In the design of long fibre reinforced thermoplastic (LFT) structures, there is a direct dependency on the manufacturing. Therefore, it is indispensable to integrate the manufacturing influences into the design process. This not only offers new opportunities for material- and load-adapted designs, but also reduces cost-intensive modifications in later stages. The goal of this contribution is to make the complexity manageable by presenting a method which couples LFT manufacturing and structural simulations in an automated optimization loop. Herein, the influence of linear-elastic, local anisotropic material properties as well as residual stresses resulting from the compression molding of LFT on the stiffness-optimized design of beaded plates is investigated. Based on the simulation studies in this contribution, it can be summarized that the resulting bead height and flank angle, considering anisotropies and residual stresses, are smaller compared to isotropic modelling. As a conclusion, the strength constraint limits the maximum bead height and the flank angle needs to be additionally chosen as a consequence of the local fibre orientations and residual stresses resulting from manufacturing. Optimized bead cross sections are only valid for a specific system under investigation, as they depend on the defined boundary conditions (load case, initial charge geometry and position, fibre orientations, etc.).

Cable-driven parallel manipulators (CDPMs) offer advantages over traditional parallel manipulators. Though their ability to accelerate is higher than the traditional motion platforms, the capabilities are often not used optimally. The issues of cable slackening (especially at higher accelerations) and the emergence of singularity poses have traditional limitations. This paper analyzes and generates manipulator configurations that reduce the effect of these two essential hindrances of deploying CDPMs. A methodology, inspired by rigid body dynamics of multiple contact problems, used to optimize the positions of attachment points, is shown to be effective.

Consider a multidimensional risk model, in which an insurer simultaneously confronts m (m ≥ 2) types of claims sharing a common non-stationary and non-renewal arrival process. Assuming that the claims arrival process satisfies a large deviation principle and the claim-size distributions are heavy-tailed, asymptotic estimates for two common types of ruin probabilities for this multidimensional risk model are obtained. As applications, we give two examples of the non-stationary point process: a Hawkes process and a Cox process with shot noise intensity, and asymptotic ruin probabilities are obtained for these two examples.

The new reliability notion describing the remaining lifetime is introduced for items with monotonically increasing degradation. We consider the remaining lifetime of an item (to be called, the predicted remaining lifetime) after its degradation reaches the predetermined level. The prediction is executed at inception of an item into operation. For the nonhomogeneous stochastic processes of degradation, this characteristic depends on the random first passage time of a degradation process. Some properties of the predicted remaining lifetime and the corresponding stochastic comparisons are discussed for items from homogeneous and heterogeneous populations, and a generalization to the case of the n-component coherent system is outlined. The problem of regime switching is described, and the new notion of the corresponding virtual age after the switching is proposed.

Sobriety, well-filteredness, and monotone convergence are three of the most important properties of topological spaces extensively studied in domain theory. Some other weak forms of sobriety and well-filteredness have also been investigated by some authors. In this paper, we introduce the notion of Θ-fine spaces, which provides a unified approach to such properties. In addition, this general approach leads to the definitions of some new topological properties.

In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.