Validierungs-Navigator – ein Referenzprozess für Validierungsstudien zur Wirkung von Konstruktionsmethoden = Validation Navigator –a referenceprocess for validation studieson effects of design methods

Konstruktionsmethoden sind ein Kernergebnis der Konstruktionsforschung und sollten vor ihrer Einführung und Verbreitung in Unternehmen validiert werden. In der Kon-struktionsforschung fehlt bisher ein Standard für Forschungsmethoden als Orientie-rung für den Aufbau von Validierungsstudien sowie für die Datenerhebung und -aus-wertung. Für die Etablierung eines solchen Standards ist eine Referenz notwendig, um vergleichbare Studien zu ermöglichen. Auf die vergleichbare Umsetzung einzelner Validierungsstudien im Detail und die Operationalisierung von Erfolgskriterien gehen bestehende Ansätze nicht ein, weshalb sie nicht als Referenz für den Aufbau einzelner Studien geeignet sind. Wie Validierungsstudien in der aktuellen Forschungspraxis durchgeführt werden und wie eine Vergleichbarkeit zwischen diesen Studien herge-stellt werden kann, ist zudem nicht explizit bekannt. Zur Untersuchung der aktuellen Forschungspraxis wurde eine systematische Litera-turstudie durchgeführt. Durch eine Kategorisierung aktueller Validierungsstudien in Bezug auf Schritte in der Validierung und die Art des verwendeten Studiendesigns wurde sichtbar, dass Studien in verschiedenen Validierungsschritten bisher nur wenig vergleichbar sind. Eine Analyse der kategorisierten Studien zeigte Herausforderungen in der Vergleichbarkeit der Konstruktionsmethoden selbst sowie der Umsetzung der Validierungsstudien. Strategien für eine höhere Vergleichbarkeit in aktuellen Validie-rungsstudien umfassen die Anknüpfung an bestehende Theorie, eine einheitliche Ziel-setzung mit zugehörigen Variablen für Klassen von Konstruktionsmethoden sowie die Nutzung etablierter Modelle und Werkzeuge für den Aufbau von Validierungsstudien. Die Erkenntnisse aus der Literaturstudie wurden genutzt, um den Referenzprozess Validierungs-Navigator zu entwickeln, der eine Unterstützung für Forschende für den Aufbau vergleichbarer Validierungsstudien zur Wirkung von Konstruktionsmethoden bereitstellt. Im Referenzprozess werden Elemente aus bestehenden Ansätzen mit den in der Literaturstudie identifizierten Strategien kombiniert, um die Qualität und Ver-gleichbarkeit entstehender Validierungsstudien positiv zu beeinflussen. Der Validie-rungs-Navigator beschreibt zwei Studien, um zunächst ein qualitatives Verständnis aufzubauen, bevor eine quantitative Untersuchung stattfindet, und berücksichtigt die aufeinander aufbauenden Erfolgskriterien Anwendbarkeit und Wirksamkeit. Der Validierungs-Navigator wurde durch eine exemplarische Anwendung für den Auf-bau von Validierungsstudien zur Wirkung einer Konstruktionsmethode evaluiert. Es konnten Beiträge zur Studienqualität geleistet werden, indem das Studiendesign so-wie beinhaltete Erhebungsmethoden und Stimuli kontinuierlich weiterentwickelt wur-den. Auf die Vergleichbarkeit der zur Validierung eingesetzten Variablen, der Zielset-zung und Vorgehensweise der untersuchten Konstruktionsmethode sowie der Operationalisierung der Erfolgskriterien konnte ein positiver Einfluss erzielt werden

Investigating qualitative modelling in design – experimental method validation at the Contact and Channel Approach

Qualitative modelling in embodiment design aims to support the gain of system understanding. However, it remains unclear whether using a qualitative modelling method impacts system understanding compared to intuitive approaches. In this contribution, an experimental study is conducted to investigate the impact of the modelling method based on the Contact and Channel Approach on system understanding. The study is set up with 35 participants, video-based modelling training and two technical systems. The tasks are analyses of causes for system behaviour on two different detail levels of system understanding. The control group solves the task with intuitive approaches, the test group uses the modelling method. On the system level, general relations of embodiment and behaviour are investigated. On the detail level, function-critical system areas, as well as function-relevant system states, are investigated. The modelling method increases understanding of technical systems at the system level compared to intuitive approaches. In the detail level, no statement about statistically significant differences could be derived. Challenges in identifying critical areas and relevant system states are uncovered, which provide insights for improving the modelling method. With this study design, the modelling process is now observable. It can provide a baseline for investigations of similar modelling methods

Enhancing design method training with insights from educational research–improving and evaluating a training course for a qualitative modelling method

Abstract This study presents an approach for identification and elimination of challenges in modelling in embodiment design. These challenges can be caused either by the modelling method or the corresponding training course. To investigate the efficacy of a modelling method, first challenges of the corresponding training course need to be addressed. The study is conducted at a training course of the modelling method of the Contact and Channel Approach. A situation analysis of the training course is conducted in three application with 45 participants. Based on the findings, the training course is improved through application of insights from educational research that correspond to the identified challenges. A concluding evaluation takes place with 20 participants. The improvement of the training course takes place based on identification of challenges in the four areas of didactic elements, content structure, visualization and practical modelling in evaluations. Modularization is needed for purposeful training of different target groups. An issue regarding the practical modelling indicates a clearer view on the efficacy of the modelling method. Article highlights Identification of challenges in a training course for qualitative modelling in embodiment design through free text evaluation in three applications. Clustering of the evaluation results enabled identification of suitable findings from educational research to eliminate challenges in the training course. Conflicts of objectives regarding content and time can be addressed by modularization, however, this increases the effort needed for investigations

