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.
SpinCastML an Open Decision-Making Application for Inverse Design of Electrospinning Manufacturing: A Machine Learning, Optimal Sampling and Inverse Monte Carlo Approach
Integrating Wide and Deep Neural Networks with Squeeze-and-Excitation Blocks for Multi-Target Property Prediction in Additively Manufactured Fiber Reinforced Composites
Department of Mechanical Engineering, Southern Methodist University（机械工程系，南方 Methodist 大学）
Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest
Portland State University（波特兰州立大学）；Imagars LLC（伊马格斯有限责任公司）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Siemens Corporation, Corporate Technology（西门子公司企业技术部）；National Energy Transportation Laboratory（国家能源运输实验室）
Univariate Conditional Variational Autoencoder for Morphogenic Patterns Design in Frontal Polymerization-Based Manufacturing
National Center for Supercomputing Applications, University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校国家超级计算应用中心）；Beckman Institute for Advanced Science and Technology, University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校贝克曼先进科学技术研究所）；Grainger College of Engineering, University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校格兰杰工程学院）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Kansas State University（堪萨斯州立大学）；Massachusetts Institute of Technology（麻省理工学院）
Reinforcement Learning for Synchronised Flow Control in a Dual-Gate Resin Infusion System
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València（人类中心技术大学研究院，HUMAN-tech，瓦伦西亚理工大学）；Department of Civil and Mechanical Engineering, Technical University of Denmark（土木与机械工程系，技术大学 of Denmark）；Artikode Intelligence S.L
