Data-driven prediction of next-layer melt pool temperatures in laser powder bed fusion based on co-axial high-resolution Planck thermometry measurements

Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

(2021) Qiming Zhu et al.   COMPUTATIONAL MECHANICS

Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning: The state-of-the-art and research challenges

(2021) Peng Wang et al.   Journal of Manufacturing Processes

Digitally twinned additive manufacturing: Detecting flaws in laser powder bed fusion by combining thermal simulations with in-situ meltpool sensor data

(2021) R. Yavari et al.   MATERIALS & DESIGN

Crack free metal printing using physics informed machine learning

(2021) B. Mondal et al.   ACTA MATERIALIA

Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives

(2021) Mojtaba Mozaffar et al.   JOURNAL OF MATERIALS PROCESSING TECHNOLOGY

FROM SCAN STRATEGY TO MELT POOL PREDICTION: A NEIGHBORING-EFFECT MODELING METHOD

(2020) Zhuo Yang et al.   JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING

Additive manufacturing: scientific and technological challenges, market uptake and opportunities

(2018) Syed A.M. Tofail et al.   Materials Today

Common defects and contributing parameters in powder bed fusion AM process and their classification for online monitoring and control: a review

(2017) Ehsan Malekipour et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Process defects andin situmonitoring methods in metal powder bed fusion: a review

(2017) Marco Grasso et al.   MEASUREMENT SCIENCE and TECHNOLOGY

The metallurgy and processing science of metal additive manufacturing

(2016) W. J. Sames et al.   INTERNATIONAL MATERIALS REVIEWS