Continuous Analysis within 3D-Printed Structures Using
In-Chamber Sensors
In this research we are investigating how to efficiently and accurately measure per- layer composition and build chamber conditions, in situ, for objects manufactured by selective laser sintering (SLS).
This will make the build process transparent and highlight any defects in the layered construction.
Our investigation is a first step towards integrating sensors into the powder bed and eventually into parts themselves.
In-process materials composition sensing for Nylon selective laser sintering (Nylon SLS): To understand whether low-cost sensors provide sufficient accuracy to monitor per-layer build material composition and build chamber properties during the additive manufacturing (AM) process.
The results of the project will make it possible, for the first time, to have a per-object “digital birth certificate” detailing the entire volumetric / per-layer build process and object parameters, for each Nylon SLS AM part produced.
Monitor powder bed properties (e.g., compaction, flow, temperature distributions) with in-powder sensors. Significant research challenges in energy-efficient signal processing, sensor platform miniaturization, in-situ machine learning on miniature in-powder sensors, and more. Complements existing research on in situ metrology for metal powder bed fusion [3].
Monitor object properties (e.g., stress concentrations) over lifetime of use, with in-object sensors. Will require fundamental to extracting in-situ-
new understanding for in situ sensor signal processing, localization of sensors, and new approaches processed sensor data.
The study with the volunteers suggested four themes: naturalistic as the Cemetery is a natural reserve in the city; socio-historical with the stories of those buried there in the 19th century; weird & wonderful with curiosities and anecdotes; and the volunteers favourite places.
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This will make the build process transparent and highlight any defects in the layered construction.
Our investigation is a first step towards integrating sensors into the powder bed and eventually into parts themselves.
In-process materials composition sensing for Nylon selective laser sintering (Nylon SLS): To understand whether low-cost sensors provide sufficient accuracy to monitor per-layer build material composition and build chamber properties during the additive manufacturing (AM) process.
The results of the project will make it possible, for the first time, to have a per-object “digital birth certificate” detailing the entire volumetric / per-layer build process and object parameters, for each Nylon SLS AM part produced.
Monitor powder bed properties (e.g., compaction, flow, temperature distributions) with in-powder sensors. Significant research challenges in energy-efficient signal processing, sensor platform miniaturization, in-situ machine learning on miniature in-powder sensors, and more. Complements existing research on in situ metrology for metal powder bed fusion [3].
Monitor object properties (e.g., stress concentrations) over lifetime of use, with in-object sensors. Will require fundamental to extracting in-situ-
new understanding for in situ sensor signal processing, localization of sensors, and new approaches processed sensor data.
The study with the volunteers suggested four themes: naturalistic as the Cemetery is a natural reserve in the city; socio-historical with the stories of those buried there in the 19th century; weird & wonderful with curiosities and anecdotes; and the volunteers favourite places.
Project dates
2019
2019
Publications
Research by
Nick Dulake
Daniela Petrelli
Partners & Stakeholders
Phillip Stanley-Marbell, University of Cambridge
Rob Hewson, Imperial College London
Funders
Phillip Stanley-Marbell, University of Cambridge
Rob Hewson, Imperial College London
Funders
