Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing

Abstract

Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to the high cost of generating data from either physics-based models or experiments. This paper engineers features for a surrogate model to predict the consolidation degree in the fused filament fabrication process. Our features are informed by the physics of the underlying thermal processes and capture the characteristics of the part’s geometry and the deposition process. Our model is learned from medium-size data generated using a physics-based thermal model coupled with the polymer healing theory to determine the consolidation degree. Our results demonstrate high accuracy (>90%) of consolidation degree prediction at a low computational cost (four orders of magnitude faster than the numerical model).

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“…21 Feature engineering or multivariable feature analysis refers to the process of recruiting comprehensive statistical methods and domain knowledge to reveal the most relevant subset of features from the raw feature sets to be used in supervised or unsupervised predictive analytics processes. [22][23][24][25][26][27][28] Such analyses can be either performed in the spatialdomain (image feature space), or the frequency-domain to highlight specific information and reveal the most relevant and explanatory features towards the application of interest. The focus of the frequency-based feature engineering techniques is to decompose the raw information into different frequency bandwidths or information components in the frequencydomain which can be compared among different samples/cases and used for model development.…”