomaly was limited to be certain that no more than three
 parts out of six would contain anomalies. 
description: # Detecting anomalies in melt-extruded 3D printed parts using in situ data
 The AMNOW contest objective was to demonstrate evidence of the benefits of
 using *in-situ* data to enable users of melt extrusion to evaluate the
 quality of a part without expensive or destructive testing, increase the
 visibility of the power in using in situ data to discover anomalies and
 trends in melt extruded parts, make feasible the possibilities of
 simulating development and discussion of artificial intelligence
 algorithms for finding anomalies in in situ data sets within the melt
 extrusion 3D printing community, and create motivation for industry
 machine providers to incorporate data collection and monitoring features
 in their products. Improved understanding of critical parameters in the 3D
 printing process, such as filament diameter and filament feed slippage
 opens the door for developing corrective intervention and improving the
 success rate of 3D printing processes. The work in collecting the data in
 this repository inspired the publication of *Non-destructive evaluation of
 melt-extruded part quality using in situ data* in the Rapid Prototyping
 Journal of the Emerald Publishing Group.  ## Description of the data and
 file structure The dataset consists of files from the cylinder
 pressurization tests, as well as the In Situ data from the printer
 equipped with sensors, and a log tracking the details of the procedural
 adherence for the contest data. Any file names with negative part numbers
 in the Pressure Test data folder (e.g -0.01.tdms) were recorded and saved
 as part of the process to fine tune the procedure and track any mistakes
 made before official contest data was collected.  None of the files in the
 builds from "Build Data" contains these parts, as these were all
 of the builds used for official contest data. The "Pressure Test
 Data" folder contained builds that were not submitted to the contest
 participants, and thus contained some parts with the negative sign
 indicative of prior mishaps before collecting the official contest data.
 The pressurization data is in .tdms file format, which is designed for
 data streaming applications and contains both metadata of the measurement
 system and the raw data in its tab-delimited column format. These files
 can easily be opened as a .csv in a text editor like notepad.  The files
 in each folder of the compressed "Build Data" contains the STL
 files of each build (6 cylinders), the gcode for each build after applying
 the slicer settings, the in situ data from the sensor-equipped desktop
 printer, an AMRP file, which is just a plain text file, that contains the
 slicer settings, and the setup specifications, which describe the hardware
 used, slicing parameters, build layout, and sensor algorithms. The
 redundancy of the setup specifications and the AMRP file in each build
 folder ensures consistency in how data was acquired between builds for any
 party interested in reproducibility of the data in the future.  For users
 unfamiliar with the described file structure, the STL file format, which
 stands for stereolithography, approximates the surfaces of a solid model
 with triangles. The gcode tool path files are plain text files with each
 line containing a specific command for the printer to control important
 parameters such as stepper motor function, temperatures, etc. The AMRP
 file holds printer settings, and the .csv file contains the in situ sensor
 data streamed from the printer. ## Code/Software The supporting software
 used for the AMNOW contest can be found in the repository below. The
 AnomalyDetectionLogger folder in the repository below contains the C++
 code uploaded to an Arduino UNO for measuring the filament diameter along
 the length of filament that was used for the internal validation of the
 filament deformation procedure. Other critical information for code
 reusability can be found in the README of the repository below.  *
 [https://github.com/IkonOne/OctoPrint-SensorDataVis](https://github.com/IkonOne/OctoPrint-SensorDataVis)