Additive Manufacturing

Follow this topic Ask about this topic

Additive manufacturing studies how to make parts by depositing or solidifying material layer by layer, from fused-filament printing to laser powder-bed fusion and wire-arc systems. Work combines process physics and materials models with machine learning—especially vision and sensor-based defect detection, physics-informed neural networks, graph models, and optimization—to predict quality and adjust settings or toolpaths. Robotic and multi-axis printing also brings in collision-aware planning, geometry processing, and language-model agents for design and process assistance. A central challenge is making models reliable across materials, machines, geometries, and changing conditions, while validating predictions and automated decisions on physical builds rather than simulations alone.

Latest papers

50 papers6 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

AcoustiPrint: Physics-Guided Neural Design of Manufacturable Acoustic Structures
Oct 21, 2026Shanmuka Sadhu, Harshvardhan C. Takawale, Yehia Zakaria +2Inverse Design Physics-Informed ML
Department of Computer Science, University of Maryland, College Park · Department of Mechanical and Materials Engineering, University of Cincinnati
Concurrent two-scale topology optimization of printable bone scaffolds via rectified-flow generation and exact latent-space refinement
Oct 18, 2026Reza T Batley, Sourav SahaTopology Optimization Latent Space Optimization
Kevin T Crofton Department of Aerospace and Ocean Engineering, Virginia Tech, 1600 Innovation Drive, Blacksburg, 24060, VA, United States
Domain-informed Adaptive Sampling for Generalizable PINNs in Metal Additive Manufacturing via Conditional Flow Matching
Oct 6, 2026Hyeonsu Lee, Jihoon JeongAdaptive Sampling Physics-Informed ML
Wm Michael Barnes ’64 Department of Industrial and Systems Engineering Texas A&M University College Station, TX 77843, USA
Generative World Models Enable Predictive Control of Laser Melt Pool Dynamics
Oct 5, 2026Yiyang Yan, Markus Bambach, Mohamadreza AfrasiabiWorld Model Learning Model Predictive Control
Advanced Manufacturing Lab, ETH Zürich, Leonhardstrasse 21, Zürich 8092, Switzerland
Event Cameras for Melt-Pool Monitoring in Additive Manufacturing: A Benchmark and a Cross-Machine Transfer Analysis
Oct 3, 2026Mohamad Yazan Sadoun, Sarah Sharif, Yingtao Liu +2Additive Manufacturing Event-Based Vision
School of Electrical & Computer Engineering, University of Oklahoma, Norman, OK, USA. · INQUIRE Lab, University of Oklahoma, Norman, OK, USA. · School of Aerospace & Mechanical Engineering, University of Oklahoma, Norman, OK, USA.
DCM-SAM: Defect-Conditioned Mixture of LoRA Experts for NPU-Deployed AM Defect Segmentation
Sep 30, 2026Md Mushfiqur Rahaman, Md Mahedi Hasan, Imtiaz Ahmed +1Low-Rank Adaptation Mixture of Experts
Department of Industrial and Management Systems Engineering West Virginia University, Morgantown, WV · School of Mathematical and Data Sciences West Virginia University, Morgantown, WV · Lane Department of Computer Science & Electrical Engineering West Virginia University, Morgantown, WV
Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning
Sep 16, 2026Jingzhan Ge, Ruimin Chen, Azadeh Haghighi +2Large Language Model-Based Robot Planning Robotics
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, Connecticut, USA · Department of Mechanical and Industrial Engineering, University of Illinois Chicago, Chicago, IL 60608, USA
Toward 3D Printable Non-Planar Electroadhesive Structures for Active Anchoring
Sep 16, 2026Mostafa A. Atalla, Carol R. Derla, Pep Canyelles Pericàs +1Additive Manufacturing
Robotics and Mechatronics Group, Faculty of EEMCS, University of Twente, 7522 Enschede, The Netherlands
Predicting build orientation for SLM dental parts: a comparison of rotation representations and direct vector regression
Sep 14, 2026Felix Schmalzel, Reimar Waitz, Moritz Kronberger +1Manufacturing Additive Manufacturing
Technical University of Applied Sciences Augsburg, Augsburg, 86161, Germany · R. Waitz Data & Science, Kaufering, 86916, Germany
An Automated Thickness Evaluation Procedure Using an Integrated Structured Light 3D Camera in a Robotic Bioprinting Framework
Sep 10, 2026Ehsan Zobeidi, Omid Rezayof, Farshid AlambeigiAdditive Manufacturing
Walker Department of Mechanical Engineering and Texas Robotics, University of Texas at Austin, TX, USA
Robot Aware Computational Design of Object Specific Passive Grippers for Additive Manufacturing
Sep 3, 2026Abdullah Yahya Abdullah Omaisan, Ibrahim Sheikh MohamedRobotic Grasping Robotic Gripper Design
1Independent Researchers, Riyadh, Saudi Arabia. · 2QSS AI and Robotics Lab, Riyadh, Saudi Arabia.
WildFab: Multi-Axis 3D Printing from Models in the Wild
Sep 2, 2026Jiasheng Qu, Zhikai Shen, Chenyu Xu +6Additive Manufacturing Motion Planning
The Chinese University of Hong Kong, Hong Kong, China · Centre for Perceptual and Interactive Intelligence, Hong Kong SAR, China · Carnegie Mellon University, Pittsburgh, USA +2
AI Agentic Selective Laser Sintering Process Optimization
Aug 26, 2026Peter Pak, Victor Alvarado, Amir Barati FarimaniAdditive Manufacturing Agentic Workflows
