Title: A generative AI design framework for performant-influenced decision-making using fused filament fabrication and an adaptive neuro fuzzy inference system

Authors: Ezekiel Yorke; Boppana V. Chowdary

Addresses: Faculty of Engineering, The University of the West Indies, St. Augustine Campus, Trinidad and Tobago ' Faculty of Engineering, The University of the West Indies, St. Augustine Campus, Trinidad and Tobago

Abstract: The manufacturing industry has witnessed a plethora of advancements in recent decades, notably in the fused filament fabrication (FFF) process. However, seeking ways of accurately predicting part performance for satisfying decision-making criteria based on manufacturing parameters has remained a challenge due to increased volumes of manufacturing process parameters. This research therefore sought to address this challenge by using generative artificial intelligence (GenAI) built on an adaptive neuro-fuzzy inference system (ANFIS) and a genetic algorithm (GA) approach. A design framework was subsequently applied to explore ways of mitigating business-decision uncertainty by analysing the compatibility between data acquisition, analysis, training and optimisation. Findings revealed benefits in the use of a GenAI-ANFS-GA solution for situations where noise or insufficient fuzziness in data existed. Recommendations for future research were also provided, which inferred the effectiveness of integrating such frameworks into existing workflows and therefore derive better business-oriented strategies toward more reliable output.

Keywords: ANFIS; generative artificial intelligence; genetic algorithm; fused filament fabrication; FFF; fuzzy machine learning; FML.

DOI: 10.1504/IJGAIB.2026.151821

International Journal of Generative Artificial Intelligence in Business, 2026 Vol.1 No.1/2, pp.155 - 174

Received: 26 Jul 2025
Accepted: 03 Aug 2025
Published online: 20 Feb 2026 *