journal article·2024-05-31Open access

Automated Quantification of Raster Orientation of Fused Filament Fabrication Components Using Ultrasonic Testing

Atik Amin, David A. Jack, Trevor J. Fleck

Applied Sciences· Volume 14· Issue 11

Abstract

An automated method for nondestructively characterizing the layer-by-layer raster orientation of additively manufactured components fabricated via the fused filament fabrication (FFF) process is presented, which utilizes full waveform capture of the ultrasonic signal paired with two-dimensional fast Fourier transform analysis. The proposed method extracts internal features of the fabricated component at various depths and then applies the two-dimensional Fourier transformation in the spatial domain to analyze the raster path and extract the orientation. Three material systems are studied: a standard polymer (Poly cyclohexylenedimethylene terephthalate glycol, PCTG), an engineered polymer (high-temperature nylon, HTN) and a carbon fiber-filled polymer (polyethylene terephthalate, PET-CF). Samples were fabricated using an industrial-grade FFF system and scanned using a high-resolution custom immersion ultrasonic platform. Studies were performed using both a 10 MHz and a 15 MHz spherically focused transducer, with the 10 MHz transducer yielding more accurate and more consistent results for the investigated material systems. The analyzed results show that the presented automated method can accurately identify the direction of the raster path with an error within 1°–2° in each of the first 9~10 deposited layers of the investigated PCTG and the PET-CF samples, and the first 14 layers of the HTN samples. This study provides an approach for the automated analysis of the internal features of FFF components using ultrasonic testing, which can further inform the quality control process, in turn increasing reliability and enabling acceptance of AM parts in various industries.
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Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization · Additive Manufacturing Materials and Processes

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

Affiliations

Department of Mechanical Engineering, Baylor University, Waco, TX 76798, USA

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