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA · Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
Electrospun Fields: 3D Nano-Fiber Material Computation as Design Method
Aug 24, 2026Wai Lok Wan, Ayah Mahmoud, Sergio Mutis +3Additive Manufacturing Materials Science
MIT, Cambridge, MA, USA · Harvard University, Cambridge, MA, USA · MIT Media Lab, Cambridge, MA, USA
GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing
Aug 24, 2026Sosmita Paul, Krishna RoySpatio-Temporal Attention Anomaly Localization
New Mexico Institute of Mining and Technology Socorro, NM 87801
Task-Driven 3D Printability Assistance via Geometry- and Knowledge-Grounded LLM Reasoning
Aug 22, 2026Zhaoda Du, Qiaojie Zheng, Xiaoli ZhangAdditive Manufacturing LLM Reasoning
Colorado School of Mines, 1500 Illinois St., Golden, 80401, CO, USA
Process Optimization Under Uncertainty for Improving the Bond Quality of Polymer Filaments in Fused Filament Fabrication
Aug 19, 2026Berkcan Kapusuzoglu, Matthew Sato, Sankaran Mahadevan +1Additive Manufacturing Robust Optimization
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235, USA · Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA
Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication
Aug 19, 2026Berkcan Kapusuzoglu, Paromita Nath, Matthew Sato +2Multi-Objective Optimization Additive Manufacturing
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235, USA · Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
Aug 18, 2026Berkcan Kapusuzoglu, Sankaran MahadevanPhysics-Informed ML Additive Manufacturing
Department of Civil and Environmental Engineering Vanderbilt University Nashville, TN 37235, USA
Manufacturing Complex Airtight Soft Pneumatic Actuators for Soft Robotics: Process Evaluation and Optimization
Aug 13, 2026Mohammed AbboodiManufacturing Soft Pneumatic Actuators
Neuro-Symbolic Closed-Loop Control of Laser Powder Bed Fusion with an In-Loop Ontology
Aug 6, 2026Gisuk Hong, Jaebong Cho, Hyunbo ChoOntology-Based Reasoning Neuro-Symbolic Reasoning
aDepartment of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 37673, South Korea
Toward Geometry-Scalable Whole-Body Touch for Humanoids: A 3D-Printed Conformal EIT Skin
Aug 3, 2026Haofeng Chen, Carson Kohlbrenner, Jiri Kubik +6Tactile Sensing Additive Manufacturing
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague · Human Interaction and Robotics group at CU Boulder · Eindhoven University of Technology
XCT-SAM: Sequential Parameter-Efficient Domain Adaptation of SAM for Industrial XCT Defect Segmentation
Jul 15, 2026Md Mahedi Hasan, Md Mushfiqur Rahaman, Alan Pachkovskiy +3Domain Adaptation Vision Foundation Model Adaptation
Lane Department of Computer Science & Electrical Engineering West Virginia University, Morgantown, WV · Department of Industrial and Management Engineering West Virginia University, Morgantown, WV · School of Mathematical and Data Sciences West Virginia University, Morgantown, WV
AgentsCAD: Automated Design for Manufacturing of FDM Parts via Multi-Agent LLM Reasoning and Geometric Feature Recognition
Jul 2, 2026Emmanuel George, Christopher Keefe, Peter Pak +1Multi-Agent LLM Systems Additive Manufacturing
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Trajectory Optimization for Collision-Aware Redundant Robotic Multi-Axis Additive Manufacturing by Constrained Gradient Projection
Jun 29, 2026Zhikai Shen, Jiasheng Qu, Chenyu Xu +4Manufacturing Constrained Motion Planning
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China · Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong, China · Department of Robotics at the School of Mechanical Engineering, Southeast University, China
Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Jun 18, 2026Kianoush Aqabakee, Leonardo StellaMaterials Property Prediction Soft Actor-Critic
Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, and Department of Mechanical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran · School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom
Side-Channel Attacks Bypass Protection in 3D Printers
Jun 11, 2026Eric Yocam, Varghese Vaidyan, Micah Flack +2Cybersecurity Additive Manufacturing
Department of Computer Science California Polytechnic State University San Luis Obispo, CA 93407, USA · Beacom College of Computer and Cyber Sciences Dakota State University Madison, SD 57042 USA · Idaho National Laboratory +2
Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
Jun 8, 2026Joyce Karen Pelaez, Siqi Zhang, Hoo Sang KoAdditive Manufacturing Multimodal Graph Learning
Department of Industrial Engineering, Southern Illinois University Edwardsville, Box 1805, Edwardsville, 62026, IL, USA.
Learning and Adaptation in Wire Arc Additive Manufacturing Bead Geometry Control
May 27, 2026Chen-Lung Lu, John WenAdditive Manufacturing Adaptive Control
Rensselaer Polytechnic Institute, Troy, NY
RocketSmith: Agentic Additive Manufacturing of High-Powered Rockets
May 25, 2026Peter Pak, Jesse Barkley, Rumi Loghmani +3Agentic Workflows Additive Manufacturing
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA · Tripoli Rocketry Association, Pittsburgh, PA, USA · Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

