2026 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2026): Computational Techniques
Program Organizers: David Leigh, University of Texas at Austin
Tuesday 1:30 PM
August 4, 2026
Room: Lavaca
Location: AT&T Center
1:30 PM
An Improved Interlocking Pattern for Desktop Multi-Material Additive Manufacturing: Connor Kitchin1; Seth Pearl1; 1Drury University
Advancements in desktop material extrusion (MEX) printers have led to an increasing support in multi-material fabrication, enabling end-use parts that leverage the distinct properties of multiple polymers. These newly added capabilities have expanded the toolbox for which designers can leverage in the designs they create for additive manufacturing (AM), however, the interfacial bond strength between dissimilar materials remains a critical limiting factor in structural applications. This work evaluates the tensile strength properties of multi-material objects manufactured with desktop AM. The default settings from PrusaSlicer were treated as the control in the experimental tests. The interlocking parameters were then modified by directly editing the G-code beyond the constraints of PrusaSlicer’s native interface. The results of tensile testing these new samples demonstrated significant improvements in the interfacial strength compared to the default settings, validating this new interlocking pattern for high-strength applications.
1:50 PM
Leonardo, Murray, and Hack: An Investigation of Flow Uniformity in Nature-inspired Branching Networks: Yash Mistry1; Aniruddha Talange1; Morgan Nunez1; Zubin Mistry2; Gokul Chandrasekaran1; Chad Westover1; Puneet Achpal3; Dhruv Bhate1; 1Arizona State University; 2Purdue University; 3Lam Research Corp.
Branching networks have received significant interest from mathematicians, biologists and physicists. Yet there is no capability that merges ideas from these domains in the generation of branching forms for fabrication with additive manufacturing for engineering applications. This research aims to address this gap by developing a computational approach that integrates mathematical, physical, and biological laws within the context of fluid flow in branching channels. Three laws proposed for branching in natural system by Leonardo, Murray and Hack are examined. Originating from tree architecture, vascular systems, and river deltas respectively, these three laws represent distinct approaches to specifying the geometric relationships that constrain the design of a branching structure. Computational modeling and additive manufacturing are leveraged to enable simulation-driven and experimental studies of fluid flow in different branching designs. The findings suggest that Murray’s Law optimizes flow resistance, while Leonardo’s Law exhibits a closer approximation to uniform flow distribution.
2:10 PM
Process Parameter Optimization for Overlapped Regions in Hybrid LP-DED: James Wateska1; Carolyn Seepersad1; 1Georgia Institute of Technology
Overlapped regions in hybrid additive manufacturing are challenging to produce with high volumetric accuracy and metallurgical continuity. Corners overbuild where the toolpath doubles onto itself, while lateral beads require careful overlap selection to ensure fusion between adjacent tracks. This study investigates process parameter optimization to enhance volumetric consistency and metallurgical continuity in powder-blown Directed Energy Deposition of 17-4 PH stainless steel. For corners, a space-filling design of experiments samples additive parameters across varying corner angles and layer counts. Volumetric data near the corner peak is used to guide parameter selection for reducing overbuild. For lateral beads, metallurgical continuity between adjacent beads is assessed using dilution and wetting angle. A semi-analytical relationship captures powder catchment efficiency and informs a thermal finite element model for predicting bead overlap behavior. The proposed strategies are validated experimentally for corners and through simulation for lateral beads, demonstrating improved volumetric fidelity and predicted metallurgical continuity.