The success story of graphite as a lithium-ion anode material – fundamentals, remaining challenges, and recent developments including silicon (oxide) composites

Lithium-ion batteries are nowadays playing a pivotal role in our everyday life thanks to their excellent rechargeability, suitable power density, and outstanding energy density. A key component that has paved the way for this success story in the past almost 30 years is graphite, which has served as a lithium-ion host structure for the negative electrode. And despite extensive research efforts to find suitable alternatives with enhanced power and/or energy density, while maintaining the excellent cycling stability, graphite is still used in the great majority of presently available commercial lithium-ion batteries. A comprehensive review article focusing on graphite as lithium-ion intercalation host, however, appeared to be missing so far. Thus, herein, we provide an overview on the relevant fundamental aspects for the de-/lithiation mechanism, the already overcome and remaining challenges (including, for instance, the potential fast charging and the recycling), as well as recent progress in the field such as the trade-off between relatively cheaper natural graphite and comparably purer synthetic graphite and the introduction of relevant amounts of silicon (oxide) to boost the energy and power density. The latter, in fact, comes with its own challenges and the different approaches to overcome these in graphite/silicon (oxide) composites are discussed herein as well

Laplace-Beltrami Refined Shape Regression Applied to Neck Reconstruction for Craniosynostosis Patients Combining posterior shape models with a Laplace-Beltrami based approach for shape reconstruction

This contribution is part of a project concerning the creation of an artificial dataset comprising 3D head scans of craniosynostosis patients for a deep-learning-basedclassification. To conform to real data, both head and neck are required in the 3D scans. However, during patient recording, the neck is often covered by medical staff. Simply pasting an arbitrary neck leaves large gaps in the 3D mesh. We therefore use a publicly available statistical shape model (SSM) for neck reconstruction. However, mostSSMs of the head are constructed using healthy subjects, so the full head reconstruction loses the craniosynostosis-specific head shape. We propose a method to recover the neck while keeping the pathological head shape intact. We propose a Laplace-Beltrami-based refinement step to deform the posterior mean shape of the full head model towards the pathological head. The artificial neck is created using the publicly available Liverpool-York-Model. We apply our method to construct artificial necks for head scans of 50 scaphocephaly patients. Our method reduces mean vertex correspondence error by approximately 1.3 mm compared to the ordinary posterior mean shape, preserves the pathological head shape, and creates a continuous transition between neck and head. The presented method showed good results for reconstructing a plausible neck to craniosynostosis patients. Easily generalized it might also be applicable to other pathological shapes

The Use of Artificial Intelligence for the Classification of Craniofacial Deformities

Positional cranial deformities are a common finding in toddlers, yet differentiation from craniosynostosis can be challenging. The aim of this study was to train convolutional neural networks (CNNs) to classify craniofacial deformities based on 2D images generated using photogrammetry as a radiation-free imaging technique. A total of 487 patients with photogrammetry scans were included in this retrospective cohort study: children with craniosynostosis (n = 227), positional deformities (n = 206), and healthy children (n = 54). Three two-dimensional images were extracted from each photogrammetry scan. The datasets were divided into training, validation, and test sets. During the training, fine-tuned ResNet-152s were utilized. The performance was quantified using tenfold cross-validation. For the detection of craniosynostosis, sensitivity was at 0.94 with a specificity of 0.85. Regarding the differentiation of the five existing classes (trigonocephaly, scaphocephaly, positional plagiocephaly left, positional plagiocephaly right, and healthy), sensitivity ranged from 0.45 (positional plagiocephaly left) to 0.95 (scaphocephaly) and specificity ranged from 0.87 (positional plagiocephaly right) to 0.97 (scaphocephaly). We present a CNN-based approach to classify craniofacial deformities on two-dimensional images with promising results. A larger dataset would be required to identify rarer forms of craniosynostosis as well. The chosen 2D approach enables future applications for digital cameras or smartphones

A Radiation-Free Classification Pipeline for Craniosynostosis Using Statistical Shape Modeling

Background: Craniosynostosis is a condition caused by the premature fusion of skull sutures, leading to irregular growth patterns of the head. Three-dimensional photogrammetry is a radiation-free alternative to the diagnosis using computed tomography. While statistical shape models have been proposed to quantify head shape, no shape-model-based classification approach has been presented yet. Methods: We present a classification pipeline that enables an automated diagnosis of three types of craniosynostosis. The pipeline is based on a statistical shape model built from photogrammetric surface scans. We made the model and pathology-specific submodels publicly available, making it the first publicly available craniosynostosis-related head model, as well as the first focusing on infants younger than 1.5 years. To the best of our knowledge, we performed the largest classification study for craniosynostosis to date. Results: Our classification approach yields an accuracy of 97.8 %, comparable to other state-of-the-art methods using both computed tomography scans and stereophotogrammetry. Regarding the statistical shape model, we demonstrate that our model performs similar to other statistical shape models of the human head. Conclusion: We present a state-of-the-art shape-model-based classification approach for a radiation-free diagnosis of craniosynostosis. Our publicly available shape model enables the assessment of craniosynostosis on realistic and synthetic data

Generative-Adversarial-Network-Based Data Augmentation for the Classification of Craniosynostosis

Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. The simulated scarcity scenario of 10% training data may have limited the model’s ability to learn the underlying data distribution