Oct 21, 2026J/K move · Enter open · S save

AcoustiPrint: Physics-Guided Neural Design of Manufacturable Acoustic Structures

Shanmuka Sadhu, Harshvardhan C. Takawale, Yehia Zakaria, Ahmed Allam+1

Department of Computer Science, University of Maryland, College Park · Department of Mechanical and Materials Engineering, University of Cincinnati

Inverse Design Physics-Informed ML Additive Manufacturing

Open paper AskPDF arXiv

Recent work in additive manufacturing and neural inverse design enables the creation of complex three-dimensional structures to manipulate physical fields, yet these designs must also adhere to discrete, non-differentiable fabrication constraints. Current methods limit the design space by imposing differentiable soft manufacturability loss penalties, or by fixing structures through post-processing, causing a discrepancy between the geometry optimized by the physics model and the final fabricated result. Thus, we present AcoustiPrint, a physics-informed neural architecture for constrained inverse design that represents three-dimensional geometries with an implicit neural representation (INR) and passes each candidate through a printability operator before evaluating its final response. The printability operator executes layer-wise self-support, discrete material selection, and whole-body connectivity, assuring fabrication feasibility. To allow for backpropagation through the discrete non-differentiable operations, we use surrogate gradients for the backward pass and a self-supervised printability objective that progressively drives the INR toward a valid design domain. We demonstrate AcoustiPrint for personalized transcranial focused ultrasound, where inter-patient differences in acoustic properties and head geometry deform wave propagation and requires patient-specific acoustic focusing. AcoustiPrint jointly optimizes three-dimensional geometry and material composition through differentiable wave physics while ensuring manufacturability. Experiments show manufacturable acoustic structures with enhanced focal accuracy and over density-based and phase-based baselines, demonstrating a generalized methodology for neural design through non-differentiable manufacturing constraints and differentiable physics.