Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/63161
Title: X-ray assessment of porosity in 3D-printed metal components
Authors: Tan, Shaun Xin Wei
Keywords: DRNTU::Engineering::Materials::Metallic materials::Alloys
DRNTU::Engineering::Materials::Testing of materials
Issue Date: 2015
Abstract: Additive Manufacturing has enjoyed much fervency in the recent years both in the commercial and industrial sectors. Vis-à-vis conventional manufacturing methods of molding, casting, machining and assembly which comes with a hefty price tag, additive manufacturing brings with it immense potential (Engineering, 2013). Additive manufacturing not only allows the production of highly intricate and complex shapes with time and cost savings but also substantial reduction in material wastage or industrial waste commonly observed in conventional manufacturing sectors. With the advent of additive manufacturing technology in the automotive and also the aerospace scene, porosity levels of additively manufactured parts are gaining interest in the Additive Manufacturing community. This is even more so for the aerospace industry as parts are extremely costly to manufacture and any failure of a single unit/component can potentially jeopardize a critical operation and at times even leading to the loss of lives. Porosity levels are extremely detrimental and undesirable when parts produced are subjected to high stress levels during operations. Moreover, variability in the porosity levels of manufactured parts is also an accurate reflection of the health of the Additive Manufacturing Process. In this article, the porosity in 3D-printed metal components will be assessed using both 2D and 3D X-ray imaging. Image processing tools will be identified and implemented to support the assessment of porosity and subsequent attempts will be made to link the determined porosity levels to parameters in the 3D printing process. The functions of the parameters in the scanning process as well as the reconstruction process will be investigated and optimized to provide optimal images to aid further image analysis. This will lead to further cost and time savings.
URI: http://hdl.handle.net/10356/63161
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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