A knowledge-based process planning framework for wire arc additive manufacturing

Yi Xiong, Audelia Gumarus Dharmawan, Yunlong Tang, Shaohui Foong, Gim Song Soh, David William Rosen

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


Wire arc additive manufacturing (WAAM) provides a rapid and cost-effective solution for fabricating low-to-medium complexity and medium-to-large size metal parts. In WAAM, process settings are well-recognized as fundamental factors that determine the performance of the fabricated parts such as geometry accuracy and microstructure. However, decision-making on process variables for WAAM still heavily relies on knowledge from domain experts. For achieving reliable and automated production, process planning systems that can capture, store, and reuse knowledge are needed. This study proposes a process planning framework by integrating a WAAM knowledge base together with our in-house developed computer-aided tools. The knowledge base is construed with a data-knowledge-service structure to incorporate various data and knowledge including metamodels and planning rules. Process configurations are generated from the knowledge base and then used as inputs to computer-aided tools. Moreover, the process planning system also supports the early-stage design of products in the context of design for additive manufacturing. The proposed framework is demonstrated in a digital workflow of fabricating industrial-grade components with overhang features.

Original languageEnglish
Article number101135
JournalAdvanced Engineering Informatics
Publication statusPublished - Aug 2020


  • Computer-aided design
  • Design for additive manufacturing
  • Directed energy deposition
  • Knowledge-based engineering
  • Process planning

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