Abstract
To improve the consistency of part quality in Additive Manufacturing, it is critical to understand the relationship between the mechanisms underlying the layer-by-layer printing process and the resulting product quality. This paper investigates this relationship by incorporating attention mechanism into a Long Short-term Memory network, using Fused Deposition Modeling as a case study. In-process thermal variations, as reflected in the in-situ temperature measurement, are fused with machine settings to establish a data-driven model for part tensile strength prediction. Analysis using attention mechanism quantified the relative influence of each printed layer on the predictive result, providing insight into the network operation.
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