A recommendation system for CAD assembly modeling based on graph neural networks

In computer-aided design (CAD), software tools support design engineers during the modeling of assemblies, i.e., products that consist of multiple components. Selecting the right components is a cumbersome task for design engineers as they have to pick from a large number of possibilities. Therefore, we propose to analyze a data set of past assemblies composed of components from the same component catalog, represented as connected, undirected graphs of components, in order to suggest the next needed component. In terms of graph machine learning, we formulate this as graph classification problem where each class corresponds to a component ID from a catalog and the models are trained to predict the next required component. In addition to pretraining of component embeddings, we recursively decompose the graphs to obtain data instances in a self-supervised fashion without imposing any node insertion order. Our results indicate that models based on graph convolution networks and graph attention networks achieve high predictive performance, reducing the cognitive load of choosing among 2,000 and 3,000 components by recommending the ten most likely components with 82-92% accuracy, depending on the chosen catalog

Turning software engineers into machine learning engineers

A first challenge in teaching machine learning to software engineering and computer science students consists of changing the methodology from a constructive design-first perspective to an empirical one, focusing on proper experimental work. On the other hand, students nowadays can make significant progress using existing scripts and powerful (deep) learning frameworks -- focusing on established use cases such as vision tasks. To tackle problems in novel application domains, a clean methodological style is indispensable. Additionally, for deep learning, familiarity with gradient dynamics is crucial to understand deeper models. Consequently, we present three exercises that build upon each other to achieve these goals. These exercises are validated experimentally in a master's level course for software engineers

Graph machine learning for assembly modeling

Assembly modeling refers to the design engineering process of composing assemblies (e.g., machines or machine components) from a common catalog of existing parts. There is a natural correspondence of assemblies to graphs which can be exploited for services based on graph machine learning such as part recommendation, clustering/taxonomy creation, or anomaly detection. However, this domain imposes particular challenges such as the treatment of unknown or new parts, ambiguously extracted edges, incomplete information about the design sequence, interaction with design engineers as users, to name a few. Along with open research questions, we present a novel data set

PermeabilityNets: comparing neural network architectures on a sequence-to-instance task in CFRP manufacturing

Carbon fiber reinforced polymers (CFRP) offer highly desirable properties such as weight-specific strength and stiffness. Liquid composite moulding (LCM) processes are prominent, economically efficient, out-of-autoclave manufacturing techniques and, in particular, resin transfer moulding (RTM), allows for a high level of automation. There, fibrous preforms are impregnated by a viscous polymer matrix in a closed mould. Impregnation quality is of crucial importance for the final part quality and is dominated by preform permeability. We propose to learn a map of permeability deviations based on a sequence of camera images acquired in flow experiments. Several ML models are investigated for this task, among which ConvLSTM networks achieve an accuracy of up to 96.56%, showing better performance than the Transformer or pure CNNs. Finally, we demonstrate that models, trained purely on simulated data, achieve qualitatively good results on real data

Inferring material properties from FRP processes via sim-to-real learning

Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models

Soft Constraints in MiniBrass: Foundations and Applications

Over-constrained problems are ubiquitous in real-world decision and optimization problems, in particular, those emerging from self-organizing, autonomous systems where the full problem specification is only available at runtime. To address over-constrainedness, several theoretical formalisms to describe soft constraints have been proposed, including weighted, fuzzy, or probabilistic constraints. All of them were shown to be instances of algebraic structures such as valuation structures or c-semirings. In terms of implemented modeling languages and solvers, however, the field of soft constraints lags far behind the state of the art in classical constraint optimization. Therefore, this dissertation describes MiniBrass, a versatile soft constraint modeling language building on the unifying algebraic framework of partially-ordered valuation structures (PVS). It is implemented as an extension of MiniZinc and MiniSearch. The most important characteristics of MiniBrass, and the ones that distinguish it from previous work, are that it is extensible and modular, supports a variety of concrete soft constraint formalisms, works with many solvers (inherited from MiniZinc), admits a graphical modeling language, and has been applied to several real-life case studies. Contributions of this dissertation include the following: (1) The design, implementation, and performance evaluation of MiniBrass using 28 benchmark problems and six solvers, (2) A formal foundation that includes the systematic derivation of partially-ordered valuation structures and c-semirings from partial orders using basic category theory, (3) The qualitative soft constraint formalism constraint preferences, and (4) concepts for multiagent optimization with (possibly) antagonistic preferences, including lexicographic and Cartesian products as well as voting operators.Zahlreiche Entscheidungs- und Optimierungsprobleme in praktischen Anwendungen sind überbestimmt, also unlösbar mit der gegebenen Menge an Nebenbedingungen (Constraints). Im Besonderen betrifft dies jene Probleme, die dem Umfeld selbstorganisierender oder autonomer Systeme entstammen und deren Parameter erst zur Laufzeit vollständig bekannt sind. Zur Spezifikation und Lösung solcher Probleme wurden mehrere theoretische Formalismen vorgestellt, wie zum Beispiel gewichtete, unscharfe oder probabilistische Constraints, welche die Beschreibung weicher Bedingungen ermöglichen, um Probleme lösbar zu machen. All diese Formalismen lassen sich als Instanzen algebraischer Strukturen wie Bewertungsstrukturen oder C-Halbringen ausdrücken. Was tatsächlich implementierte Modellierungssprachen betrifft, hinkt das Gebiet der weichen Bedingungen allerdings weit dem Stand der Technik in klassischer Constraint-Optimierung hinterher. Zu diesem Zweck stellt diese Dissertation MiniBrass vor, eine vielseitige Modellierungssprache für weiche Bedingungen, die auf dem vereinheitlichenden algebraischen Rahmen der partiell geordneten Bewertungsstrukturen (eng. PVS) aufsetzt. Sie ist als Erweiterung zu MiniZinc und MiniSearch implementiert. Die wichtigsten Eigenschaften des MiniBrass-Systems (und zugleiche jene, die es von vorherigen Ansätzen abgrenzen) sind, dass es erweiterbar und modular ist, eine Vielzahl von konkreten Formalismen unterstützt, für zahlreiche Solver übersetzen kann (durch die Übersetzung nach MiniZinc), eine grafische Modellierungsform erlaubt und auf mehrere Fallstudien angewandt wurde. Zu den Beiträgen dieser Dissertation zählen: (1) der Entwurf, die Implementierung und die experimentelle Evaluation der Leistungsfähigkeit von MiniBrass auf 28 Benchmark-Problemen mit sechs Solvern; (2) eine formale Fundierung, welche die systematische Konstruktion partiell geordneter Bewertungsstrukturen und von C-Halbringen basierend auf partiellen Ordnungen mittels elementarer Kategorientheorie umfasst; (3) der qualitative Spezifikationsformalismus Constraint-Präferenzen; sowie (4) Konzepte und Algorithmen zur Optimierung in Multiagenten-Systemen mit (möglicherweise antagonistischen) Präferenzen unter Verwendung von lexikographischen und kartesischen Produkten sowie sozialen Auswahlfunktionen

Embedding constraint relationships into C-semirings

These notes provide technical details that are required to embed constraint relationships into the c-semiring framework presented in terms of category theory. It contains all steps required to map a dag to a partial order (Section 1), construct the free meet monoid from this partial order (Section 3) as well as the free c-semiring (Section 4). A constraint solving algorithm based on branch-and-bound search is presented in §34 for c-semirings and in §36 for meet monoids. A concrete instantiation for constraint relationships along with an example soft constraint problem concludes the report in Section 7