Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

Najmeh Samadiani (), Amanda S. Barnard, Dayalan Gunasegaram and Najmeh Fayyazifar
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Najmeh Samadiani: CSIRO Manufacturing
Amanda S. Barnard: Australian National University
Dayalan Gunasegaram: CSIRO Manufacturing
Najmeh Fayyazifar: Curtin School of Allied Health, Curtin University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 7, No 3, 4477-4517

Abstract: Abstract The process parameters used for building a part utilizing the powder-bed fusion (PBF) additive manufacturing (AM) system have a direct influence on the quality—and therefore performance—of the final object. These parameters are commonly chosen based on experience or, in many cases, iteratively through experimentation. Discovering the optimal set of parameters via trial and error can be time-consuming and costly, as it often requires examining numerous permutations and combinations of parameters which commonly have complex interactions. However, machine learning (ML) methods can recommend suitable processing windows using models trained on data. They achieve this by efficiently identifying the optimal parameters through analyzing and recognizing patterns in data described by a multi-dimensional parameter space. We reviewed ML-based forward and inverse models that have been proposed to unlock the process–structure–property–performance relationships in both directions and assessed them in relation to data (quality, quantity, and diversity), ML method (mismatches and neglect of history), and model evaluation. To address the common shortcomings inherent in the published works, we propose strategies that embrace best practices. We point out the need for consistency in the reporting of details relevant to ML models and advocate for the development of relevant international standards. Significantly, our recommendations can be adopted for ML applications outside of AM where an optimum combination of process parameters (or other inputs) must be found with only a limited amount of training data.

Keywords: Forward model; Inverse model; Layer-wise optimization; Geometry complexity; Geometric features; Regression; Data augmentation; Small data; Deep learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02490-4

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