Bridging the Gap Between AI and Reality • Rhodes, Greece
Time: Wednesday, 5.11
Room: Room C
Authors:
Abstract: **Moved to LCNC Session 1, Nov. 3rd at 12.00 (Room C).** The characterisation of anisotropic behaviour in additively manufactured maraging steel, especially under varying build orientations and heat treatment conditions, remains a critical challenge in materials science. Traditional methods for exploring mechanical properties such as yield strength, hardness, and plastic anisotropy across these conditions require considerable coding expertise, manual configuration, and iterative visualisation. This paper presents a novel methodology using the Low-Code/No-Code (LC/NC) platform WAVE, designed to abstract and automate this process, so as to enable non-technical domain experts to systematically explore, compare, and analyse interpolation-based predictions. By bridging LC/NC paradigms with material characterisation workflows, this study demonstrates how domain-specific methodologies can democratise data-driven insights in scientific domains that are traditionally reliant on programmers. Preliminary feedback from materials researchers highlights WAVE's potential not only as an educational tool but also as a research accelerator, facilitating iterative hypothesis refinement and design-space exploration in additive manufacturing.