2:30 PM
Simulation-Aided Iterative Redesign for Laser Powder Bed Fusion Additive Manufacturing of a Complex Ti-6Al-4V Part: Jonathan Reyes Ortiz1; Anthony Rollett1; Sneha Prabha Narra1; 1Carnegie Mellon University
Laser powder bed fusion (LPBF) of geometrically complex parts requires rigorous Design for Additive Manufacturing (DfAM) strategies that preserve part functionality, critical features, and post-build characterization requirements. This work presents the simulation-aided iterative redesign for successful printing of an industry-relevant complex part, referred to as "Challenge Part," in Ti-6Al-4V on an EOS M290 system at Carnegie Mellon University. The part underwent five design iterations ranging from a minimal redesign with added fillets and tree supports to a hybrid strategy combining near self-supporting geometry using internal and external ribs to minimize distortion. Each iteration was evaluated by tracking thermal history, displacement, and von Mises equivalent stress in Autodesk Netfabb 2025, with selected cases cross-checked in the Ansys AM LPBF Thermal-Structural module. The final iteration significantly reduced the initially predicted distortion and stress values, and was successfully printed in approximately five days.
2:50 PM
Lowering the Process-Knowledge Barrier in Additive Manufacturing Education through LLM-Guided Slicing: Syed Ziaul Bin Bashar1; Jesus Dias1; Chaitanya Mahajan1; Satyajayant Misra1; 1New Mexico State University
Integrating Additive Manufacturing (AM) into education allows students to visualize complex concepts through tangible models. While Fused Filament Fabrication (FFF) is the standard in classrooms due to its affordability, the technical complexity of slicing remains a significant barrier for many users. To address this challenge, we introduce AutoSlice, a modular framework that streamlines the transition from design to physical product. Unlike existing applications that primarily rely on Large Language Models (LLMs) for CAD generation, AutoSlice uses an LLM as a core reasoning engine to automate slicer configuration. By synthesizing optimal print parameters from material properties and geometric heuristics, AutoSlice bridges a critical gap in the AM workflow. This innovation empowers educators and students to bypass technical bottlenecks, fostering a profound and accessible learning experience.
3:10 PM
Machine Learning-Based Predictive Modeling for Geometric Distortion in Metal Material Extrusion: Saisruthi Kotagiri1; Andrea Gonzalez Martinez1; Jesus Diaz1; Chaitanya Mahajan1; Venkata Sirimuvva Chirala1; 1New Mexico State University
Additive Manufacturing of metals via Material Extrusion (MEX) enables cost-effective production of complex geometries; however, post-processing steps, debinding and sintering,cause significant non-linear shrinkage and distortion, complicating the expected final-shape. This research introduces a machine learning method to predict the final sintered component from its initial Computer Aided Design (CAD) from laser-scanned meshes. Fabricated benchmark partsare printed, sintered, and scanned into meshes to create paired datasets for an artificial neural network which will be trained to model shrinkage and distortion, allowing accurate prediction of final part shapes. This approach aims to provide compensation during the design phase, enabling designers to adjust CAD models for manufacturing-induced changes. Ultimately, the method seeks to improve dimensional accuracy in metal MEX, reducing trial-and-error while achieving tolerances comparable to traditional manufacturing—all within a concise, data-driven workflow.
3:30 PM
Defect-Oriented Risk and Monitoring Framework for Quality Assurance in Laser Powder Bed Fusion: Ashish Gahlot1; 1Airbus Helicopters
Quality assurance in Laser Powder Bed Fusion (LPBF) remains challenging due to the interdependent nature of defect formation across the additive manufacturing process chain. Critical defects such as lack of fusion, porosity, cracking, embrittlement, distortion, and mechanical property drift arise from interactions among powder characteristics, process stability, thermal history, chamber conditions, and post-processing operations. While conventional Process Failure Mode and Effects Analysis (PFMEA) provides structured process risk evaluation, its process-step-oriented structure limits systematic tracing of defect origins and propagation pathways.This work proposes a defect-oriented risk and monitoring framework for LPBF quality assurance that combines defect-driven backward mapping and process-driven forward mapping. The framework links process conditions, defect formation mechanisms, metallurgical consequences, monitoring opportunities, and final quality effects within an interconnected defect–process structure. The proposed approach supports defect-origin identification, monitoring prioritization, and risk-informed quality assurance strategies for demanding LPBF production environments.