Flat-panel composite curing distortion dataset: raw simulation outputs
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This record presents the raw simulation outputs of a synthetic dataset of process-induced distortion in flat laminated composite panels generated within the DIDEAROT project (DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures). The dataset has been produced using the Process Induced Distortion (PID) synthetic data generator, a high-fidelity numerical workflow based on Alya’s simulation framework. Each case corresponds to a finite-element simulation of a flat rectangular composite laminate subjected to a cure cycle, with the objective of capturing the distortion that emerges after tool release due to residual stresses developed during manufacturing. The simulations account for the coupled effects of thermoelastic residual stresses and chemical shrinkage, providing a physically grounded description of post-cure out-of-plane deformation. This record preserves the original case-level simulation outputs and associated artefacts in their raw form, enabling full traceability, detailed inspection, reprocessing, and reconstruction of derived quantities through the original post-processing workflow. The dataset is released through two complementary Zenodo records. The present record contains the raw simulation outputs, while the companion record provides the curated tabular data and representative deformed surfaces, offering the user-facing, analysis-ready version of the dataset for exploratory analysis, benchmarking, surrogate modelling, and machine learning workflows. This dataset is one of the synthetic data assets developed within the DIDEAROT framework to enable data-driven methodologies for robust composite design and certification. Additional details on the simulation framework, modelling assumptions, and workflow implementation can be found in Deliverable D3.7 of DIDEAROT and in the work entitled “A High-Fidelity HPC Workflow for Predicting Process-Induced Distortions in Composites Using Surrogate Models” [1]. [1] M. Teixidor-Vilarrasa, A. Quintanas-Corominas, A. Ortega, I. Zárate, E. Marquinez, I. Otero and G. Guillamet, A High-Fidelity HPC Workflow for Predicting Process-Induced Distortions in Composites Using Surrogate Models, Materiales Compuestos (Online first). URL https://www.scipedia.com/public/Vilarrasa_et_al_2025a This project has received funding from the European Union’s Horizon Europe Framework Programme under grant agreement No. 101056682 for the project “DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures (DIDEAROT)”. The contents of this publication are the sole responsibility of the participants and do not necessarily reflect the opinion of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
本数据集记录了DIDEAROT项目（Digital Design strategies to certify and manufacture Robust composite sTructures，即用于认证与制造稳健复合材料结构的数字化设计策略）所生成的平面层压复合材料板工艺诱导变形合成数据集的原始仿真输出。该数据集通过工艺诱导变形（Process Induced Distortion, PID）合成数据生成器构建，这是一套基于Alya仿真框架（Alya’s simulation framework）的高保真数值仿真工作流。每个仿真案例对应一次对受固化循环作用的扁平矩形复合层压板的有限元仿真，旨在捕捉脱模后因制造过程中产生的残余应力所引发的变形。 本次仿真考量了热弹性残余应力与化学收缩的耦合效应，为固化后平面外变形提供了基于物理原理的描述。本数据集完整保留了原始案例级仿真输出及相关伪影的原始格式，可实现全流程可追溯性、精细化检视、重处理，并可通过原始后处理工作流重构衍生量化指标。 本数据集通过两条互补的Zenodo记录发布。本记录仅包含原始仿真输出，而配套记录则提供经过整理的表格数据与代表性变形曲面，为用户提供可直接用于探索性分析、基准测试、代理建模及机器学习工作流的数据集版本。本数据集是DIDEAROT框架下开发的合成数据资产之一，旨在支撑面向稳健复合材料设计与认证的数据驱动方法论。有关仿真框架、建模假设及工作流实现的更多细节，可参阅DIDEAROT项目的交付件D3.7，以及题为《使用代理模型预测复合材料工艺诱导变形的高保真高性能计算（High Performance Computing, HPC）工作流》[1]的研究成果。 [1] M. Teixidor-Vilarrasa、A. Quintanas-Corominas、A. Ortega、I. Zárate、E. Marquinez、I. Otero与G. Guillamet，《使用代理模型预测复合材料工艺诱导变形的高保真高性能计算工作流》，*Materiales Compuestos*（在线优先出版），网址：https://www.scipedia.com/public/Vilarrasa_et_al_2025a 本项目获欧洲联盟地平线欧洲框架计划资助，资助协议编号为101056682，项目名称为“DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures (DIDEAROT)”。本出版物的内容仅由参与方负责，未必反映欧洲联盟的观点。欧洲联盟及资助机构均不对其内容承担责任。
