Now Enrolling · 2026

AI-Powered Intelligent
3D Printing Research Program

A cutting-edge research program at the intersection of artificial intelligence and additive manufacturing — led by Prof. Yang Yang of San Diego State University, in collaboration with Haddee Education.

8 Live Sessions + 1-Year Mentorship

2 hrs/session · Online

HS & Early Undergrad

Possible Journal Co-Authorship

Submit Parent Inquiry Form

About the Program

Where Artificial Intelligence Meets the Future of Manufacturing

Haddee Education collaborates with San Diego State University (SDSU) to present this research program on Machine Learning Applications in Intelligent 3D Printing Systems. Guided personally by Professor Yang Yang, students explore the powerful intersection of additive manufacturing, materials science, and artificial intelligence.

Through 8 live group sessions and a full year of one-on-one mentorship, students develop hands-on skills in CAD design, machine learning, and defect detection — with real applications in aerospace, biomedical, and mechanical engineering.

The intelligent optimization algorithms developed in this program have been applied to the manufacturing of aerospace components at Boeing. High-achieving students may earn co-authorship on peer-reviewed journal submissions.

Program Mentor

Prof. Yang Yang

Asst. Professor · San Diego State University

Dept. of Mechanical Engineering

PhD in Bioengineering from UCLA, postdoctoral research at USC. Award-winning expert in bio-inspired 3D printing and machine learning with applications spanning aerospace, mechanical, and biomedical engineering.

50+

Publications

h=38

H-Index

NSF

CAREER Award

Published in Nature Communications · Science Advances · Advanced Materials · Energy & Environmental Science

Why This Program

What Sets This Program Apart

🚀

Real-World Industry Impact

The intelligent optimization algorithms developed in this program have been directly applied to the manufacturing of aerospace components at Boeing — giving students exposure to research that reaches the real world.

📄

Possible Journal Co-Authorship

High-achieving students can earn co-authorship on peer-reviewed journal submissions in AI, additive manufacturing, and materials science — based on depth of involvement, data analysis, and writing contributions.

🎓

Proven College Admissions Impact

Former participants have gained admission to UC Berkeley, Carnegie Mellon, Purdue, UCSD, UW-Madison, and more. Previous participants' research has won gold awards at international youth science and technology innovation competitions.

🔬

Interdisciplinary Frontier Research

Go beyond a single discipline. This program bridges AI, mechanical engineering, materials science, and biomedical applications — equipping students with future-ready skills in smart manufacturing and advanced materials.

Curriculum

8-Session Course Outline

Each session is 2 hours of online instruction · Plus 8–10 hrs/week independent research & writing

Session 1

Welcome & Intro to 3D Printing

Introduction to additive manufacturing, types of 3D printing (FDM, SLA, SLS). Icebreaker: design your nameplate in Fusion 360.

Session 2

Materials & In-Situ Sensors

Common 3D printing materials and properties. Introduction to in-situ monitoring with thermal and acoustic sensors.

Session 3

CAD Tutorial & 3D Printer Lab

Master CAD software to design 3D models. Learn the working principles of 3D printers hands-on.

Session 4

Machine Learning Basics

Supervised, unsupervised, and reinforcement learning. Case study: defect detection using ML. Mini-project: identify defects in sample prints.

Session 5

Deep Learning & Smart Manufacturing

Overview of deep learning and CNNs. Smart factories and adaptive printing. Guest speaker from industry or graduate research.

Session 6

Bio-Inspired Design & 3D Printing

Nature-to-printer: examples from biomedical and aerospace. Design activity: sketch and model a bio-inspired part in Fusion 360.

Session 7

ML + Bio-Inspired Design Combined

Applications of ML in bio-inspired design — mechanics, thermodynamics, and interface research.

Session 8

Project Presentations & Certificates

Team presentations with feedback from Prof. Yang. Certificate award and recommendation letter discussions.

Tools & Platforms

Fusion 360 SolidWorks TensorFlow PyTorch FDM / SLA 3D Printers Thermal Cameras Acoustic Sensors Python

Research Topics

Choose Your Research Focus

→AI-Driven Bio-Inspired 3D Printing: Current Trends, Challenges, and Future Prospects
→Machine Learning for 3D Printing: From Process Optimization to Defect Detection
→Machine Learning for Real-Time Monitoring and Control in 3D Printing
→AI-Generated Structures: The Future of Customized 3D-Printed Architectures
→Data-Driven Approaches for Predicting Material Behavior in Additive Manufacturing
→Learning from Nature: AI-Driven Bio-Inspired Design and Innovation Methods

Research Outcomes

What You'll Produce

📝

Research Paper or Project Report

A detailed research paper covering methodology, findings, and future directions in AI-driven additive manufacturing.

🤖

ML-Based Monitoring Prototype (Optional)

Deploy a simple AI-powered 3D printing monitoring system for advanced students.

🏆

Certificate & Recommendation Letter

Strong letters of recommendation from Prof. Yang for top-performing students.

Student Publications

→ IOP Science — Student co-authored peer-reviewed publication → MDPI Biomimetics — Student co-authored peer-reviewed publication

Your Mentor

About Prof. Yang Yang

Academic Background

→

PhD in Bioengineering

University of California, Los Angeles (UCLA) — joint program
→

Postdoctoral Researcher

Dept. of Industrial & Systems Engineering, USC
→

Assistant Professor of Mechanical Engineering

San Diego State University (SDSU)

Awards & Recognition

→
NSF CAREER Award — National Science Foundation award for outstanding young scientists
→
SME Outstanding Young Manufacturing Engineer Award — Society of Manufacturing Engineers
→
Research Journal 2022 Best Paper Award — "3D-Printed Nacre-Inspired Structures with Superior Mechanical and Flame-Retardant Properties"
→
Reviewer for Nature Communications, Advanced Materials, Small, Additive Manufacturing

Eligibility

Who Should Apply

🎒

HS & Early Undergrad

High school students and undergraduate freshmen/sophomores worldwide.

⚙️

AI / Engineering Interest

Strong curiosity in AI, 3D printing, mechanical, electrical, biomedical, or chemical engineering.

⏰

Time Commitment

Ability to commit 8–10 hrs/week for independent research, reading, and writing.

Application Process

Parents complete the Inquiry Form to receive the full program catalog and tuition details. Students then complete an entrance assessment test. The full process typically takes 7–10 business days. Early application is recommended to secure a spot.

AI-Powered Intelligent3D Printing Research Program