Integrated numerical modelling and deep learning for multi-layer cube deposition planning in laser aided additive manufacturing

ABSTRACT

Heat accumulation is a critical problem in continuous multi-layer laser aided additive manufacturing (LAAM) process, resulting in inhomogeneous mechanical properties and non-uniformity in the deposited height which can deteriorate the deposition process. This work presents a new integrated finite element (FE) simulation and machine learning approach to select a multi-layer laser infill toolpath planning strategy for fabricating quadrilateral parts to minimise localised heat accumulation during the deposition process. After one layer deposition simulation, the approach employs a Temperature-Pattern Recurrent Neural Networks (TP-RNN) model to predict the temperature field after the next layer deposition for each of the candidate infill toolpaths, and a process parameters inspired thermal field evaluation method to select the best candidate toolpath. The approach would significantly improve the computational efficiency of the laser infill toolpath planning, which was validated by improving the flatness of the 20-layer cube deposition samples with two dimensions (20 mm × 20 mm and 30 mm × 30 mm).

KEYWORDS:

• Laser aided additive manufacturing
• multi-layer deposition
• thermal analyses
• recurrent neural networks
• toolpath planning

Acknowledgement

This research was supported by Agency for Science, Technology and Research (A*STAR), Republic of Singapore, under the IAF-PP program “Integrated large format hybrid manufacturing using wire-fed and powder-blown technology for LAAM process”, Grant No: A1893a0031.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

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

Additional information

Funding

This research was supported by Agency for Science, Technology and Research (A*Star), Republic of Singapore, under the IAF-PP program ‘Integrated large format hybrid manufacturing using wire-fed and powder-blown technology for LAAM process’, Grant No: A1893a0031.

Notes on contributors

K. Ren

Kai Ren is a research scientist at Singapore Institute of Manufacturing Technology.

Y. Chew

Youxiang Chew is a research scientist at Singapore Institute of Manufacturing Technology.

N. Liu

Ning Liu is a development scientist at Advanced Remanufacturing and Technology Centre.

Y. F. Zhang

Yunfeng Zhang is an associate professor at National University of Singapore.

J. Y. H. Fuh

Jerry Ying Hsi Fuh is a professor at National University of Singapore.

G. J. Bi

Guijun Bi is a senior research scientist at Singapore Institute of Manufacturing Technology.