Quality Assessment Of 3d-Printed Plastic Parts Using Image Analysis Technique
Three dimensional (3D) printing or additive manufacturing has arisen to become an important technology that change the manufacturing industry. 3D printing has several attractive points compared to conventional manufacturing (eg. injection moulding), such as production flexibility and part flexibil...
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| Main Author: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2018
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| Online Access: | http://eprints.usm.my/53239/1/Quality%20Assessment%20Of%203d-Printed%20Plastic%20Parts%20Using%20Image%20Analysis%20Technique_Chim%20Jia%20Wen_B1_2018.pdf http://eprints.usm.my/53239/ |
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| Summary: | Three dimensional (3D) printing or additive manufacturing has arisen to become an important technology that change the manufacturing industry. 3D printing has several attractive points compared to conventional manufacturing (eg. injection
moulding), such as production flexibility and part flexibility. However, 3D printing has limitation in terms of product size, build time and materials availability.
Nonetheless, the impact of 3D printing on society has been found especially in life sciences field, where customized product for individual is needed. Ethylene Vinyl
Acetate (EVA) is one of the material that is reported safe for medical application and hence make it a good choice of material to be studied for 3D printing. In this project,
attempt to 3D print EVA using UP Plus 2 3D printer was done. However, this attempt was not successful due to variation in viscosity of the chosen EVA grade during the printing process. Hence, further studies on the quality assessment of 3D printed part was done using Acrylonitrile Butadiene Styrene (ABS) filament. The experiment was designed using 2k factorial method. The effect of layer thickness, raster angle and platform temperature on the surface roughness of printed parts were studied. The evaluation of surface roughness was done using an image analysis software, ImageJ
using a plugin called SurfCharJ. The effect of layer thickness on the surface roughness was reported to be the most significant and is the only one that is statistically
significant. The interaction between platform temperature and layer thickness has the second place of significance. The optimum parameter settings where lowest surface
roughness value is observed is when layer thickness
= 0.2 mm, raster angle = 45° and platform temperature = 80℃. |
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