journal article·2025-12-04

Scaled additive manufacturing for geometry-centric nondestructive evaluation: a case study on five distinct railway sleeper models

Ferhat Çeçen

Publisher page

Abstract

This study introduces an experimental non-destructive evaluation (NDE) methodology to systematically compare the modal behaviour of five geometrically distinct railway sleeper designs using scaled 3D-printed prototypes and precision dynamic testing. Full-scale modal investigations are constrained by size, reinforcement complexity, variability in concrete production, and high costs of fabrication and testing. By employing additive-manufactured miniature specimens under controlled free – free boundary conditions, these challenges are overcome, establishing a repeatable platform that isolates geometry-induced vibrational behaviour. Across the five sleeper types, distinct differences in resonance frequencies (503–818 Hz), damping ratios (0.56–0.83%), and CMIF peak amplitudes (5,068–9188) were identified, reflecting the influence of section geometry, inertia distribution, and modal mass. The methodology further enabled the introduction and benchmarking of a newly developed H-type geometry, not previously applied in railway practice, whose inclusion in the comparison demonstrated the tangible value of early-stage screening. Such comparative characterisation allows sleeper performance to be ranked reliably without full-scale production, providing a fast, cost-effective, and non-destructive route to identify configurations with improved vibrational resilience. The proposed framework offers a scalable tool for designing long-life, high-performance railway superstructure components, while also contributing to broader NDE and modal topology optimisation (MTO) practices in dynamically loaded engineering structures.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Additive Manufacturing Materials and Processes · Cellular and Composite Structures

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Suleyman Demirel University

Funding

Scientific and Technological Research Council of Türkiye

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