Data-driven Process Parameter Optimisation for Laser Wire Metal Additive Manufacturing

Roberts, Matthew and Xia, Min and Kennedy, Andrew (2022) Data-driven Process Parameter Optimisation for Laser Wire Metal Additive Manufacturing. In: Proceedings of the 27th International Conference on Automation & Computing :. IEEE, GBR, pp. 1-6. ISBN 9781665498081

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Abstract

Laser Wire Additive manufacturing (LWAM) requires a clear understanding of process parameters and their effects on the geometry and wider material properties of the parts produced to support the production of consistent, repeatable quality parts. Furthermore, its ability to capitalise on using novel alloys depends on efficient characterisation of optimum process parameters. In this work, a method for identifying the range of usable parameters is presented, which produces sufficient data to train Cascade Forward Neural Networks, which are capable of predicting process windows and basic LWAM track geometries for 316L stainless steel. The performance of these networks provides the foundation for further work to identify optimum process parameters and, through transfer learning, may reduce the experimental requirements for the process development of other alloys.

Item Type:

Contribution in Book/Report/Proceedings

Additional Information:

©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Subjects:

?? additive manufacturinglaser wire metal additive manufacturingdirected energy depositionmachine learning ??

Departments:

Faculty of Science and Technology > Engineering

ID Code:

178485

Deposited By:

ep_importer_pure

Deposited On:

02 Nov 2022 14:30

Refereed?:

Yes