KI-gestützte produktionsgerechte Produktentwicklung : Automatisierte Wissensextraktion aus vorhandenen Produktgenerationen

Produzierende Unternehmen stehen heutzutage vor der Herausforderung, aufgrund des starken globalen Wettbewerbsdrucks in immer kürzerer Zeit dennoch innovative Pro-dukte zu einem möglichst günstigen Preis auf den Markt zu bringen. Insbesondere für die Produktentwicklung entsteht dadurch ein enormer Zeit- und Kostendruck, allerdings ist die sogenannte Time-to-Market entscheidend für den Markterfolg eines Produktes. Durch die Wiederverwendung von bereits existierenden Produktmodellen sowie dem darin enthaltenen Wissen besteht großes Potenzial, diese Entwicklungszeit zu reduzie-ren. Jedoch wird diese Wissensbasis in Form bestehender Produktmodelle häufig noch nicht systematisch genutzt. Das Problem ist, dass dieses größtenteils implizite Wissen häufig nicht ohne weiteres formalisierbar ist. Durch die zunehmende Nutzung digitaler Tools auch im Bereich der Produktentwicklung und die damit einhergehende wachsen-de Datenbasis ergibt sich über datengetriebene Ansätze jedoch die Möglichkeit, dieses (implizite) Wissen zu extrahieren, zu formalisieren und nutzbar zu machen. Das Ziel dieser Dissertation ist die Entwicklung einer Methode zur automatisierten Ex-traktion von implizitem Wissen in Form von Features und Mustern aus vorhandenen Produktmodellen mit Hilfe von Verfahren des Maschinellen Lernens sowie dessen anschließende Nutzung zur Unterstützung in der Produktentwicklung. Hierfür werden über Autoencoder zunächst die relevanten geometrischen Eigenschaften aus den Produkt-modellen in Form von CAD-Modellen erlernt. Darüber hinaus werden weitere produkt-beschreibende Informationen, die insbesondere die spätere Produzierbarkeit beeinflus-sen, extrahiert. Dieser geometrische Fußabdruck einer Produktkomponente bildet die Grundlage für die entwickelte Methode. Auf Basis von Konstruktionsmustern, die mit Hilfe von Recurrent Neural Networks aus den Produktmodellen erlernt werden, kann für einen gegebenen Konstruktionszustand der Folgezustand prädiziert werden. Auf Grundlage der geometrischen Eigenschaften dieser Vorhersage werden für das gege-bene (halbfertige) CAD-Modell die ähnlichsten bereits existierenden finalen Modelle aufgezeigt. Diese können anschließend bezüglich der weiteren Produktinformationen sortiert werden. Anhand der Menge der ähnlichsten Modelle können die produktionsre-levanten Produkteigenschaften des aktuellen Konstruktionszustandes durch das Auf-zeigen von üblichen Ausprägungskombinationen bewertet werden. Das Vorgehen wird anhand von mechanischen Komponenten eines industriellen Da-tensatzes demonstriert. Für verschiedene Konstruktionszustände beispielhafter Produk-te werden die Ähnlichkeitssuche, die Bewertung produktionsrelevanter Produkteigen-schaften, die Vorhersage nächster Konstruktionszustände sowie die Interaktion der ein-zelnen Methodenbausteine aufgezeigt. Durch die Methodik können bereits für anfängliche Konstruktionszustände ähnliche Produktmodelle identifiziert werden, wodurch die Wiederverwendung von Wissen ge-fördert sowie die Generierung von Dubletten reduziert werden. Darüber hinaus können bereits frühzeitig Hinweise auf mögliche Probleme bezüglich der späteren Produzierbarkeit gegeben werden

