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Voxel-based sensor placement for additive manufacturing applications

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Abstract

The manufacturing sector is experiencing a transformation to Industry 4.0. The authors are researching 3D printable sensors to measure operating conditions like wear, strain and temperature, as part of a project to develop a large-scale multi-material extrusion 3D printer to print a Gravity Separation Spiral—a piece of mining equipment that separates minerals from slurry. This paper proposes a sensor placement methodology for placing embedded sensors in large 3D printed objects. Voxels, the 3D equivalent of 2D pixels, are used to discretise the object and an optimisation routine optimally positions the 3D printable sensors into a 3D printed object. The optimisation objectives that are used during sensor placement include the information gain from the sensor, the ability to print the sensor using a robot, and the ability to discourage sensors being placed in important structural locations by penalising these voxels. Finite element analysis is employed to measure the information gain, while the robot arm’s manipulability measures the capability to print at each voxel location. The objectives are integrated using 3D kernels, which are represented by voxels shaped in the size of the sensor and different weights related to the intricate traces that need to be printed. Using a weighted objective function the best locations are chosen. A simulation environment has been developed to simulate the printing and Matlab is used to do the voxel-based calculations to identify the ideal location for sensor placement.

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Acknowledgements

This research is supported by UTS, The Commonwealth of Australia’s Department of Industry, Innovation and Science (Innovative Manufacturing CRC Ltd) and Downer, via its subsidiary Mineral Technologies. Thank you to Rapido, in particular, Hervé Harvard and Michael Behrens for establishing this overall research activity and leading the overall R&D engineering project. Authors acknowledge that Jordan Henry created the simulation environment of the printer according to the actual dimensions. Thank you to UTS:RI/CAS for providing the required resources to carry out this research.

Funding

This research is supported by UTS, The Commonwealth of Australiaś Department of Industry, Innovation and Science (Innovative Manufacturing CRC Ltd) and Downer, via its subsidiary Mineral Technologies.

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  1. Centre for Autonomous Systems, University of Technology Sydney, Ultimo, Australia

    Nuwan Munasinghe, Thomas Romeijn & Gavin Paul

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  1. Nuwan Munasinghe
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  2. Thomas Romeijn
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Correspondence to Nuwan Munasinghe.

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This research is supported by UTS, in particular, Rapido; The Commonwealth of Australia’s Department of Industry, Innovation and Science (Innovative Manufacturing CRC Ltd); and Downer, via its subsidiary Mineral Technologies.

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Munasinghe, N., Romeijn, T. & Paul, G. Voxel-based sensor placement for additive manufacturing applications. J Intell Manuf 34, 739–751 (2023). https://doi.org/10.1007/s10845-021-01823-x

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