# Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing

arXiv: https://arxiv.org/abs/2509.16233 · HTML: https://mlsanity.com/paper/2509.16233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data](https://mlsanity.com/paper/2003.06097/) (2020)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [Towards Bayesian Deep Learning: A Framework and Some Existing Methods](https://mlsanity.com/paper/1608.06884/) (2016)
- [Diversity Matters When Learning From Ensembles](https://mlsanity.com/paper/2110.14149/) (2021)
- [A Primer on Bayesian Neural Networks: Review and Debates](https://mlsanity.com/paper/2309.16314/) (2023)
- [MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning](https://mlsanity.com/paper/2201.11662/) (2022)
- [Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization](https://mlsanity.com/paper/2402.17718/) (2024)
- [Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks](https://mlsanity.com/paper/2311.02495/) (2023)
- [Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression](https://mlsanity.com/paper/2103.08349/) (2021)
- [Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling](https://mlsanity.com/paper/2306.04664/) (2023)
- [A stochastic control approach for constrained stochastic differential games with jumps and regimes](https://mlsanity.com/paper/2301.12921/) (2023)

## Abstract (arXiv)

We present a probabilistic framework to accurately estimate dimensions of additively manufactured components. Using a dataset of 405 parts from nine production runs involving two machines, three polymer materials, and two-part configurations, we examine five key design features. To capture both design information and manufacturing variability, we employ models integrating continuous and categorical factors. For predicting Difference from Target (DFT) values, we test deterministic and probabilistic machine learning methods. Deterministic models, trained on 80% of the dataset, provide precise point estimates, with Support Vector Regression (SVR) achieving accuracy close to process repeatability. To address systematic deviations, we adopt Gaussian Process Regression (GPR) and Bayesian Neural Networks (BNNs). GPR delivers strong predictive performance and interpretability, while BNNs capture both aleatoric and epistemic uncertainties. We investigate two BNN approaches: one balancing accuracy and uncertainty capture, and another offering richer uncertainty decomposition but with lower dimensional accuracy. Our results underscore the importance of quantifying epistemic uncertainty for robust decision-making, risk assessment, and model improvement. We discuss trade-offs between GPR and BNNs in terms of predictive power, interpretability, and computational efficiency, noting that model choice depends on analytical needs. By combining deterministic precision with probabilistic uncertainty quantification, our study provides a rigorous foundation for uncertainty-aware predictive modeling in AM. This approach not only enhances dimensional accuracy but also supports reliable, risk-informed design strategies, thereby advancing data-driven manufacturing methodologies.