Use this URL to cite or link to this record in EThOS:
Title: Reducing the surface deviation of stereolithography components
Author: Reeves, Philip E.
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 1998
Availability of Full Text:
Access through EThOS:
Access through Institution:
The Stereolithography (SL) process has developed into an accurate method of replicating 3D CAD images into tactile objects used for functions such as product evaluation, preproduction testing or as patterns around which tool cavities can be formed. One of the main limitations with the SL process is the surface roughness of parts resulting from the layer manufacturing process. To-date surface roughness has only been reduced using techniques such as additive coating or abrasive finishing. Research has shown however, that these techniques are both detrimental to the accuracy of parts and can prove to increase the cost of SL parts to the end user. The object of this research is to assess the fundamental cause of surface roughness in layer manufacturing and develop techniques that can be used during the build process to produce SL parts with lower surface deviation. To do this a comparison of the most common commercial RP systems was undertaken to identify the attributes causing surface deviation. From these attributes a mathematical model of layer manufactured surface roughness was developed. Parts manufactured using different SL machines were compared to the mathematical model showing a variety of causes in surface deviation not considered in earlier research, such as layer composition, layer profile and the affects of over curing or print-through on surface deviation. The layer edge profile caused by the shape of the scanning laser also has a significant effect on roughness deviation. However, by using a combination of part orientation and optimal shaped meniscus smoothing, the surface deviation of SL parts was found to be reduced by up to 400% on at least 90- degrees of continuous surfaces. A better understanding of layer manufactured surface roughness has now been achieved and a new smooth build algorithm has been developed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TS Manufactures Manufacturing processes Pattern recognition systems Pattern perception Image processing