Work

Thermal simulations in additive manufacturing using machine learning

Year: 2025

Type: article

Abstract: Thermal simulations are critical in additive manufacturing to ensure product quality, structural integrity, and optimized designs without the need for repeated real experiments. Conventionally, these simulations utilize numerical-based methods, which can often be time-consuming and resource-intensive, especially as the problem domain expands. To address these limitations, a data-driven framework is proposed. In this work, a methodology to train and validate machine learning-based models for thermal simulation tasks is outlined. For this purpose, an exemplary 2D simulation scenario in laser-based powder bed fusion is considered. Furthermore, the developed framework is utilized in parameter selection with the aim to obtain an energy-efficient process.

Source: Procedia CIRP

DOI: 10.1016/j.procir.2024.12.029

Authors: Shradha Ghansiyal, Svenja Ehmsen, Matthias Klar, Jan C. Aurich

Institution: Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Language: English

Topic: Additive Manufacturing Materials and Processes

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Deutsche Forschungsgemeinschaft