2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024): Slicing and Path Generation
Program Organizers: Joseph Beaman, University of Texas at Austin
Tuesday 8:00 AM
August 13, 2024
Room: 412
Location: Hilton Austin
Session Chair: Hongtao Song, Georgia Insitute of Technology
8:00 AM
Fundamental Path Optimization Strategies for Extrusion-Based Additive Manufacturing: Alex Roschli1; Liam White1; Michael Borish1; Cameron Adkins1; Ashley Gannon1; Adam Stevens1; Thomas Feldhausen1; Brian Post1; Eric MacDonald2; 1ORNL; 2UTEP
Extrusion-based additive manufacturing processes begin with a software program, called a slicer, that generates layer geometry and fits toolpaths to each layer to define where material is to be extruded or deposited. Before the toolpaths are output as g-code for the additive manufacturing system to execute, the toolpaths should be optimized. Many complex optimization approaches using graph theory, Chinese postman problem, and other complex mathematical models exist, but these approaches are rarely used in daily printing operations and are not available through common slicing programs such as Cura and PrusaSlicer. Instead, path planning and optimization typically revolves around simpler, fully automated approaches such as inside out and next closest. This paper will explore the fundamental optimization strategies for toolpath planning and document a new implementation, available via open-source slicing software, that allows for greater control of the path planning process.
8:20 AM
GRATER: The Graph Theory Based Slicer: Logan Hutton1; Joseph Bartolai1; 1Pennsylvania State University
A novel graph-theory driven toolpath generation algorithm for polymer Material Extrusion (MEX) additive manufacturing (AM) that considers process optimization when writing toolpaths is presented. Contemporary toolpath design algorithms are written to prioritize robustness (always reaching a valid solution) and computational cost (time to solution). This novel toolpath generation algorithm, known as GRATER (GRAph Theory based slicER), is a work-in-progress tool that uses graph theory to guarantee a single continuous toolpath (a toolpath with no travel moves) for every closed contour. GRATER reduced travel moves, which create defects within MEX parts – particularly those built using pellet-fed MEX, by up to 95% when compared to contemporary slicing softwares. Additionally, GRATER is shown to reduce travel distance by a factor of 3 and build time by 25%.
8:40 AM
Towards Freeform Additive Manufacturing: Recent Improvements Made on ORNL Large Scale Metal Slicer to Meet the Challenges brought by Growing Complexity of Parts in Wire-arc Additive Manufacturing: Canhai Lai1; Alex Waters1; Chris Masuo1; Andrzej Nycz1; William Carter1; Michael Sebok1; Joshua Vaughan1; 1Oak Ridge National Laboratory
ORNL Slicer is a slicing engine and graphical user interface (GUI) developed for large scale additive manufacturing at Manufacturing Demonstration Facility (MDF) of Oak Ridge National Laboratory (ORNL). An independent branch has been developed for the wire-arc additive manufacturing (WAAM) robotic- based technology. As the additive manufacturing technology advances and adapts to the geometrical complexity and customized design, many features have been added in the Slicer to address the complexities so that the 3D printing is optimized in characterizing geometric features with minimum mechanical and thermal stress. In this presentation, a few recently developed features will be introduced: flange cladding, multibody freeform, and multi-material profile, all building blocks of freeform transformation of building layers. This presentation will bring a few examples of wire-arc parts to illustrate how the newly developed features help to generate the desired toolpaths to meet the goals of freeform additive manufacturing.
9:00 AM
Modeling of Laser Stirring Scan Strategy in LPBF for High-Throughput Applications: Elizabeth Chang-Davidson1; Todd Spurgeon2; Ajay Krishnan2; Sinan Muftu1; 1Northeastern University; 2EWI
In laser powder bed fusion (LPBF), the most commonly used scan strategies rely on raster- and contour-based patterns, over a whole part or subdivisions of the part. In this work, we investigated a unique circular scanning pattern, termed “laser stirring,” for the purpose of producing significantly larger than usual melt pools and thus increasing build rates. Experimental data was used to calibrate a semi-analytical model, due to the need for fast-running simulations in systematically exploring the high-dimensional process space of this strategy. Based on the experimental data, balling, keyholing, and lack of fusion (non-continuous melt pools) were identified as the flaw regions of interest when process mapping this scan strategy, and semi-analytical modeling was used to predict flaw regions across process space. This laser stirring scan strategy shows promise for going beyond the current boundaries of processing space in LPBF and opening up more potential casting-replacement industry applications of AM.
9:20 AM
A Print-planning Technique Using Edge-weights to Align Toolpaths to Vector Fields: Joseph McKee1; Ian Rybak1; Roger Gonzalez1; Joshua Green1; 1University of Texas at El Paso
Print-planning software can solve many challenges that limit the functionality of additively manufactured parts. Custom slicing software was developed to optimize toolpath alignment with input vector fields. The software converts slices of 3D models into graphs with edge-weights. Print move edge-weights were calculated using the difference in orientation between edges and local vectors while travel moves were assigned edge-weights exceeding the largest print move weight. A Hamiltonian path solver generated toolpaths that align well with the input vector fields by choosing paths that minimized the total weight of selected edges. As a demonstration, a part was printed with fused filament fabrication having extrusions aligned to brush strokes from reference artwork which included custom color assignment realized through use of an active-mixing hotend. Although conventional infill patterns can align well with simple vector fields, this technique provides automated print patterns which can follow both simple and complex vector fields.
