A prediction and compensation scheme for in-plane shape deviation of additive manufacturing with information on process parameters

ABSTRACT

Shape fidelity is a critical issue that hinders the wider application of Additive Manufacturing (AM) technologies. In many AM processes, the shape of a product is usually different from its input design and the deviation usually depends on certain process parameters. In this article, we aim to improve the shape fidelity of AM products through compensation, with the information on these parameters. To achieve this, a two-step hierarchical scheme is proposed to predict the in-plane deviation of the product shape, which relates to the process parameters and the two-dimensional input shape. Based on this prediction procedure, a shape compensation strategy is developed that greatly improves the dimensional accuracy of products. Experimental studies of fused deposition modeling processes validate the effectiveness of our proposed scheme in terms of both predicting the shape deviation and improving the shape accuracy.

KEYWORDS:

• Additive manufacturing
• Gaussian process model
• in-plane shape deviation
• kernel smoothing

Acknowledgements

The authors thank the Editor, the Department Editor, and three anonymous referees for their valuable comments that have significantly improved the quality of this article.

Additional information

Funding

Fugee Tsung's research was supported by the Hong Kong RGC General Research Fund 16203917 and SSTSP FP302.

Notes on contributors

Longwei Cheng

Longwei Cheng is a Ph.D. candidate in the Department of Industrial Engineering and Logistics Management at Hong Kong University of Science and Technology. He received a bachelor's degree in automation from the University of Science and Technology of China in 2014. His research interests include machine learning, statistical modeling, and quality control.

Andi Wang

Andi Wang is a Ph.D. student in the Department of Industrial and System Engineering at the Georgia Institute of Technology. Previously, he obtained a Ph.D. degree from the Hong Kong University of Science and Technology in 2016 and worked as a post-doc research associate for a year. He obtained his bachelor's degree in statistics from Peking University, China. His research interests include machine learning, statistical modeling, and process control for data generated from complex systems, especially advanced manufacturing processes.

Fugee Tsung

Fugee Tsung is Professor of the Department of Industrial Engineering and Decision Analytics (IEDA), Director of the Quality and Data Analytics Lab, at the Hong Kong University of Science & Technology (HKUST). He is a Fellow of the Institute of Industrial Engineers (IIE), Fellow of the American Society for Quality (ASQ), Fellow of the American Statistical Association (ASA), Academician of the International Academy for Quality (IAQ), and Fellow of the Hong Kong Institution of Engineers (HKIE). He is Editor-in-Chief of Journal of Quality Technology (JQT), Department Editor of IIE Transactions, and Associate Editor of Technometrics. He has authored over 100 refereed journal publications and was awarded the Best Paper Award from IIE Transactions in 2003 and 2009. He received both his M.Sc. and Ph.D. from the University of Michigan, Ann Arbor, and his B.Sc. from National Taiwan University. His research interests include quality engineering and management in manufacturing and service industries, statistical process control and monitoring, industrial statistics, and data analytics.