A physics-informed neural network is developed to solve conductive heat transfer partial differential equation (PDE), along with convective heat transfer PDEs as boundary conditions (BCs), in manufacturing and engineering applications where parts are heated in ovens. Since convective coefficients are typically unknown, current analysis approaches based on trial and error finite element (FE) simulations are slow. The loss function is defined based on errors to satisfy PDE, BCs and initial condition. An adaptive normalizing scheme is developed to reduce loss terms simultaneously. In addition, theory of heat transfer is used for feature engineering. The predictions for 1D and 2D cases are validated by comparing with FE results. It is shown that using engineered features, heat transfer beyond the training zone can be predicted. Trained model allows for fast evaluation of a range of BCs to develop feedback loops, realizing Industry 4.0 concept of active manufacturing control based on sensor data.
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
Department of Agricultural and Environmental Sciences, University of Bari（巴里大学农业与环境科学系）；Department of Mechanical, Mathematics, and Management (DMMM), Polytechnic University of Bari（巴里理工学院机械、数学与管理系）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）
Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely physics-based computational models suffer from intensive computational costs and the need of calibrating unknown parameters, thus not suitable for online control and iterative design application. Data-driven models taking advantage of the latest developed computational tools can serve as a more efficient surrogate, but they are usually trained over a large amount of simulation data and often fail to effectively use small bu…
Real-time distortion prediction in metallic additive manufacturing via a physics-informed neural operator approach
School of Mechanical and Power Engineering, Nanjing Tech University（南京工业大学机械与动力工程学院）；Institute of Reliability centered Manufacturing, Nanjing Tech University（南京工业大学可靠性制造研究所）；Jiangsu Provincial Key Laboratory of Energy Power Manufacturing Equipment and Reliability Technology, Nanjing Tech University（南京工业大学江苏省能源动力制造装备与可靠性技术重点实验室）
Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；Center for Quantum Research and Technology, University of Oklahoma（俄克拉荷马大学量子研究与技术中心）；Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory（创新研究与工程智能神经形态与量子理解实验室）；Material Science and Engineering Program, University of Oklahoma, Norman, OK 73019 USA（俄克拉荷马大学材料科学与工程项目，Norman, OK 73019 USA）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Livermore Software Technology, an ANSYS company（利弗莫尔软件技术公司，ANSYS 旗下公司）
The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling. However, the success of conventional machine learning tools in data science is primarily attributed to the unprecedented large amount of labeled data-sets (big data), which can be either obtained by experiments or first-principle simulations. Unfortunately, these labeled data-sets are expensive to obtain in AM due to the high expense of the AM experiments and prohibitive computational cost of high-fidelity simulations. W…
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
Department of Computer and Data Sciences, Shahid Beheshti University（计算机与数据科学系，谢赫·贝赫什提大学）；Department of Cognitive Modeling, Shahid Beheshti University（认知建模系，谢赫·贝赫什提大学）
Kent State University（肯特州立大学）；Colorado School of Mines（科罗拉多矿业学院）
To control part quality, it is critical to analyze pore generation mechanisms, laying theoretical foundation for future porosity control. Current porosity analysis models use machine setting parameters, such as laser angle and part pose. However, these setting-based models are machine dependent, hence they often do not transfer to analysis of porosity for a different machine. To address the first problem, a physics-informed, data-driven model (PIM), which instead of directly using machine setting parameters to predict porosity levels of printed parts, it first interprets machine settings into …
Virginia Tech（弗吉尼亚理工大学）；University of Michigan（密歇根大学）
Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weather modeling, healthcare, manufacturing, etc. Poor scalability is one of the barriers to utilizing PINNs for many real-world problems. To address this, a Self-scalable tanh (Stan) activation function is proposed for the PINNs. The proposed Stan function is smooth, non-saturating, and has a trainable parameter. During training, it can allow easy flow of gradients to compute the required derivatives and also enable syst…
PhysAttNet: Enhancing Predictive Performance in Industrial and Astrophysical Time Series via Physics-Informed Attention
Faculty of Physics and Astronomy, Ruhr University Bochum（鲁尔大学波鸿分校物理与天文学学院）；Virtual Machining Group, TU Dortmund（多特蒙德工业大学虚拟加工组）；Faculty of Physics（物理学院）
