Integrated Sensor-based Condition Monitoring in Advanced Manufactured 3D-Printed Equipment

Field	Value	Language
dc.contributor.author	Munasinghe, Nuwan https://orcid.org/0000-0002-2209-7371
dc.date.accessioned	2022-02-22T06:17:40Z
dc.date.available	2022-02-22T06:17:40Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/10453/154774
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The future vision of advanced manufacturing is one of connected smart manufacturing equipment that takes advantage of data capture and analysis systems to optimise operations. Australia's manufacturing sector is a vital component of the economy. A key to progress is the application of advanced manufacturing technologies, systems and processes. Additive Manufacturing (AM), also known as 3D printing, is an advanced manufacturing technology that plays a significant role in the fourth industrial revolution (Industry 4.0). In recent years, manufacturers in the mining sector have been looking to leverage advanced manufacturing technologies to help improve productivity, efficiency and safety. Gravity Separation Spirals (GSS) are vital to mineral processing operations in the mining sector for separating mineral-rich slurry into its different density components, particularly when high throughput is required. GSS have traditionally been manufactured in moulds, using a manual process that is subject to numerous inherent drawbacks, including significant tooling costs, limited customisation, and the risk of worker exposure to hazardous materials. A multi-partner project is underway to develop a bespoke 3D printer to print an upgraded and customisable GSS. By embedding Internet of Things sensors inside the GSS, it is possible to remotely determine the operation conditions, perform predictive maintenance, and use the collected data to optimise the production output. The research in this thesis is focused on developing the required sensors that can be embedded in the printed spiral. These sensors can be either 3D printed or conventional sensors. Research also focuses on the sensor placement problem to determine the ideal location to place sensors so as to maximise the information gain whilst simultaneously considering the 3D printing process, and the required structural integrity. In order to print the structure with the sensors inline, a novel radial slicing algorithm has been devised to slice helical objects, along with a path planning algorithm for radial robot-based 3D printing. Experiments using conductive filament have shown how the devised 3D printed sensors can be used to measure, with acceptable accuracy, the required physical quantities, such as strain, temperature, and vibration. The design of the traditional 3D strain sensor has been improved to compensate for temperature changes. A partial pipe flow meter has been developed based on ultrasonic velocity measurement and capacitance level sensing. Experimental results showed that this sensor performed better than a conventional flow meter. The devised voxel-based sensor placement approach has been shown to propose ideal locations that consider various competing objectives.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/154774/5/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Integrated Sensor-based Condition Monitoring in Advanced Manufactured 3D-Printed Equipment	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-11-03T00:00:00+1000

Abstract:

The future vision of advanced manufacturing is one of connected smart manufacturing equipment that takes advantage of data capture and analysis systems to optimise operations. Australia's manufacturing sector is a vital component of the economy. A key to progress is the application of advanced manufacturing technologies, systems and processes. Additive Manufacturing (AM), also known as 3D printing, is an advanced manufacturing technology that plays a significant role in the fourth industrial revolution (Industry 4.0). In recent years, manufacturers in the mining sector have been looking to leverage advanced manufacturing technologies to help improve productivity, efficiency and safety. Gravity Separation Spirals (GSS) are vital to mineral processing operations in the mining sector for separating mineral-rich slurry into its different density components, particularly when high throughput is required. GSS have traditionally been manufactured in moulds, using a manual process that is subject to numerous inherent drawbacks, including significant tooling costs, limited customisation, and the risk of worker exposure to hazardous materials. A multi-partner project is underway to develop a bespoke 3D printer to print an upgraded and customisable GSS. By embedding Internet of Things sensors inside the GSS, it is possible to remotely determine the operation conditions, perform predictive maintenance, and use the collected data to optimise the production output. The research in this thesis is focused on developing the required sensors that can be embedded in the printed spiral. These sensors can be either 3D printed or conventional sensors. Research also focuses on the sensor placement problem to determine the ideal location to place sensors so as to maximise the information gain whilst simultaneously considering the 3D printing process, and the required structural integrity. In order to print the structure with the sensors inline, a novel radial slicing algorithm has been devised to slice helical objects, along with a path planning algorithm for radial robot-based 3D printing. Experiments using conductive filament have shown how the devised 3D printed sensors can be used to measure, with acceptable accuracy, the required physical quantities, such as strain, temperature, and vibration. The design of the traditional 3D strain sensor has been improved to compensate for temperature changes. A partial pipe flow meter has been developed based on ultrasonic velocity measurement and capacitance level sensing. Experimental results showed that this sensor performed better than a conventional flow meter. The devised voxel-based sensor placement approach has been shown to propose ideal locations that consider various competing objectives.

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