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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI-Enabled Materials Processing: Integrating Accelerated Experimental Workflows and Processing-Aware Machine Learning
Presentation Title Framework Towards Design of Workflows for Self-driving Laboratories (Application in Additive Manufacturing)
Author(s) Evelyn Dede Quansah
On-Site Speaker (Planned) Evelyn Dede Quansah
Abstract Scope Self-driving laboratories promise accelerated materials discovery, but their autonomy is limited in terms of the ability to choose and overlay different workflows on the same facility, rather than automate one fixed workflow. This work presents framework for generating workflows of an autonomous research facility before optimization or machine learning begins. The facility is formalized as a closed-universal set system consisting of binding triplets (namely a unit bock) of nouns, verbs, and process-model constructs. Using this formulation, candidate workflows are represented as typed block graphs and checked for structural validity and semantic validity. The framework is transformed in machine readable logic to produce valid workflows. Mathematical objects are defined over workflows to enhance futuristic decision-making. This framework provides a general foundation for trustworthy autonomous workflow design in materials laboratories and is demonstrated using an additive manufacturing facility context (AIMPF). We aim to support review-and-confirm operation for future self-driving laboratory deployment.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Other, Machine Learning

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