Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing

• URL: http://arxiv.org/abs/2510.26586v2
• Date: Sun, 09 Nov 2025 00:04:44 GMT
• Title: Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
• Authors: Sebastian Basterrech, Shuo Shan, Debabrata Adhikari, Sankhya Mohanty,
• Abstract summary: We leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing processes.<n>The empirical evaluation was conducted by analyzing real-world data from two AM processes.
• Score: 0.824969449883056
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing (AM) processes. By incorporating physics based principles, we also ensure that the model is sensitive to meaningful physical parameter variations. The empirical evaluation was conducted by analyzing real-world data from two AM processes: Directed Energy Deposition and Laser Powder Bed Fusion. In addition, we also studied the performance of the developed framework over public datasets with different alloy type and experimental parameter information. The results show the potential of physics-guided mixture models to examine the underlying physical behavior of an AM system.

Related papers

• Foundation Models for Discovery and Exploration in Chemical Space [57.97784111110166]
  MIST is a family of molecular foundation models trained on large unlabeled datasets.<n>We demonstrate the ability of these models to solve real-world problems across chemical space.
  arXiv  Detail & Related papers  (2025-10-20T17:56:01Z)
• Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling [5.437298646956505]
  Building energy modeling is a key tool for optimizing the performance of building energy systems.<n>Recently, hybrid approaches that combine the strengths of both paradigms have gained attention.
  arXiv  Detail & Related papers  (2025-07-23T14:07:33Z)
• Learning and Transferring Physical Models through Derivatives [61.227256589854726]
  We propose Derivative Learning (DERL), a supervised approach that models physical systems by learning their partial derivatives.<n>We also leverage DERL to build physical models incrementally, by designing a distillation protocol that effectively transfers knowledge from a pre-trained model to a student one.
  arXiv  Detail & Related papers  (2025-05-02T17:02:00Z)
• A preliminary data fusion study to assess the feasibility of Foundation Process-Property Models in Laser Powder Bed Fusion [0.0]
  A major challenge that impedes the construction of foundation process-property models is data scarcity.<n>We generate experimental datasets from 17-4 PH and 316L stainless steels (SSs) in Laser Powder Bed Fusion (LPBF)<n>We then leverage Gaussian processes (GPs) for process-property modeling in various configurations to test if knowledge about one material system or property can be leveraged to build more accurate machine learning models for other material systems or properties.
  arXiv  Detail & Related papers  (2025-03-20T19:29:38Z)
• A Phenomenological AI Foundation Model for Physical Signals [1.204553980682492]
  We develop and train a model on 0.59 billion samples of cross-modal sensor measurements. No prior knowledge of physical laws or inductive biases were introduced into the model. We demonstrate that a single foundation model could effectively encode and predict physical behaviors.
  arXiv  Detail & Related papers  (2024-10-15T21:03:53Z)
• ContPhy: Continuum Physical Concept Learning and Reasoning from Videos [86.63174804149216]
  ContPhy is a novel benchmark for assessing machine physical commonsense. We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy. We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models.
  arXiv  Detail & Related papers  (2024-02-09T01:09:21Z)
• Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation [9.978961706999833]
  This work presents a novel approach, physics-guided adversarial machine learning (ML), that improves the confidence over the physics consistency of the model. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach.
  arXiv  Detail & Related papers  (2022-09-07T19:23:45Z)
• Bayesian Calibration of imperfect computer models using Physics-informed priors [0.0]
  We introduce a computational efficient data-driven framework suitable for quantifying the uncertainty in physical parameters of computer models. We extend this into a fully Bayesian framework which allows quantifying the uncertainty of physical parameters and model predictions. This work is motivated by the need for interpretable parameters for the hemodynamics of the heart for personal treatment of hypertension.
  arXiv  Detail & Related papers  (2022-01-17T15:16:26Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.