9:40 AM Break
10:00 AM
Process Planning Toolbox for Cold Spray Additive Manufacturing Iterative Build Plan Optimization: Elizabeth Chang-Davidson1; Mann Patel1; Akshay Vaidya1; Ozan Ozdemir1; Sinan Muftu1; 1Northeastern University
Cold spray additive manufacturing (CSAM) is an emerging technology within AM, with unique advantages due to the low temperatures compared to fusion-based AM process, as well as high build rates. However, special challenges exist, especially in producing sharp corners and edges, as well as in controlling the thickness of the deposit layer by layer. To successfully build arbitrary 3D shapes using CSAM, toolpath planning is needed to ensure the desired part is fully within the envelope of the final deposit. In this work, a toolbox for pre-build planning is developed, from parameter selection to part slicing to layer-wise toolpath control, including prediction of the final as-built geometry. Toolbox performance is validated through experimental deposition and user feedback. The ability to iterate the build plan before deposition to optimize for key process outcomes saves time and money, and is a valuable step towards qualification of CSAM for applications in industry.
10:20 AM
An Interpolative Slicing Algorithm for Continuously Graded Stiffness in Viscous Thread Printed Foams: Masa Nakura-Fan1; Vivek Sarkar1; Daniel Revier1; Brett Emery2; Jeffrey Lipton2; 1University of Washington; 2Northeastern University
Foams, essential for applications from car seats to thermal insulation, are limited by traditional manufacturing techniques that struggle to produce graded stiffness, a key feature for enhanced functionality. Here, we introduce a novel slicing algorithm for producing heterogeneous foams through viscous thread printing (VTP). Our slicer generates a single, global toolpath for the entire foam volume while modulating the viscous thread's self-interactions along this path to program stiffness. The slicer integrates multiple meshes into a unified print space and interpolates the print speed and height based on specified mesh parameters to program the desired stiffness variations. Using both qualitative samples and quantitative compression tests, we demonstrate that our slicer can (1) generate foam stiffnesses spanning an order of magnitude, (2) achieve millimeter precision in stiffness control, and (3) continuously vary stiffness between regions of constant stiffness using arbitrary functional forms.
10:40 AM
Strategizing Scan Technique Selection for Component Remanufacturing Using 3D Printing: A combined Reverse Engineering and SWARA based Multi Criteria Decision Making Framework: Binoy Debnath1; Zahra Pourfarash1; Shivakumar Raman1; Hamidreza Samadi1; 1University of Oklahoma
Remanufacturing mechanical components via reverse engineering and 3D printing holds promising prospective for sustainable manufacturing by prolonging part lifecycles. However, choosing the right scanning method for reverse engineering is pivotal for ensuring remanufacturing accuracy, efficiency, and cost-effectiveness. This study's framework aims to aid decision-making on scanning method selection, considering criteria like manufacturing time, cost, and product quality. Initially, both contact-based (Coordinate Measuring Machine, CMM) and non-contact-based (Laser scanning) methods are employed to capture the component's geometry for CAD model development. Fused Deposition Modeling (FDM) 3D printing approach is then used to reproduce the part based on CAD models from each scanning method. Then, Step-wise Weight Assessment Ratio Analysis (SWARA) is applied to determine the best scanning technique the relative importance of criteria, allowing stakeholders to prioritize factors based on their needs. This structured approach enables strategic selection of the most suitable scanning technique for 3D printing mechanical component remanufacturing.
11:00 AM
Functionally Graded Toolpaths: Dynamic Process Parameter Control with OpenVCAD: Charles Wade1; Robert MacCurdy1; 1University of Colorado Boulder
OpenVCAD is an open-source volumetric design compiler suited for multi-material design workflows including functionally graded meta-materials and lattice structures. In this presentation, we extend its capabilities to support tool-path-based 3D printers, enabling unprecedented control over functionally graded toolpaths. By leveraging dynamic adjustments in process parameters like feed rate, temperature, and mixture ratios, OpenVCAD allows for precise material grading along tool paths. We demonstrate the efficacy of OpenVCAD with case studies involving mixing extruders, temperature-responsive materials, and variable stiffness foams. Additionally, we will detail how OpenVCAD can directly interface with finite elements and map multi-material gradients based on simulation results. We aim to demonstrate the versatility of OpenVCAD across a wide variety of printing practices. Our work showcases how OpenVCAD empowers users to design and fabricate complex multi-material objects, unlocking new possibilities in multi-material additive manufacturing.
11:20 AM
Reinforcement Learning for Energy-Efficient Toolpath Generation in Additive Manufacturing: George Duke1; David Somade1; Niechen Chen1; 1Northern Illinois University
Toolpath design can significantly impact this Additive Manufacturing process’s efficiency. Traditional toolpath optimization methods frequently depend on empirical methods, which may not adequately account for the complex dynamics of the printing process. This study introduces a novel synergy of Reinforcement Learning (RL) algorithms to optimize toolpath design, specifically aiming to reduce energy consumption. In this work, a custom-built environment is developed to simulate the toolpath planning scenario as a discrete grid space where an agent, the printing nozzle, learns to navigate optimally. Utilizing Proximal Policy Optimization (PPO), a state-of-the-art RL algorithm, the reinforcement learning agent dynamically interacts with the simulated environment, learning to navigate complex geometries while adhering to energy constraints implemented using a custom energy model. Ultimately, the energy efficiency of the toolpath generated would be analyzed by comparing its energy consumption to that of traditional toolpath generation algorithms.