Manufacturing

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Manufacturing research uses data, models, and automation to improve how products are designed and made, from CAD-based process planning to shop-floor scheduling and robot coordination. Common approaches include deep learning for inspection and sensor analysis, reinforcement learning for scheduling and process control, physics-informed models for predicting process behavior, and knowledge graphs or language-model agents for connecting instructions, machine data, and decisions. Researchers evaluate these methods using production records, simulators, digital twins, and benchmarks for tasks such as defect detection, machine understanding, and cross-machine transfer. A central challenge is making systems reliable beyond controlled tests despite scarce or shifting data, while keeping decisions physically feasible, interpretable, and safe for human-supervised production.

Latest papers

77 papers10 papers in the last four weeks, up 25% on the four weeks before. 0.1% of all new papers.

Dynamic resource matching in manufacturing using deep reinforcement learning
Mar 28, 2026Saunak Kumar Panda, Yisha Xiang, Ruiqi LiuManufacturing RL for Combinatorial Optimization
Department of Industrial Engineering, University of Houston, Houston, TX 77004, USA · Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
Physics-Informed Neural Networks and Sequence Encoder: Application to heating and early cooling of thermo-stamping process
Mar 27, 2026Mouad Elaarabi, Domenico Borzacchiello, Philippe Le Bot +2Physics-Informed ML Neural Surrogate Modeling
Nantes Université, Ecole Centrale Nantes, CNRS, GeM, UMR 6183, Nantes, 44321, France · Nantes Université, IRT Jules Verne, Bouguenais, 44340, France
Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory
Mar 25, 2026Yaxin Liao, Qimei Cui, Kwang-Cheng Chen +5Smart Manufacturing Multi-Robot Coordination
National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Electrical Engineering, University of South Florida, Tampa, FL 33620 USA · School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection
Mar 23, 2026Ekta U. Samani, Christopher G. AtkesonManufacturing Large Language Model-Guided Optimization
Robotics Institute at Carnegie Mellon University
From Language Models to Control Policies: Search, Selection, and Verification
Mar 20, 2026Nima H. Siboni, Seyedreza Kiamousavi, Vahid Rostami +1Automated Heuristic Design LLM-Based Program Synthesis
Juna.ai, Kastanienallee 32, 10435 Berlin · Institute of Metal Forming, RWTH Aachen, Intzestr. 10, 52072 Aachen
Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes
Mar 20, 2026Sebastian Rodriguez, Mikhael Tannous, Jad Mounayer +3Manufacturing Autoencoders
Univ Toulouse, IMT Mines Albi, INSA Toulouse, ISAE-SUPAERO, CNRS, ICA, Albi, France
A Human-Centred Architecture for Large Language Models-Cognitive Assistants in Manufacturing within Quality Management Systems
Mar 17, 2026Marcos Galdino, Johanna Grahl, Tobias Hamann +2Human-Centered AI Manufacturing
IQS Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, RWTH Aachen University, Aachen, Germany
Geometric and Topological Deep Learning for Predicting Thermo-mechanical Performance in Cold Spray Deposition Process Modeling
Mar 15, 2026Akshansh MishraSurrogate Modeling Manufacturing
School of Industrial and Information Engineering, Politecnico di Milano, Milan, Italy
Gen-Fab: A Variation-Aware Generative Model for Predicting Fabrication Variations in Nanophotonic Devices
Mar 12, 2026Rambod Azimi, Yuri Grinberg, Dan-Xia Xu +1Manufacturing Digital Twins
Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada. · Digital Technologies Research Center, National Research Council of Canada, Ottawa, ON, Canada. · Quantum and Nanotechnologies Research Center, National Research Council of Canada, Ottawa, ON, Canada.
Classifying Novel 3D-Printed Objects without Retraining: Towards Post-Production Automation in Additive Manufacturing
Mar 8, 2026Fanis Mathioulakis, Gorjan Radevski, Silke GC Cleuren +4Additive Manufacturing Manufacturing
KU Leuven, Belgium. · Materialise, Belgium. · Iristick, Belgium.
Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation
Mar 6, 2026Julien Amblard, Niklas Groll, Matthew Tait +3Fault Detection Interpretable ML
Department of Computing, Imperial College London, London, United Kingdom · Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark · ILASP Limited, London, United Kingdom
CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing
Mar 6, 2026Mohammadhossein Ghahramani, Mengchu ZhouSmart Manufacturing Industrial Fault Diagnosis
Birmingham City University, UK · the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Capability Thresholds and Manufacturing Topology: How Embodied Intelligence Triggers Phase Transitions in Economic Geography
Mar 3, 2026Xinmin Fang, Lingfeng Tao, Zhengxiong LiManufacturing Embodied AI
LeTau Robotics · University of Colorado Denver · Kennesaw State University
Design Framework and Manufacturing of an Active Magnetic Bearing Spindle for Micro-Milling Applications
Feb 26, 2026Kazi Sher Ahmed, Bekir BedizManufacturing
Faculty of Engineering and Natural Sciences, Mechatronics Engineering Program, Sabanci University, Istanbul, 34956, Turkiye
AutoSew: A Geometric Approach to Stitching Prediction with Graph Neural Networks
Feb 25, 2026Pablo Ríos-Navarro, Elena Garces, Jorge Lopez-MorenoManufacturing Graph Matching
Universidad Rey Juan Carlos, Madrid, Spain · Adobe Research, France
Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation
Feb 20, 2026Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng +1Industrial Automation Manufacturing
Singapore Institute of Manufacturing Technology (SIMTech), A*STAR, Singapore 636732, Republic of Singapore · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 · Advanced Remanufacturing and Technology Centre (ARTC), A*STAR, Singapore 637143, Republic of Singapore
DEFT: Differentiable Branched Discrete Elastic Rods for Modeling Furcated DLOs in Real-Time
Feb 20, 2025Yizhou Chen, Felicia Sang, Shicheng Peng +6Deformable Object Simulation Dynamic Robotic Manipulation
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, United States

Mar 28, 2026J/K move · Enter open · S save

Dynamic resource matching in manufacturing using deep reinforcement learning

Saunak Kumar Panda, Yisha Xiang, Ruiqi Liu

Department of Industrial Engineering, University of Houston, Houston, TX 77004, USA · Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA

Manufacturing RL for Combinatorial Optimization Model-Free RL Deep Deterministic Policy Gradient Constrained RL

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Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturing industries. In particular, capacity sharing has received much attention recently. In this paper, we consider the problem of dynamically matching demand-capacity types of manufacturing resources. We formulate the multi-period, many-to-many manufacturing resource-matching problem as a sequential decision process. The formulated manufacturing resource-matching problem involves large state and action spaces, and it is not practical to accurately model the joint distribution of various types of demands. To address the curse of dimensionality and the difficulty of explicitly modeling the transition dynamics, we use a model-free deep reinforcement learning approach to find optimal matching policies. Moreover, to tackle the issue of infeasible actions and slow convergence due to initial biased estimates caused by the maximum operator in Q-learning, we introduce two penalties to the traditional Q-learning algorithm: a domain knowledge-based penalty based on a prior policy and an infeasibility penalty that conforms to the demand-supply constraints. We establish theoretical results on the convergence of our domain knowledge-informed Q-learning providing performance guarantee for small-size problems. For large-size problems, we further inject our modified approach into the deep deterministic policy gradient (DDPG) algorithm, which we refer to as domain knowledge-informed DDPG (DKDDPG). In our computational study, including small- and large-scale experiments, DKDDPG consistently outperformed traditional DDPG and other RL algorithms, yielding higher rewards and demonstrating greater efficiency in time and episodes.