Wire Arc Additive Manufacturing (WAAM) holds significant potential for the fabrication of intricate, medium to large-scale metallic components. However, current research predominantly targets lab-scale specimens, often exploring limited ranges of process parameters and overlooking the complex interactions between thermal, geometrical, and mechanical factors. In this study, a comprehensive experimental program is conducted to systematically investigate the influence of key process parameters, namely current, voltage, wire feed speed, and travel speed, across three Gas Metal Arc Welding (GMAW) transfer modes: Cold Metal Transfer (CMT), Conventional Spray (S-GMAW), and Pulsed Current (P-GMAW). 3Dprint AM 46 carbon steel was used as the feedstock material. The investigation proceeded in three stages: development of a broad process window (produced by CMT), characterisation of 10-layer walls (produced by CMT, S-GMAW and P-GMAW), and mechanical testing of large-scale walls (produced by P-GMAW) to construct a process–property map. Experimental characterisation revealed that the heat input (HI) and the wire feed speed to travel speed (WFS/TS) ratio govern the cooling time (Δt₈/₅), which exhibited a linear dependence on HI. Bead width and height were governed by combined effects of HI and WFS/TS, while hardness, yield strength, and ultimate tensile strength followed inverse power-law correlations with HI. Ultimate strain showed a positive correlation with HI. In parallel, an analytical modelling framework is proposed to establish predictive equations linking process parameters with thermal cycles, deposition geometry, and mechanical properties. These models aim to accelerate process planning and enable property-driven slicing strategies for WAAM structural applications. ... Read more

Shareable tables generated by Foundational Models (FM) are increasingly adopted in industry and scientific applications. With the advent of such data, governance of it becomes an ever more pressing matter. Towards this, watermarking is shown to be a promising approach. While existing watermarking approaches successfully embed watermarks in natural language, they fall short for tabular data due to the susceptibility of token-based representations to post-processing and the arbitrary column ordering. We propose a novel watermarking scheme for tabular data, Tabular Token(less) Watermark (TabTokWak), which embeds a watermarking signal on FM-generated tabular value and its tokenized representation. To be invariant to table generation order, our token watermark uses a seeded pattern to derive its watermark signal. In our experiments on five real-world datasets and a representative tabular FM, we demonstrate that TabTokWak enables effective data governance with high detection efficiency and minimal impact on data quality. Therewith, empirically demonstrating TabTokWak’s detectability and its robustness with a favorable quality trade-off. Code online: https://gitlab.ewi.tudelft.nl/dmls/publications/tabtokwak. ... Read more