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

Weak properties and robustness of t-Hill estimators

International audienceWe describe a novel method of heavy tails estimation based on transformed score (t-score). Based on a new score moment method we derive the t-Hill estimator, which estimates the extreme value index of a distribution function with regularly varying tail. t-Hill estimator is distribution sensitive, thus it differs in e.g. Pareto and log-gamma case. Here, we study both forms of the estimator, i.e. t-Hill and t-lgHill. For both estimators we prove weak consistency in moving average settings as well as the asymptotic normality of t-lgHill estimator in iid setting. In cases of contamination with heavier tails than the tail of original sample, t-Hill outperforms several robust 2 P. Jordanova et al. tail estimators, especially in small samples. A simulation study emphasizes the fact that the level of contamination is playing a crucial role. The larger the contamination, the better are the t-score moment estimates. The reason for this is the bounded t-score of heavy-tailed distributions (and, consequently, bounded influence functions of the estimators). We illustrate the developed methodology on a small sample data set of stake measurements from Guanaco glacier in Chile

Using Elastically Actuated Legged Robots in Rough Terrain: Experiments with DLR Quadruped bert

This paper addresses walking and balancing in rough terrain for legged locomotion in planetary exploration as an alternative to the commonly used wheeled locomotion. In contrast to the latter, where active balancing is not necessary, legged locomotion requires constant effort to keep the main body stabilized during motion. While common quadrupedal robots require to carefully plan motions through torque control and force distribution, this paper presents an approach where elastic elements in the drive train function as an intrinsic balancing component that allows to ignore inaccuracies in the locomotion pattern and passively accommodate for terrain unevenness. The approach proposes a static walking gait algorithm, which is formulated for a general quadrupedal robot, and a hardware foot design to support the locomotion pattern. The method is experimentally tested on an elastically actuated quadrupedal robot

Genetic diversity of eleven European pig breeds

A set of eleven pig breeds originating from six European countries, and including a small sample of wild pigs, was chosen for this study of genetic diversity. Diversity was evaluated on the basis of 18 microsatellite markers typed over a total of 483 DNA samples collected. Average breed heterozygosity varied from 0.35 to 0.60. Genotypic frequencies generally agreed with Hardy-Weinberg expectations, apart from the German Landrace and Schwäbisch-Hällisches breeds, which showed significantly reduced heterozygosity. Breed differentiation was significant as shown by the high among-breed fixation index (overall FST = 0.27), and confirmed by the clustering based on the genetic distances between individuals, which grouped essentially all individuals in 11 clusters corresponding to the 11 breeds. The genetic distances between breeds were first used to construct phylogenetic trees. The trees indicated that a genetic drift model might explain the divergence of the two German breeds, but no reliable phylogeny could be inferred among the remaining breeds. The same distances were also used to measure the global diversity of the set of breeds considered, and to evaluate the marginal loss of diversity attached to each breed. In that respect, the French Basque breed appeared to be the most "unique" in the set considered. This study, which remains to be extended to a larger set of European breeds, indicates that using genetic distances between breeds of farm animals in a classical taxonomic approach may not give clear resolution, but points to their usefulness in a prospective evaluation of diversity