https://fordatis.fraunhofer.de/handle/fordatis/12 2026-05-07T15:23:35Z 2026-05-07T15:23:35Z Klöpfer, Kevin Michelberger, Claudia Huber, Marco https://fordatis.fraunhofer.de/handle/fordatis/479 2026-02-13T04:13:34Z 2026-02-01T00:00:00Z

Titel: Supplementary Material: Interview Online Questionnaire - Why cost estimation matters for design decisions in the early phases of product development and what practitioners expect from artificial intelligence Datenautorinnen und Datenautoren: Klöpfer, Kevin; Michelberger, Claudia; Huber, Marco Zusammenfassung: The interview guide and online questionnaire were used in an exploratory sequential mixed-methods design. The aim of the study was to investigate the cost estimation of products in the early phases of product development in mechanical engineering, as well as practitioners’ expectations of cost estimation assisted by artificial intelligence (AI).

2026-02-01T00:00:00Z Dobosz, Thomas Lubitz, Sonja https://fordatis.fraunhofer.de/handle/fordatis/470 2025-12-20T02:30:56Z 2026-01-01T00:00:00Z

Titel: Dataset for 'Reducing Sensor Configuration for Data-Driven Shoulder Load Estimation for Exoskeleton Control' Datenautorinnen und Datenautoren: Dobosz, Thomas; Lubitz, Sonja Zusammenfassung: This paper investigates the role of various sensors in estimating shoulder loads during manual manipulation tasks in the context of exoskeleton control. The sensors examined include textile-integrated electromyography (EMG) sensors forthe trapezius, deltoids, biceps, and forearm muscles; inertial measurement units (IMUs) on key body segments such as the pelvis, shoulder, upper arm, and forearm; and pressure-sensing insoles. The objective is to reduce the sensor configuration for predicting the internal torque exerted on the shoulder in the sagittal plane. To achieve this, a study involving nine subjects manipulating dumbbells in the sagittal plane was conducted. The protocol encompasses four static positions as well as isolated elbow and shoulder flexions. Further, four distinct machine learning model architectures were trained, systematically omitting one sensor at a time. The significance of each sensor was evaluated by assessing the impact of its omission on the predictive correlation using cross-validated R2 scores. Consequently, a top-five sensor configuration was identified and compared against configurations based solely on domain knowledge and the full sensor array. The configuration proposed in this study achieved a correlation of R2 = 0.83 in predicting shoulder loads, slightly surpassing the performance of the full sensor setup (R2 = 0.82) and outperforming the domain knowledge-based (DKB) setup (R2 = 0.63). All metrics are determined in a leave-one-subject-out cross-validation (loso-cv) training strategy.

2026-01-01T00:00:00Z Jordan, Florian https://fordatis.fraunhofer.de/handle/fordatis/458 2025-10-16T01:30:22Z 2025-10-01T00:00:00Z

Titel: ILIS - Iteratively Labeled Instance Segmentation Datenautorinnen und Datenautoren: Jordan, Florian Zusammenfassung: This dataset provides real and synthetic images with instance annotations for each objects. The images per scene are created in an iterative context, for the real images and a part of the synthetic images the individual iteration images are provided.

2025-10-01T00:00:00Z Schade, René https://fordatis.fraunhofer.de/handle/fordatis/422.2 2025-02-25T03:49:33Z 2024-01-01T00:00:00Z

Titel: Simulation Decision Matrix_Analytical Hierarchy Process_data_expert survey Datenautorinnen und Datenautoren: Schade, René Zusammenfassung: Raw data and analysis results of the six experts of the analytical hierarchy process (AHP) expert survey. The expert survey was conducted for prioritising sub-criteria of the Simulation Decision Matrix (SDM). The SDM is primarily used for prioritising quality-critical product parameters of hydrogen technologies for simulation applications in their production process.

2024-01-01T00:00:00Z