Development of a manufacturability predictor for periodic cellular structures in a selective laser melting process via experiment and ANN modelling

ABSTRACT

Manufacturability analysis is a critical step before manufacturing to reduce costs and risks. It is used widely in conventional manufacturing (CM) processes. However, to the best of our knowledge, there is no natural method to evaluate the manufacturability of additive manufacturing (AM) processes that have more uncertainty-derived risks and costs than CM processes. A clear definition of the manufacturability of AM processes has not been established, and there is no standard to check whether a component is manufactured successfully by an AM process, particularly for porous complex components. This study introduces the development of a new machine learning-based method to solve the problem mentioned above. It is based on the statistical measurement of experimental samples. The proposed method can be used to perform the manufacturability analysis for periodic cellular structures printed by a selective laser melting (SLM) process. A novel definition of the manufacturability of the SLM-ed periodic cellular structure was proposed. Experimental results indicate that the developed learning model (ANN model) can achieve up to 94% classification accuracy and 96% prediction accuracy, which satisfies the application requirements of the AM industry. Moreover, the developed model can be adapted for the manufacturability analysis of different AM processes.

KEYWORDS:

• Selective laser melting
• Artificial neural network
• periodic cellular structure
• manufacturability analysis
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Liping Ding

Dr. Liping Ding is an associate professor at Nanjing University of Aeronautics and Astronautics in China. His research interests include additive manufacturing, machine learning, and intelligent equipment.

Shujie Tan

Shujie Tan is a PhD candidate at Nanjing University of Aeronautics and Astronautics. His research interests include design, planning and optimization for additive manufacturing (AM).

Wenliang Chen

Dr. Wenliang Chen is a full professor at Nanjing University of Aeronautics and Astronautics in China. He is currently the leader of the lab of aircraft intelligent manufacturing. His research interests include CAD/CAE technology, sheet metal forming and the intelligent assembly of aircraft.

Yaming Jin

Dr. Yaming Jin is currently a senior engineer of Nanjing Profeta Intelligent Technology Co., Ltd. His current research interests include additive manufacturing, machine learning and multiferroic materials.

Yuchun Sun

Dr. Yuchun Sun is a professor at the School and Hospital of Stomatology, Peking University. He is a leader of the National Engineering Laboratory for Digital and Material Technology of Stomatology, one of the leading talents of capital science and technology, the chief scientist of the National Key Research and Development Program, and the director of key projects of the National Natural Science Foundation of China. His research topic is the artificial intelligent design and precision bionic manufacturing of complex dental restoration.

Yicha Zhang

Dr. Yicha Zhang is now an associate professor at the University of Technology Belfort-Montbeliard (UTBM). His main research topics include design, planning and optimization for additive manufacturing (AM). He was elected as an associate member of CIRP (International Academy for Production Engineering) in 2020 and awarded the CIRP Taylor Medal for his contribution to the design & planning for AM in 2021.