Case study report on design, manufacturing and digital representation of a DED-Arc steel node for construction

TL;DR

This paper explores the design and manufacturing of a steel node using DED-Arc technology, aiming to improve construction efficiency and reduce waste through digital twin integration.

Contribution

The study introduces a framework integrating design, manufacturing, and geometry data for DED-Arc steel nodes, enabling first-time-right fabrication and behavior prediction.

Findings

DED-Arc allows near-net-shape production of complex steel nodes, reducing manual welding and machining.

Digital Twin data supports stress distribution predictions and manufacturing optimization.

Early consideration of manufacturing constraints enhances design efficiency and reduces prototyping.

Abstract

This report presents a comprehensive investigation into the design, manufacturing, and evaluation of a DED-Arc (also known as Wire Arc Additive Manufacturing, WAAM) Y-Node for the construction industry. While conventional steel nodes for such applications are typically fabricated by welding multiple segments from cut plates or by complex castings, DED-Arc enables individual near-net-shape production of geometrically complex, force-flow optimized components while reducing the need for manual welding and machining. Focusing on the challenges of slicing and manufacturing strategy, such as collision avoidance between the torch and the built component, and guaranteeing torch accessibility in regions with pronounced overhangs, the study highlights the relationship between geometric freedom, path planning complexity, and manufacturing optimization. It emphasizes the importance of early consideration of manufacturing process constraints to enhance design efficiency. The integration of design, manufacturing process, and geometry data within a framework aiming towards a Digital Twin (DT) structure is thoroughly explored with the goal to support a first-time-right fabrication without the need for prototyping, thus reducing material waste. Moreover, the paper demonstrates the role of DT data in predicting component behavior, offering insights into stress distribution predictions influenced by manufacturing strategy. This research contributes to advancing methods for component behaviour analysis and optimization, with significant implications for the construction industry.

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