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Title: Finite element analysis of curl development in the selective laser sintering process
Author: Jamal, Naim Musa
ISNI:       0000 0001 3588 6540
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2001
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Selective laser sintering (SLS) is a rapid prototyping process, which operates by using a laser to locally heat an area within a layer of powder material, causing it to fuse together, creating a thin cross-section of solid material. 3D shapes are built by repeatedly depositing a layer of fresh powder on top of the cross-section and then locally heating it, causing it to fuse together and to the layer beneath. However, during SLS processing, temperature differences that exist in different regions of the fabricated parts lead to uneven shrinkages. The shrinkages cause surfaces in the part, which are intended to be flat, to exhibit a curved profile; a phenomenon termed curl. The development of curl is highly influenced by the SLS machine parameters selected in fabrication. The production of geometrically acceptable parts involves numerous fabrication trials before the optimum machine parameters can be found. The procedure can be time consuming and expensive. The aim of the work presented in this thesis was to develop finite element models for the purpose of predicting curl in SLS fabricated polycarbonate parts. The ultimate goal was to use the models to estimate the optimum SLS machine parameters for the physical fabrication of geometrically acceptable parts, produced in any material, and therefore avoid the costly and time consuming process of using SLS machines for experimental purposes. The prediction of curl was made through heat transfer and stress finite element models that were both coupled using the sequentially coupled thermal-stress analysis technique. Experimental work was carried out to measure material properties used as input to the models and to validate results predicted. The sensitivity of curl predicted to assumptions considered in the heat and stress models was introduced, and the assumptions highly influencing the accuracy of curl predictions were identified.
Supervisor: Dalgarno, K. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Industrial processes & manufacturing processes