Deep Learning for Automated Product Design

Product development is a highly complex process that has to be individually adapted depending on the companies involved, the product to be developed and the related designers. Within this process, the approach and the know-how of the designer are very individual and can often only be described with high effort in a rule-based manner. Nevertheless, numerous routine tasks can be identified that offer enormous automation potential. Machine Learning, especially Deep Learning, has proven an immense capability to identify patterns and extract knowledge out of complex data sets. Autoencoder networks are suitable for the conversion of different 3D input data, e.g. Point Clouds, into compact latent representations and vice versa. Point Clouds are a universal representation of 3D objects and can be derived from various 3D data formats. The goal of the approach presented is to use Deep Learning algorithms to identify design patterns specific to a product family out of their underlying latent representation and use the extracted knowledge to automatically generate new latent object representations fulfilling distinct product feature specifications. A deep Autoencoder network with state-of-the-art reconstruction quality is used to encode Point Clouds into latent representations. In this approach, a conditional Generative Adversarial Network operating in latent space for generation of class-, characteristic- and dimension-conditioned objects is introduced. The model is quantitatively evaluated by a comparison of given specifications and the implemented features of generated objects. The presented findings can be used to support designers in the creation process by automatically proposing appropriate objects as well as in the adaption of future product variants to different requirements. This relieves the designer of time-consuming routine tasks and reduces the effort of knowledge-transfer between designers significantly

AI-Based knowledge extraction for automatic design proposals using design-related patterns

Engineering competence and the digitization of all processes along the product development process are highly decisive for today’s success of industrial companies. The design process is very individual and strongly based on design engineers’ experience. Part of this knowledge and the result of the design approach are fixated in the existing variations of the product generations, but are difficult to extract and to formalize. Conclusions about design-related patterns between products of different generations or variants can be drawn from the model tree representing the design engineer’s thinking process for each individual CAD model. However, the model tree has hardly been used so far. The aim of this paper is to examine whether there exist any common design patterns between CAD models of certain component classes by the exemplary use case in the area of mechanical engineering. To identify patterns and to extract knowledge out of complex data sets, Machine Learning (ML), especially Deep Learning, has proven an immense capability. Finally, based on the learned patterns, meaningful next design steps are to be proposed in the form of an assistance system. The results show that there exist common design patterns for various classes of components. It is illustrated on an exemplary component class that those patterns can be used to train an assistance system based on Recurrent Neural Networks (RNNs). The corresponding design patterns were extracted from data of an industrial application partner. By transferring these design patterns to the development of new product generations or variants, on the one hand the design process itself and thus the time to market can be shortened. On the other hand, the knowledge from previous product generations contained in those patterns can be preserved. For further research the design patterns of CAD models extracted by ML algorithms is a contribution to faster knowledge extrapolation

AI based geometric similarity search supporting component reuse in engineering design

Today, companies are faced with the challenge to develop and produce individualized products in the shortest possible time at very low cost in order to remain attractive under strong competitive pressure. For reasons of efficiency, products are therefore often developed in generations. Proven components are adopted in a new product generation and only some of the components are newly developed to meet new customer requirements. Many companies, therefore, have a large database of 3D CAD product models containing years of engineering experience. Nevertheless, it is often difficult to execute database queries to find which products or components already exist and could be reused or adapted for a new product generation or variant. As a result, many duplicates are created, which are associated with high effort and costs, and the risk of introducing design errors increases. Therefore, the aim of this paper is to develop an automated approach for geometric similarity search that also takes company-specific features of components into account. Machine learning methods are capable of automatically extracting relevant geometric features by learning a suitable representation of the corresponding 3D object. For this purpose, an autoencoder is developed which is trained to extract class-specific feature vectors. To improve the representativeness of those vectors for the similarity search, the architecture and hyperparameters of the autoencoder are optimized based on several experiments. Considering a real use case with a data set from the field of mechanical engineering, it is shown that geometrically similar CAD models can be found very quickly using the learned representation, and that better results are obtained than with conventional methods based on meta information, e.g. volume and bounding box. On the one hand, the fast finding of similar models encourages the reuse of existing solutions. On the other hand, standardization and, thus, economy of scale is promoted

Product-Production-CoDesign: An Approach on Integrated Product and Production Engineering Across Generations and Life Cycles

