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22 March 2021 Development of ANN model for surface roughness prediction of parts produced by varying fabrication parameters
Bharath Bhushan Ravichander, Zehao Ye, Chen Kan, Narges Shayesteh Moghaddam, Amirhesam Amerinatanzi
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Proceedings Volume 11589, Behavior and Mechanics of Multifunctional Materials XV; 115890M (2021) https://doi.org/10.1117/12.2585603
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only
Abstract
Selective laser melting (SLM) is the most common additive manufacturing technique designed to fabricate functional parts with high accuracy. Depending on the desired properties, the process parameters for a given material need to be optimized for improving the overall reliability of the SLM devices. As all the process parameters are inter-dependent on each other, it is important to find an optimum value to suit the requirement and render the best build quality. This work primarily focuses on the effect of various process parameters such as laser power, scanning speed, and hatch spacing on the roughness of Inconel 718 parts fabricated on an EOS M290 machine. Statistical models of surface roughness are established to identify the relationship between the abovementioned process parameters. The capabilities developed in this study will permit a deep understanding of the process- property relationships in structural SLM components.
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Bharath Bhushan Ravichander, Zehao Ye, Chen Kan, Narges Shayesteh Moghaddam, and Amirhesam Amerinatanzi "Development of ANN model for surface roughness prediction of parts produced by varying fabrication parameters", Proc. SPIE 11589, Behavior and Mechanics of Multifunctional Materials XV, 115890M (22 March 2021); https://doi.org/10.1117/12.2585603
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KEYWORDS
Spatial light modulators
Additive manufacturing
Surface roughness
Fabrication
Reliability
Statistical analysis
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