AI Agentic Selective Laser Sintering Process Optimization
Peter Pak, Victor Alvarado, Amir Barati Farimani
Abstract
Agentic systems enable the intelligent automation of complex workflows, specific to additive manufacturing this is applicable for complex tasks such as process parameter optimization for mechanical properties. This work investigates the AI enabled agentic process optimization within Selective Laser Sintering (SLS) to iteratively improve the tensile and flexural properties of 3 different materials on the Inova Mk1. These materials include PA12 GF, PA11 Onyx, and PA12 Blend (volume mixture of 25% PA12 GF and 75% PA12 White) and with using knowledge from previous builds and minimal guidance from the user, the agentic system was able to optimize process parameters over a small number of iterations to achieve comparable TDS specified mechanical properties. This work showcases the ability for an agentic system to continually learn from updated data, enabling the intelligent automation of complex tasks such as process parameter optimization for selective laser sintering.
Create a lesson
Related papers
One Model, Many Minds: Unlocking Multi-Agent Synergy in a Single Agent via Mixture of Roles
Zhichen Zeng, Huiyuan Chen, Jingru Cheng et al.
Dynamic Haven Selection for Multi-Agent Pickup and Delivery in Constrained Warehouses
Taisei Hirayama, Kohei Yoshida, Hiroki Sakaji et al.
Fixed-Haven Reservation for Online Multi-Agent Pickup and Delivery in Dense Warehouses
Taisei Hirayama, Kohei Yoshida, Hiroki Sakaji et al.
Risks and Controls for Multi-Agent Systems: an analytical framework for deployment of AI agents across organisational boundaries
Alistair Reid, Simon O'Callaghan, Dustin Venini et al.
Zero-Shot Self-Orchestration with Ledger-Based Control for Improved LLM Coding Performance
Victor Gao, Vida Khosrowshahi, Ali Khosrowshahi et al.
Praxist: From Experimental Artifacts to Solution Lineages
Jin Li, Ahmed Murtadha, Zhiyu Wang et al.