The advent of Additive Manufacturing (AM) brought into existence a realm of new possibilities: 3D printed parts with high strength to weight ratio, minimal wastage, and significant lead and build time savings vis-à-vis the other traditional manufacturing methods. However, the same prudence of AM techniques in the production of optimized 3D-printed parts adds an equal amount of complexity in predicting part properties and material behavior.When it comes to the design of 3D-Printed parts, the G-Code instructions provide information on the toolpath’s movement and nozzle’s extrusion. This information is useful for the digital reconstruction of the line-by-line toolpath for micromechanical modeling. Manual reconstruction of the line-by-line toolpath can be a very time-consuming process. However, today’s computation power allows for the potential to do the same in an automated and fast manner.Previous theories provide the framework for Finite Element Analysis (FEA) but fail to account for micromechanical representation and geometrical intricacies&nbsp;by essentially treating the 3D printed part as a composite of box-like structures with varying properties. On the other hand, analytical models are based on mathematical assumptions and often require a case-by-case representation in Computer-Aided Design (CAD) software using simple equations; but do not apply as a universal representation and often require manual intervention. Since both FEA and analytical models are based on multiple assumptions, the results need to be validated using the Ultimate Tensile Test or other mechanical testing methods of the real-world 3D printed model for accurate results.These assumptions create disparities between the values of various properties as obtained by mechanical testing and Finite Element Methods in software. Moreover, predicting the behavior of 3D printed parts and gaining control on the process to influence or optimize these behaviors are still in their infancy.&nbsp;This paper attempts to minimize this gap by using an Additive Reconstruction tool with G-code as input information to reconstruct the solid line-by-line and layer-by-layer toolpath and obtain a digital equivalent of the 3D-printed part in CAD software. We wrote an algorithm in Python to read line-by-line of the G-Code of the 3D-printed toolpath file. We extracted the X, Y, Z coordinates along with the extrusion (E) values. We developed an IronPython script in Rhino software to read these coordinates to generate the solid line layer-by-layer CAD model.At every layer, the points were first collected into an array. A polyline that mimics the same toolpath trajectory is interpolated through these points, with a small fillet radius to increase the realism. Pipes of diameter equal to the filament thickness were then swept through these filleted polylines and were scaled down by a factor to make them elliptical. Thus, the various pipes were then combined using the Boolean union operation to obtain a 3D-printed solid, represented accurately in the CAD software and ready for further micromechanical modeling analysis.We will validate the results by having three comparison models: experimental 3D-printed, numerical FEA using a pristine CAD model, and numerical FEA using a reconstructed CAD model. For the experimental test, a 3D-printed tensile specimen will be designed according to the ASTM standard. The FEA Analysis under tensile loads of the generated explicit 3D model with this method is expected to exhibit closer results to the experimental test than a traditional FEA model using a pristine CAD model. The similarity of the layers generated in the model will be validated with the cross-sectional analysis of the 3D Printed samples using Scanning Electron Microscopy (SEM).The study’s significance is to ultimately move one step further in exploring whether 3D-printed model properties can be predicted and controlled post characterization in software.&nbsp;&nbsp;It provides a closed-loop in optimizing the printing direction to adapt the 3D printing process and provide a real-world model with user-desired properties.

Digital Twin Additive Reconstruction Tool for Micromechanical Modeling of 3D-Printed Parts

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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