# A Machine Learning Framework for Real-time Inverse Modeling and Multi-objective Process Optimization of Composites for Active Manufacturing Control

arXiv: https://arxiv.org/abs/2104.11342 · HTML: https://mlsanity.com/paper/2104.11342/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 22 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications](https://mlsanity.com/paper/2010.02011/) (2020)
- [Theory-Guided Machine Learning for Process Simulation of Advanced Composites](https://mlsanity.com/paper/2103.16010/) (2021)

## Built on by (most cited first, 9 of 9)

- [Residual fourier neural operator for thermochemical curing of composites](https://mlsanity.com/paper/2111.10262/) (2021)
- [Probabilistic Physics-integrated Neural Differentiable Modeling for Isothermal Chemical Vapor Infiltration Process](https://mlsanity.com/paper/2311.07798/) (2023)
- [LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers](https://mlsanity.com/paper/2511.00116/) (2025)
- [Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network](https://mlsanity.com/paper/2512.13746/) (2025)
- [Integrating supervised and unsupervised learning approaches to unveil critical process inputs](https://mlsanity.com/paper/2405.07751/) (2024)
- [Implementing NLPs in industrial process modeling: Addressing Categorical Variables](https://mlsanity.com/paper/2409.19097/) (2024)
- [Generative Model Predictive Control in Manufacturing Processes: A Review](https://mlsanity.com/paper/2511.17865/) (2025)
- [Machine Learning-Based Multi-Objective Design Exploration Of Flexible Disc Elements](https://mlsanity.com/paper/2304.07245/) (2023)
- [Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts](https://mlsanity.com/paper/2607.19060/) (2026)

## Abstract (arXiv)

For manufacturing of aerospace composites, several parts may be processed simultaneously using convective heating in an autoclave. Due to uncertainties including tool placement, convective Boundary Conditions (BCs) vary in each run. As a result, temperature histories in some of the parts may not conform to process specifications due to under-curing or over-heating. Thermochemical analysis using Finite Element (FE) simulations are typically conducted prior to fabrication based on assumed range of BCs. This, however, introduces unnecessary constraints on the design. To monitor the process, thermocouples (TCs) are placed under tools near critical locations. The TC data may be used to back-calculate BCs using trial-and-error FE analysis. However, since the inverse heat transfer problem is ill-posed, many solutions are obtained for given TC data. In this study, a novel machine learning (ML) framework is presented capable of optimizing air temperature cycle in real-time based on TC data from multiple parts, for active control of manufacturing. The framework consists of two recurrent Neural Networks (NN) for inverse modeling of the ill-posed curing problem at the speed of 300 simulations/second, and a classification NN for multi-objective optimization of the air temperature at the speed of 35,000 simulations/second. A virtual demonstration of the framework for process optimization of three composite parts with data from three TCs is presented.