Engineering innovation happens where design, manufacturing, technology, and human judgement come together.
At ADML, we study and shape these connections. Our research spans the methods, knowledge, tools, and technologies through which ideas become purposeful and manufacturable solutions. We are interested not only in how products and manufacturing systems are created, but also in how engineering knowledge and advanced technologies can strengthen manufacturing capacity—the ability to produce, adapt, scale, and respond to changing societal needs.
While our research continues to evolve, it is united by a commitment to rigorous engineering, intelligent decision-making, and meaningful real-world impact. Through our work, we aim to contribute to more capable, resilient, and responsible manufacturing systems that support technological progress, economic prosperity, and societal well-being.
Spanning generative design, in-process control, and life-cycle accounting.
Abbreviated as 'AM' and commonly called 3D printing, Additive Manufacturing have demonstrated its competence in speeding up engineering processes through rapid prototyping. At ADML, researchers work on various cutting-edge AM technologies.
Lattice · SLM · EBM · LPBF · FDM
All 7 areasADML brings machine learning and data-driven methods to design and manufacturing — learning from process, sensor, and quality data to make additive and advanced manufacturing more predictable and better understood.
ML · Surrogate · Monitoring
All 4 areasGlobalization places emphasis sustainability and environmental friendliness over business and industry. This echoes with ADML's research principle and research works are conducted in this field.
LCA · Eco-design · Efficiency
All 3 areasRecent work from the lab and its collaborators.
Lingchen Kong, Yaoyao Fiona Zhao
Advanced ScienceOPEN ACCESS
Manuel Sage, Bi Cheng Zhao, Yaoyao Fiona Zhao
Zenodo (CERN European Organization for Nuclear Research)OPEN ACCESS
Lingchen Kong, Yaoyao Fiona Zhao
Smart Materials in ManufacturingOPEN ACCESS
Research software the lab authors and maintains alongside its papers.
A contact sheet of the lab’s design studies, simulations, sketches, and life in the lab — each frame captioned.
Awards, grants, and releases from the lab — and the funded places open to join it.
ADML has proposed the reproducibility checklist for machine learning-based AM process monitoring systems!
ADML repository with machine learning learning resources is now available
Chonghui Zhang was awarded MEDA from 2022 to 2024
2022–2024Manuel Philipp Sage was awarded MEDA from 2021 to 2023
2021–2023The councils, companies, and partner labs that fund and build alongside the lab.
