Veera Harsha Vardhan Jilludimudi, Daniel Zhou, Eric Rubstov, Alexander Gonzalez, Will Daknis, Erin Gunn and David Prawel
This study aims to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing and use only the collected data to non-destructively identify printed parts that…
Abstract
Purpose
This study aims to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing and use only the collected data to non-destructively identify printed parts that contain defects.
Design/methodology/approach
A set of sensors was created to collect real-time, in situ data from polymer ME 3D printing. A variance analysis was completed to identify an “acceptable” range for filament diameter on a popular desktop 3D printer. These data were used as the basis of a quality evaluation process to non-destructively identify spatial regions of printed parts in multi-part builds that contain defects.
Findings
Anomalous parts were correctly identified non-destructively using only in situ collected data.
Research limitations/implications
This methodology was developed by varying the filament diameter, one of the most common reasons for print failure in ME. Numerous other printing parameters are known to create faults in melt extruded parts, and this methodology can be extended to analyze other parameters.
Originality/value
To the best of the authors’ knowledge, this is the first report of a non-destructive evaluation of 3D-printed part quality using only in situ data in ME. The value is in improving part quality and reliability in ME, thereby reducing 3D printing part errors, plastic waste and the associated cost of time and material.