16 citations · 40 across the 11 of their papers we have counts for
11 papers
AI Agentic Selective Laser Sintering Process Optimization
Peter Pak, Victor Alvarado, Amir Barati Farimani
Agentic systems enable the intelligent automation of complex workflows, specific to additive manufacturing this is applicable for complex tasks such as process parameter optimizati…
AgentsCAD: Automated Design for Manufacturing of FDM Parts via Multi-Agent LLM Reasoning and Geometric Feature Recognition
Emmanuel George, Christopher Keefe, Peter Pak +1
Parts manufactured with Fused Deposition Modeling (FDM) often require Design for Additive Manufacturing (DFAM) modifications to ensure printability, structural integrity, and reduc…
RocketSmith: Agentic Additive Manufacturing of High-Powered Rockets
Peter Pak, Jesse Barkley, Rumi Loghmani +3
RocketSmith is an agentic system which intelligently automates the DFAM process for the development of high powered rockets suitable for launch. The system utilizes a large languag…
Domain Adapted Large Language Models for Additive Manufacturing
Peter Pak, Amir Barati Farimani
This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using…
Image2Gcode: Image-to-G-code Generation for Additive Manufacturing Using Diffusion-Transformer Model
Ziyue Wang, Yayati Jadhav, Peter Pak +1
Mechanical design and manufacturing workflows conventionally begin with conceptual design, followed by the creation of a computer-aided design (CAD) model and fabrication through m…
Agentic Additive Manufacturing Alloy Evaluation
Peter Pak, Achuth Chandrasekhar, Amir Barati Farimani
Agentic systems enable the intelligent use of research tooling, augmenting a researcher's ability to investigate and propose novel solutions to existing problems. Within Additive M…
AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing
Peter Pak, Amir Barati Farimani
In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize…
LLM-3D Print: Large Language Models To Monitor and Control 3D Printing
Yayati Jadhav, Peter Pak, Amir Barati Farimani
Industry 4.0 has revolutionized manufacturing by driving digitalization and shifting the paradigm toward additive manufacturing (AM). Fused Deposition Modeling (FDM), a key AM tech…
BeadSight: An Inexpensive Tactile Sensor Using Hydro-Gel Beads
Abraham George, Yibo Chen, Atharva Dikshit +2
In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sig…
Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers
Francis Ogoke, Peter Myung-Won Pak, Alexander Myers +4
Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance.…
ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning
Peter Myung-Won Pak, Francis Ogoke, Andrew Polonsky +5
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring dat…