Towards a Study Protocol: A Data-Driven Workflow to Identify Error Sources in Direct Ink Write Mechatronics

Hein Htet Aung, Case Western Reserve University
Quynh D. Tran, Case Western Reserve University
Pawan Tripathi
Roger H. French, Case Western Reserve University
Laura S. Bruckman, Case Western Reserve University

Abstract

Using Direct Ink Write (DIW) technology in a rapid and large-scale production requires reliable quality control for printed parts. Data streams generated during printing, such as print mechatronics, are massive and diverse which impedes extracting insights. In our study protocol approach, we developed a data-driven workflow to understand the behavior of sensor-measured X- and Y-axes positional errors with process parameters, such as print velocity and velocity control. We uncovered patterns showing that instantaneous changes in the velocity, when the build platform accelerates and decelerates, largely influence the positional errors, especially in the X-axis due to the hardware architecture. Since DIW systems share similar mechatronic inputs and outputs, our study protocol approach is broadly applicable and scalable across multiple systems. Graphical abstract: (Figure presented.)

 

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