# Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning

arXiv: https://arxiv.org/abs/2609.19347 · HTML: https://mlsanity.com/paper/2609.19347/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [How Can Large Language Models Help Humans in Design and Manufacturing?](https://mlsanity.com/paper/2307.14377/) (2023)
- [AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing](https://mlsanity.com/paper/2406.00031/) (2024)
- [LLM-3D Print: Large Language Models To Monitor and Control 3D Printing](https://mlsanity.com/paper/2408.14307/) (2024)
- [AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing](https://mlsanity.com/paper/2501.17784/) (2025)
- [Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction](https://mlsanity.com/paper/2304.05685/) (2023)
- [Agentic Additive Manufacturing Alloy Evaluation](https://mlsanity.com/paper/2510.02567/) (2025)
- [Singularity-aware motion planning for multi-axis additive manufacturing](https://mlsanity.com/paper/2103.00273/) (2021)
- [Planning Jerk-Optimized Trajectory with Discrete-Time Constraints for Redundant Robots](https://mlsanity.com/paper/1909.06570/) (2019)
- [Towards Intelligent Cooperative Robotics in Additive Manufacturing: Past, Present and Future](https://mlsanity.com/paper/2408.04827/) (2024)
- [Co-Optimization of Tool Orientations, Kinematic Redundancy, and Waypoint Timing for Robot-Assisted Manufacturing](https://mlsanity.com/paper/2409.13448/) (2024)
- [Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection](https://mlsanity.com/paper/2603.22118/) (2026)

## Abstract (arXiv)

Robotic additive manufacturing (AM) extends material-extrusion printing beyond gantry kinematics but makes process planning robot-dependent. A slicer-generated plan that appears favorable in part coordinates can become infeasible or robotically unfavorable on a manipulator because slicer-process decisions and part orientation determine the generated path, while part orientation and workspace placement affect its kinematic realization. Existing AM tools, large language model (LLM)-based decision-support methods, and digital-shadow systems do not provide integrated pre-execution evaluation of these coupled decisions. This paper presents agentic robotic additive manufacturing (A-RAM), an agent-specialist-tool framework that converts user intent and a part file into traceable, execution-ready plans. The LLM interprets manufacturing objectives and constraints, identifies prescribed and searchable planning variables, and encodes this reasoning in a schema-constrained request; a deterministic Planning Agent instantiates the corresponding search workflow, while domain tools compute quantitative evidence for slicing, placement, inverse kinematics, trajectory timing, Joint-6 jerk, and extrusion. The framework is evaluated on a six-axis robotic-arm AM cell through three case studies covering expert-specified planning, goal-only planning, objective-dependent infill screening, and geometry-dependent orientation-placement selection. Across the evaluated candidate sets, selected plans achieve up to 53.5% lower maximum Joint-6 jerk and 48.3% lower mean absolute Joint-6 jerk than the least favorable valid candidates, while objective-specific infill screening yields motion-plan completion times up to 40.1% shorter and extrusion paths up to 12.7% shorter than the corresponding least favorable screened patterns.