Evaluation of Metal Lattice Structures With X-Ray Micro-Computed Tomography: Dimensional Accuracy and Manufacturability
Additive manufacturing enables product designers to incorporate complexity onto their designs on multiple size scales. Semi-automated computer-aided design methods, such as topology and lattice optimization, have emerged as software tools for applications where part consolidation and weight reduction are desired. Applications outside of this scope are scarcer. Highly efficient heat exchangers and thermal batteries harness shape complexity to optimize surface area and fluid flow for maximized heat transfer. Moving to smaller size scales, rough surface texture in titanium implants improves osseointegration at the bone-metal interface. Still, a more delicate control of hierarchical complexity and sub-millimeter sized features would unlock a widely unexplored frontier of new design possibilities. Sub-millimeter sized part features could be designed to interact with fluids, gases and waves to provide new product functions and novel transdisciplinary applications.
However, the complexity of a design can respectively affect the manufacturing process. In powder bed fusion, the diameter, power and speed of the laser spot and the resulting size of the meltpool define the attainable feature resolution and accuracy in comparison with the original design intent. Sudden variations in cross-section and fragmentary laser vectoring can cause process defects throughout the volume and surface area of the parts. For load bearing components, the criticality of internal pores and surface defects is evident. As are result, the mechanical properties, fatigue resistance, and corrosion susceptibility of the parts are affected. For non-load bearing applications, the dimensional accuracy and surface finish of the manufactured components remain a priority. X-ray computed tomography is an often used, non-destructive verification and testing method for additively manufactured parts to assess accuracy and part quality. It can provide a detailed, volumetric representation of a part with internal features. In addition to surfaces, the scan is able to yield information on internal porosity and possible defects.
This paper examines the design accuracy of metal lattice structures with X-ray micro-computed tomography. The theoretical values are compared with the as-printed and scanned samples. A sample set of three 316L stainless steel lattice cubes was manufactured with powder bed fusion. Each cube had an edge length of 20 mm. To better understand the effects of the manufacturing process on the internal porosity, a set of four solid cubes was printed with different machine parameters, namely laser power and laser speed. The as-printed geometries were examined with X-ray micro-computed tomography and compared with the as-designed geometries for surface area, surface deviation, part volume and internal porosity. As a result of the sample packing volume within the X-ray apparatus, the voxel size for the lattices was 44.4 microns and 33.1 microns for the solid cubes. The 16-bit grayscale raw images obtained from the computed tomography scans were first binarized and then reconstructed in a tessellated format for comparison.
Evaluation of Metal Lattice Structures With X-Ray Micro-Computed Tomography: Dimensional Accuracy and Manufacturability
Category
Technical Paper Publication
Description
Session: 06-04-01 Design for Additive Manufacturing
ASME Paper Number: IMECE2020-23869
Session Start Time: November 19, 2020, 03:30 PM
Presenting Author: Tuomas Puttonen
Presenting Author Bio: Tuomas Puttonen is a doctoral candidate working as part of the Advanced Manufacturing and Materials (AM2) group at the Aalto University, Finland. In 2017, he received a Master's degree in mechanical engineering with a major in machine design and product development. Before the doctoral position, he has gathered a few years of work experience as a mechanical designer in R&D. As a researcher, he is interested to study how the advancements and interplay between computational 3D modeling, simulation, and additive manufacturing could be combined effectively into product design.
Authors: Tuomas Puttonen Aalto University