Conference Agenda
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Agenda Overview |
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D336: DATA-DRIVEN AND MULTI-MATERIAL DESIGN IN ADVANCED DFAM
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Design for additive manufacturing of multi-material microreactors: a simulative study on specific surface area and thermal management Institute of Product Development (IPeG), Leibniz University Hannover, Germany This study investigates the potential of multi-material additive manufacturing (MMAM) designs for improving microchannel reactors for ammonia decomposition. Using CFD simulations, designs made from stainless steel 316L and CuCr1Zr to enhance specific surface area and temperature distribution were analyzed. Results show that MMAM designs can reduce temperature gradients by up to 26.81 K and boost fuel processor efficiency by up to 3.2 percentage points compared to mono-material designs. These findings underscore the potential of MMAM in optimizing the reactor efficiency. Designing for compliance at the microscale: DfAM lessons from a 2PP-printed bellows structure for sensing and actuation Norwegian University of Science and Technology, Norway AM enables the design of compliant mechanisms that encode functions directly into geometry. Existing DfAM frameworks rarely address microscale AM, such as two-photon polymerisation (2PP). We present the design process of an airtight, monolithic bellows structure in rigid 2PP resin that serves both as a sensor and an actuator. Through co-evolution of problem and solution, we identify 2PP-specific design considerations and opportunities, including fabrication uncertainties, cross-scale iteration, and design for post-processing, contributing to a case-based DfAM framework for microscale AM. Machine-learning-based one-to-many inverse design of multi-material lattices Imperial College London, United Kingdom This work presents an ML-based inverse design framework for multi-material lattices with curved struts, targeting mechanical and thermal performance. Using cubic-spline parameterization and discrete material assignment, the design space expands beyond conventional lattices. A workflow combining a material classifier, property predictor, and inverse generators addresses one-to-many mapping, enabling probabilistic sampling and diverse designs. The approach supports multi-objective trade-offs and lays the foundation for multi-scale optimization of functionally graded metamaterials. Design guidelines for electrical conductors and Joule-heating structures fabricated additively by material extrusion 1Institute for Engineering Design, Technische Universität Braunschweig, Germany; 2Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taiwan Additive Manufacturing (AM) enables the local adjustment of material properties using multi-material strategies, especially with Material Extrusion (MEX). Electrically conductive structures like conductors, Joule heating structures, and their transitions can be realised with conductive polymer composites (CPC). However, specific Design for Additive Manufacturing (DfAM) guidelines for the afore mentioned structures are still missing. This work uses experimental data by thermography and the measurement of resistivity to derive twelve design rules. The rules are applied to an application example. | ||
