This paper introduces an innovative approach to large-format robotic additive manufac-turing (LFRAM) by implementing a parametric point array patterning (PAP) method within Rhino Grasshopper. Unlike conventional slicing algorithms that convert 3D models into layers, the pro-posed solution generates toolpath sequences directly from parametric models. The method-ology leverages integrated Grasshopper components to create customizable ribbed structures with adjust-able dimensions and spacing, offering enhanced flexibility for large-scale applications. Experi-mental validation was performed using a FANUC M-20iB/25 industrial robot equipped with a pellet extruder, with PETG selected for manufacturing a structural component measuring 1500 × 100 × 60 mm. The research was conducted within the PROMATAI project during the rapid prototyping of the Adjusted Grooved Feed Section of the extruder (WP2).
Abstract The rapid growth of data-intensive applications has necessitated the development of scalable and efficient architectures for cloud-based machine learning and data analysis. This study proposes a scalable, distributed, and fault-tolerant architecture designed to address t…
## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Transformation in Metallurgical Engineering: From Microstructure Analysis to Smart Manufacturing and Sustainable Production"** ### Alternative Title 2 (Process-Focused)**"Machine Learning and Deep Learning…
This conceptual paper proposes a novel quantum computing architecture specifically engineered for native genomic processing and generative synthetic biology. Traditional bioinformatics frameworks rely on binary silicon-based architectures, which introduce significant computationa…
Abstract: Deep learning has great potential for environmental monitoring, yet real-world applications often face challenges from large-scale, multimodal, and noisy datasets. We introduce GreenNet, a flexible and open-source framework that makes it easier to build and scale deep l…
Abstract Generative AI (GenAI) applications are non-deterministic. That is the same input can produce different outputs from run to run, and increasingly it is the prompt, not a line of code, that stands between an unpredictable model, and a system people can rely on. Despite thi…