Shorter product life cycles and high product variance nowadays require efficient engineering of products and production systems. Hereby a further challenge is that costs over the entire life cycle of the product and production system are defined early in the process. Existing approaches in literature and practice such as simultaneous engineering and design for manufacturing incorporate aspects of production into product engineering. However, these approaches leave potential for increasing efficiency unused because knowledge from past generations of products, production systems, and business models is not stored and reused in a formalized way and future generations are not considered in the respective current engineering process. This article proposes an approach for integrated product and production engineering across generations and life cycles of products and production systems. This includes the consideration of related business models to successfully establish the products on the market as well as the anticipation of future product and production system characteristics. The presented approach can reduce both development and manufacturing costs as well as time to market and opens the vast technological potential for product design to achieve additional customer benefits. Three case studies elaborate on aspects of the proposed approach and present its benefits

Activation of PKR Causes Amyloid ß-Peptide Accumulation via De-Repression of BACE1 Expression

BACE1 is a key enzyme involved in the production of amyloid ß-peptide (Aß) in Alzheimer's disease (AD) brains. Normally, its expression is constitutively inhibited due to the presence of the 5′untranslated region (5′UTR) in the BACE1 promoter. BACE1 expression is activated by phosphorylation of the eukaryotic initiation factor (eIF)2-alpha, which reverses the inhibitory effect exerted by BACE1 5′UTR. There are four kinases associated with different types of stress that could phosphorylate eIF2-alpha. Here we focus on the double-stranded (ds) RNA-activated protein kinase (PKR). PKR is activated during viral infection, including that of herpes simplex virus type 1 (HSV1), a virus suggested to be implicated in the development of AD, acting when present in brains of carriers of the type 4 allele of the apolipoprotein E gene. HSV1 is a dsDNA virus but it has genes on both strands of the genome, and from these genes complementary RNA molecules are transcribed. These could activate BACE1 expression by the PKR pathway. Here we demonstrate in HSV1-infected neuroblastoma cells, and in peripheral nervous tissue from HSV1-infected mice, that HSV1 activates PKR. Cloning BACE1 5′UTR upstream of a luciferase (luc) gene confirmed its inhibitory effect, which can be prevented by salubrinal, an inhibitor of the eIF2-alpha phosphatase PP1c. Treatment with the dsRNA analog poly (I∶C) mimicked the stimulatory effect exerted by salubrinal over BACE1 translation in the 5′UTR-luc construct and increased Aß production in HEK-APPsw cells. Summarizing, our data suggest that PKR activated in brain by HSV1 could play an important role in the development of AD

Confirmation of the Type 2 Myotonic Dystrophy (CCTG)(n) Expansion Mutation in Patients with Proximal Myotonic Myopathy/Proximal Myotonic Dystrophy of Different European Origins: A Single Shared Haplotype Indicates an Ancestral Founder Effect

Myotonic dystrophy (DM), the most common form of muscular dystrophy in adults, is a clinically and genetically heterogeneous neuromuscular disorder. DM is characterized by autosomal dominant inheritance, muscular dystrophy, myotonia, and multisystem involvement. Type 1 DM (DM1) is caused by a (CTG)(n) expansion in the 3′ untranslated region of DMPK in 19q13.3. Multiple families, predominantly of German descent and with clinically variable presentation that included proximal myotonic myopathy (PROMM) and type 2 DM (DM2) but without the DM1 mutation, showed linkage to the 3q21 region and were recently shown to segregate a (CCTG)(n) expansion mutation in intron 1 of ZNF9. Here, we present linkage to 3q21 and mutational confirmation in 17 kindreds of European origin with PROMM and proximal myotonic dystrophy, from geographically distinct populations. All patients have the DM2 (CCTG)(n) expansion. To study the evolution of this mutation, we constructed a comprehensive physical map of the DM2 region around ZNF9. High-resolution haplotype analysis of disease chromosomes with five microsatellite and 22 single-nucleotide polymorphism markers around the DM2 mutation identified extensive linkage disequilibrium and a single shared haplotype of at least 132 kb among patients from the different populations. With the exception of the (CCTG)(n) expansion, the available markers indicate that the DM2 haplotype is identical to the most common haplotype in normal individuals. This situation is reminiscent of that seen in DM1. Taken together, these data suggest a single founding mutation in DM2 patients of European origin. We estimate the age of the founding haplotype and of the DM2 (CCTG) expansion mutation to be ∼200–540 generations