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
Division of Advanced Nuclear Engineering, Pohang University of Science and Technology（浦项科技大学先进核工程学部）；Mueunjae School of Undergraduate Studies, Pohang University of Science and Technology（浦项科技大学文在本科书院）；Department of Quantum System Engineering, Jeonbuk National University（全北国立大学量子系统工程系）
The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging manufacturing process, shear assisted processing and extrusion (ShAPE). We generate realistic images conditio…
In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural networ…
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Colorado State University（科罗拉多州立大学）；University of Washington（华盛顿大学）
Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue…
ETH Zürich（苏黎世联邦理工学院）；Inspire AG（Inspire股份公司）；Oerlikon Metco（欧瑞康美科）
We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization procedure. \cmtb{The novel acquisition function is demonstrated, analyzed and compared on state-of-the-art benchmarking problems. We apply the optimization approach to atmospheric plasma spraying and fused deposition modeling.} Our results demonstrate that the proposed framework ca…
Sandia National Laboratories（桑迪亚国家实验室）；Arizona State University（亚利桑那州立大学）
We introduce physics-informed multimodal autoencoders (PIMA) - a variational inference framework for discovering shared information in multimodal scientific datasets representative of high-throughput testing. Individual modalities are embedded into a shared latent space and fused through a product of experts formulation, enabling a Gaussian mixture prior to identify shared features. Sampling from clusters allows cross-modal generative modeling, with a mixture of expert decoder imposing inductive biases encoding prior scientific knowledge and imparting structured disentanglement of the latent s…
University College Dublin（都柏林大学）；Macau University of Science and Technology（澳门科技大学）；New Jersey Institute of Technology（新泽西理工学院）；Royal College of Surgeons in Ireland（爱尔兰皇家外科医学院）
Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms…
The increasing deployment of advanced digital technologies such as Internet of Things (IoT) devices and Cyber-Physical Systems (CPS) in industrial environments is enabling the productive use of machine learning (ML) algorithms in the manufacturing domain. As ML applications transcend from research to productive use in real-world industrial environments, the question of reliability arises. Since the majority of ML models are trained and evaluated on static datasets, continuous online monitoring of their performance is required to build reliable systems. Furthermore, concept and sensor drift can…
University of Patras（帕特雷大学）；Westlake University（西湖大学）；University of Santiago（圣地亚哥大学）；National Centre for Scientific Research Demokritos（国家科学研究中心“德谟克利特”）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Argonne National Laboratory（阿贡国家实验室）；Superlabs ΑΜΚΕ；Biological Lattice Industries Corp.（Biological Lattice Industries公司）
Calibration of highly dynamic multi-physics manufacturing processes such as electro-hydrodynamics-based additive manufacturing (AM) technologies (E-jet printing) is still performed by labor-intensive trial-and-error practices. These practices have hindered the broad adoption of these technologies, demanding a new paradigm of self-calibrating E-jet printing machines. To address this need, we developed GPJet, an end-to-end physics-informed Bayesian learning framework, and tested it on a virtual E-jet printing machine with in-process jet monitoring capabilities. GPJet consists of three modules: a…
1 Department of Electrical；2 Department of Mechanical Engineering
Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational mode…
Physics-informed data-driven machine health monitoring for two-photon lithography
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States（密歇根大学安娜堡分校机械工程系）；Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States（伊利诺伊大学厄巴纳-香槟分校机械科学与工程系）
Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing
Center for Nanoscale Materials, Argonne National Laboratory（阿贡国家实验室纳米尺度材料中心）；Department of Mechanical and Industrial Engineering, University of Illinois, Chicago（伊利诺伊大学芝加哥分校机械与工业工程系）；Shell International Exploration and Production Inc.（壳牌国际勘探与生产公司）
IoT systems have been facing increasingly sophisticated technical problems due to the growing complexity of these systems and their fast deployment practices. Consequently, IoT managers have to judiciously detect failures (anomalies) in order to reduce their cyber risk and operational cost. While there is a rich literature on anomaly detection in many IoT-based systems, there is no existing work that documents the use of ML models for anomaly detection in digital agriculture and in smart manufacturing systems. These two application domains pose certain salient technical challenges. In agricult…
