3D BUILD MELT POOL PREDICTIVE MODELING FOR POWDER BED FUSION ADDITIVE MANUFACTURING

Published

August 16, 2020

Author(s)

Zhuo Yang, Yan Lu, Ho Yeung, Sundar Krishnamurty

Abstract

Melt pool size is a critical intermediate measure that reflects the outcome of a laser powder bed fusion process setting. Reliable melt pool predictions prior to builds can help users to evaluate potential part defects such as lack of fusion and over melting. This paper develops a layer-wise Neighboring-Effect Modeling (L-NBEM) method to predict melt pool size for 3d builds. The proposed method employs a feedforward neural network model with ten layer-wise and track-wise input variables. An experimental build using a spiral concentrating scan pattern with varying laser power was conducted on the Additive Manufacturing Metrology Testbed at the National Institute of Standards and Technology. Training and validation data were collected from 21 completed layers of the build, with 6,192,495 digital commands and 118,928 in-situ melt pool coaxial images. The L-NBEM model using the neural network approach demonstrates a better performance of average predictive error (12.12%) by leave-one-out cross-validation method, which is lower than the benchmark NBEM model (15.23%), and the traditional power-velocity model (19.41%).

Proceedings Title

Proceedings of ASME 2020 International Design Engineering Technical Conferences & Computers and
Information in Engineering Conference (IDETC/CIE 2020)

Conference Dates

August 16-19, 2020

Conference Location

St. Louis, MO, US

Pub Type

Conferences

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Keywords

Melt pool size, powder bed fusion, additive manufacturing, machine learning, layer-wise, track-wise

Citation

Yang, Z. , Lu, Y. , Yeung, H. and Krishnamurty, S. (2020), 3D BUILD MELT POOL PREDICTIVE MODELING FOR POWDER BED FUSION ADDITIVE MANUFACTURING, Proceedings of ASME 2020 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2020), St. Louis, MO, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930317 (Accessed September 15, 2024)