Special Issue: Scientific Machine Learning for Manufacturing Processes and Material Systems

摘要

Computational modeling, simulation, and optimization of manufacturing processes and materials systems have been a persistent endeavor of the engineering research community at large. Significant progress has been achieved in this field due to the exponential increase in computing power, and the incorporation of data-driven modeling methods. Process and systems modeling often involves expensive and time-intensive simulations and experiments. Incorporation of machine-learning (ML) models as efficient surrogate models has been proven to enhance the human understanding of the behavior of the system at hand and reduce the computational optimization cost of the concerned processes and systems. However, there is a rising need to go beyond the conventional data-driven techniques to address challenges, such as presence of noise in data, limited budget, data sparsity, lack of interpretability of ML models, etc. Tackling these issues will enable more comprehensive modeling of manufacturing processes and discovery of novel material systems.This special issue focuses on the new paradigm called scientific ML and aims to explore and potentially resolve issues related to improving computational efficiency, incorporating domain awareness, and improving the interpretability and robustness of the models and modeling techniques. In particular, this special issue has invited both full research and review papers focusing on research advances in the areas of scientific machine learning for manufacturing processes and material systems. The announced topics of interest included but were not limited to the following topics: Physics-informed ML for process/materials design and optimization.Physics-informed ML for diagnostics, prognostics and process control.Uncertainty quantification in modeling (including physics-informed ML, etc.).Leveraging high-throughput framework for modeling and optimization.Efficient modeling through adaptive and active learning algorithms.Explainable AI and causal inference augmented predictive modeling.Exploring state-of-the-art ML algorithms in modeling and optimization.Understanding of systems through knowledge representation and reasoning.Leveraging data-fusion and multi-fidelity techniques in modeling.As result of this call, this issue features nine papers delving into various topics, including the following:probabilistic printability maps for laser powder bed fusion via functional calibration and uncertainty propagation; unsupervised anomaly detection via nonlinear manifold learning; a physics-informed general convolutional network for the computational modeling of materials with damage; multi-fidelity physics-informed generative adversarial network for solving partial differential equations; stochastic defect localization for cooperative additive manufacturing using Gaussian mixture maps; stress representations for tensor basis neural networks: alternative formulations to Finger–Rivlin–Ericksen; machine-learning metacomputing for materials science data; physics-informed fully convolutional networks for forward prediction of temperature field and inverse estimation of thermal diffusivity and a global feature reused network for defect detection in steel images.Summaries of each of the nine papers published in this special issue are provided below:“Probabilistic Printability Maps for Laser Powder Bed Fusion via Functional Calibration and Uncertainty Propagation” by Wu, Whalen, Ma, and Balachandran, describes the development of an efficient computational framework for process space exploration in laser powder bed fusion (LPBF)-based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. A Bayesian approach is developed for inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditio