Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing
Fraunhofer Institute for Machine Tools and Forming Technology (IWU)（弗劳恩霍夫机床与成型技术研究所（IWU））；Fraunhofer Institute for Industrial Mathematics (ITWM)（弗劳恩霍夫工业数学研究所（ITWM））；Institute of Mechatronic Engineering Technische Universität Dresden（德累斯顿工业大学机电工程研究所）
Patrick Link, Miltiadis Poursanidis, Jochen Schmid, Rebekka Zache, Martin von Kurnatowski, Uwe Teicher, Steffen Ihlenfeldt
Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such machine learning models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with machine learning methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input-output relationship to be learned. The proposed methodology is applied to a brushing process with $125$ data points for predicting the surface roughness as a function of five process variables. As opposed to conventional machine learning methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. Another merit of the proposed methodology is that, in contrast to most conventional machine learning methods, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.
Compensating data shortages in manufacturing with monotonicity knowledge
Fraunhofer Institute for Industrial Mathematics（弗劳恩霍夫工业数学研究所）；Fraunhofer Institute for Machine Tools and Forming Technology（弗劳恩霍夫机床与成形技术研究所）；Fraunhofer Institute for Mechanics of Materials（弗劳恩霍夫材料力学研究所）
Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification
Merck Group（默克集团）；Versum Materials US LLC（Versum Materials美国分公司）；Merck Electronics KGaA（默克电子（KGaA））；Technical University of Darmstadt（达姆施塔特技术大学）；Otto von Guericke University Magdeburg（奥托·冯·格里克大学马格德堡分校）
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）
Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing
Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Northwestern Polytechnical University（西北工业大学）
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty
Nagoya Institute of Technology（名古屋工业大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；National Institute for Materials Sciences（国立材料科学研究所）
A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
Civil and Environmental Engineering, Indian Institute of Technology Delhi（印度理工学院德里学院土木与环境工程系）；Politecnico di Milano, Department of Energy（米兰理工学院能源系）；Fuller Technologies（Fuller技术公司）；Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里人工智能学院）
