Interactive 3D visualization of surface roughness predictions in additive manufacturing: A data-driven framework
Department of Data Informatics, Graduate School of Informatics, Middle East Technical University（数据信息系，信息科学研究生院，中东部技术大学）；Department of Modeling and Simulation, Graduate School of Informatics, Middle East Technical University（建模与仿真系，信息科学研究生院，中东部技术大学）；Department of Mechanical Engineering, Middle East Technical University（机械工程系，中东部技术大学）；Welding Technology and NDT Center, Middle East Technical University（焊接技术与无损检测中心，中东部技术大学）
Surface roughness in Material Extrusion Additive Manufacturing varies across a part and is difficult to anticipate during process planning because it depends on both printing parameters and local surface inclination, which governs the staircase effect. A data-driven framework is presented to predict the arithmetic mean roughness (Ra) prior to fabrication using process parameters and surface angle. A structured experimental dataset was created using a three-level Box-Behnken design: 87 specimens were printed, each with multiple planar faces spanning different inclination angles, yielding 1566 Ra measurements acquired with a contact profilometer. A multilayer perceptron regressor was trained to capture nonlinear relationships between manufacturing conditions, inclination, and Ra. To mitigate limited experimental data, a conditional generative adversarial network was used to generate additional condition-specific tabular samples, thereby improving predictive performance. Model performance was assessed on a hold-out test set. A web-based decision-support interface was also developed to enable interactive process planning by loading a 3D model, specifying printing parameters, and adjusting the part's orientation. The system computes face-wise inclination from the model geometry and visualizes predicted Ra as an interactive colormap over the surface, enabling rapid identification of regions prone to high roughness and immediate comparison of parameter and orientation choices.
Toward data-driven research: preliminary study to predict surface roughness in material extrusion using previously published data with Machine Learning
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing
Fraunhofer Institute for Machine Tools and Forming Technology (IWU)（弗劳恩霍夫机床与成型技术研究所（IWU））；Fraunhofer Institute for Industrial Mathematics (ITWM)（弗劳恩霍夫工业数学研究所（ITWM））；Institute of Mechatronic Engineering Technische Universität Dresden（德累斯顿工业大学机电工程研究所）
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned
Seagate Technology（希捷科技）；Case Western Reserve University（凯斯西储大学）；University of Chicago（芝加哥大学）；University of Cincinnati（辛辛那提大学）；Florida Atlantic University（佛罗里达大西洋大学）；Duke Kunshan University（昆山杜克大学）
Data-driven discovery of roughness descriptors for surface characterization and intimate contact modeling of unidirectional composite tapes
Arts et Metiers Institute of Technology（艺术与工艺技术学院）；Univ Toulouse, IMT Mines Albi, INSA Toulouse, ISAE-SUPAERO, CNRS, ICA（图卢兹大学，IMT阿尔比矿校，图卢兹INSA，ISAE-SUPAERO，CNRS，ICA）
Machine learning enables roughness-driven inverse design of milling processes
Centre Internacional de Mètodes Numèrics a l’Enginyeria (CIMNE), Campus Norte UPC, 08034 Barcelona, Spain（国际数值工程方法中心（CIMNE），UPC北校区，巴塞罗那，西班牙）；Universitat Politècnica de Catalunya (UPC), Campus Norte UPC, 08034 Barcelona, Spain（加泰罗尼亚理工大学（UPC），UPC北校区，巴塞罗那，西班牙）；Mechatronics and Modelling Applied on Technology of Materials (MECAMAT) group. Universitat de Vic-Universitat Central de Catalunya (UVic-UCC), C. de la Laura 13, 08500 Vic, Spain（机械与材料技术应用机械建模组（MECAMAT），维克-加泰罗尼亚中央大学（UVic-UCC），Laura街13号，维克，西班牙）
