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References: AI and Machine Learning in Additive Manufacturing

  1. Generative design - Wikipedia - Explains AI-driven generative design methodology, how optimization algorithms explore the design space, and why additive manufacturing is the enabling fabrication technology for the organic geometries it produces.

  2. Topology optimization - Wikipedia - Technical overview of mathematical topology optimization — the computational engine behind many AI-assisted DfAM tools — covering objectives, constraints, and SIMP method foundations.

  3. Machine learning - Wikipedia - Overview of machine learning paradigms (supervised, unsupervised, reinforcement) applicable to AM quality control, parameter optimization, and in-process defect detection.

  4. Additive Manufacturing Technologies (3rd ed.) — Ian Gibson, David Rosen, Brent Stucker, Mahyar Khorasani — Springer, 2021 — Covers computational design methods including topology optimization, lattice design, and simulation-driven part development foundational to AI-assisted AM.

  5. Artificial Intelligence in Manufacturing — David Liff — SAE International — Overview of AI applications across manufacturing processes including predictive maintenance, computer vision quality inspection, and generative design for additive manufacturing.

  6. Hubs Knowledge Base - Hubs (Protolabs Network) - Covers generative design for manufacturing and how AI-optimized, topology-reduced parts are produced and validated via AM processes in professional production workflows.

  7. Selecting the right 3D printing process - Hubs (Protolabs Network) - Process selection in the AI era: generative design outputs typically require AM for fabrication, illustrating why AI and additive manufacturing are increasingly co-dependent.

  8. Formlabs Blog - Formlabs - Blog covering AI applications in resin printing including automated support generation algorithms, AI-driven print failure prediction, and machine learning quality control systems.

  9. Printables - Prusa Research - Community showcasing AI-generated and topology-optimized models, illustrating how generative design tools are increasingly accessible to high school makers and hobbyists.

  10. RepRap - RepRap Project - Open-source platform where the community explores AI-assisted slicing parameter optimization, automated bed leveling (ABL), and machine learning approaches to calibration.