Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

Effect of temperature and age on the relationship between dynamic and static elastic modulus of concrete

This study investigates the effects of cement type, curing temperature, and age on the relationships between dynamic and static elastic moduli or compressive strength. Based on the investigation, new relationship equations are proposed. The impact-echo method is used to measure the resonant frequency of specimens from which the dynamic elastic modulus is calculated. Types I and V cement concrete specimens with water-cement ratios of 0.40 and 0.50 are cured isothermally at 10, 23, and 50 °C and tested at 1, 3, 7, and 28 days.Cement type and age do not have a significant influence on the relationship between dynamic and static elastic moduli, but the ratio of static to dynamic elastic modulus approaches 1 as temperature increases. The initial chord elastic modulus, which is measured at low strain level, is similar to the dynamic elastic modulus. The relationship between dynamic elastic modulus and compressive strength has the same tendency as the relationship between dynamic and static elastic moduli for various cement types, temperatures, and ages.     

Creep behavior and failure mechanisms of CVI and PIP SiC/SiC composites at temperatures to 1650 °C in air

Creep properties of 2D woven CVI and PIP SiC/SiC composites with Sylramic™-iBN SiC fibers were measured at temperatures to 1650 °C in air and the data was compared with the literature. Batch-to-batch variations in the tensile and creep properties, and thermal treatment effects on creep, creep parameters, damage mechanisms, and failure modes for these composites were studied. Under the test conditions, the CVI SiC/SiC composites exhibited both matrix and fiber-dominated creep depending on stress, whereas the PIP SiC/SiC composites displayed only fiber-dominated creep. Creep durability in both composite systems is controlled by the most creep resistant phase as well as oxidation of the fibers via cracking matrix. Specimen-to- specimen variations in porosity and stress raisers caused significant differences in creep behavior and durability. The Larson-Miller parameter and Monkman-Grant relationship were used wherever applicable for analyzing and predicting creep durability.     

Memory-based adaptive partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms

A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is presented in this work. This algorithm works by performing an adaptive-probabilistic refinement of the search space, with no aggregation in objective space. This work investigated the integration of MAP within the state-of-the-art fast and elitist non-dominated sorting genetic algorithm (NSGAII). Considerable improvements in convergence were achieved, in terms of both speed and accuracy. Results are provided for several commonly used constrained and unconstrained benchmark problems, and comparisons are made with standalone NSGAII and hybrid NSGAII-efficient local search (eLS).     

UAV imagery based potential safety hazard evaluation for high-speed railroad using Real-time instance segmentation

Potential safety hazards (PSHs) along the track needs to be inspected and evaluated regularly to ensure a safe environment for high-speed railroad operations. Other than track inspection, evaluating potential safety hazards in the nearby areas often requires inspectors to patrol along the track and visually identify potential threads to the train operation. The current visual inspection approach is very time-consuming and may raise safety concerns for the inspectors, especially in remote areas. Using the unmanned aerial vehicle (UAV) has great potential to complement the visual inspection by providing a better view from the top and ease the safety concerns in many cases. This study develops an automatic PSH detection framework named YOLARC (You Only Look at Railroad Coefficients) using UAV imagery for high-speed railroad monitoring. First, YOLARC is equipped with a new backbone having multiple available receptive fields to strengthen the multi-scale representation capability at a granular level and enrich the semantic information in the feature space. Then, the system integrates the abundant semantic features at different high-level layers by a light weighted feature pyramid network (FPN) with multi-scale pyramidal architecture and a Protonet with residual structure to precisely predict the track areas and PSHs. A hazard level evaluation (HLE) method, which calculates the distance between identified PSH and the track, is also developed and integrated for quantifying the hazard level. Experiments conducted on the UAV imagery of high-speed railroad dataset show the proposed system can quickly and effectively turn UAV images into useful information with a high detection rate and processing speed.     

Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

This study focuses on ways to systematically evaluate stakeholder requirements when developing a smart industrial service ecosystem (SISE) in a complex industrial context. The SISE development requires considering the service requirement from both the complex industrial context and service ecosystem manners. This study proposes a systematic framework for stakeholder requirement evaluation in SISE. The first part of the framework is the industrial context-viable system model with ecological thinking (IC-VESM) to elicit the service requirements for the SISE, which facilitates a systematic analysis of the service value proposition and service requirement elicitation in the operational lifecycle of an entire industrial context. This second part of the framework proposes a method for evaluating service requirements that is both feasible and systematic. This is achieved by combining the Fuzzy Kano and AHP methods in a Pythagorean fuzzy (PF) environment. The PF Kano computes the categories and determines the weights of service requirements from a consumer perspective, while the PF AHP hierarchically analyzes the service requirements and provides pairwise comparison paths for design experts. Finally, an illustrative case study in a renewable energy context was used to demonstrate the feasibility and effectiveness of the methodology. The proposed theoretical model provides more reliable and systematic outcomes than traditional methods when eliciting service requirements and evaluating complex smart industrial service solutions. The study has practical implications by providing useful insights for companies to recognize key smart service requirements in complex industrial contexts and to improve sustainable development.     

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.     

The rim zone of cement-based materials – Barrier or fast lane for chemical degradation?

Sophisticated evaluation models for the long-term stability of cement-based systems demand a precise knowledge of the mechanisms of deterioration reactions, particularly respecting a permanent exposure to aqueous environments. Commonly, insights into these mechanisms are deduced from long-term investigations. However, these chemical reactions start immediately after exposure to aggressive environments causing rapid changes of composition and structure. Consequently, properties of its rim zone change, which affects transport processes in aqueous solutions. In laboratory experiments, the influence of these surface processes on the stability of cement-based materials exposed to different chloride solutions was studied as a function of time and temperature. Analysis of compositional and structural changes beneath the surface reveal the role of crystalline covering layers for chemical resistance. Such layers are often described as protective barriers. However, these processes in the rim zone can accelerate chemical degradation and subsequently reduce the resilience of the cement-based materials to aggressive aqueous environments.     

Partitioned formulation of internal and gravity waves interacting with flexible structures

This paper presents a partitioned modeling of internal and gravity fluid waves that interact with flexible structures. The governing interaction model consists of three completely partitioned entities: fluid model, structural model, and interface model that acts as an internal constraint on the fluid–structure interface boundary. Thus, the proposed partitioned multi-physics modeling can employ two completely modular fluid and structure software modules plus an interface solver, hence amenable to partitioned solution algorithms. The interface discretization can exploit the nonmatching interface algorithm previously developed via the method of localized Lagrange multipliers. Also noted is that the present fluid model can make use of widely available finite element software for standard Poisson-type problems.     

Adaptive isogeometric analysis by local h-refinement with T-splines

Isogeometric analysis based on non-uniform rational B-splines (NURBS) as basis functions preserves the exact geometry but suffers from the drawback of a rectangular grid of control points in the parameter space, which renders a purely local refinement impossible. This paper demonstrates how this difficulty can be overcome by using T-splines instead. T-splines allow the introduction of so-called T-junctions, which are related to hanging nodes in the standard FEM. Obeying a few straightforward rules, rectangular patches in the parameter space of the T-splines can be subdivided and thus a local refinement becomes feasible while still preserving the exact geometry. Furthermore, it is shown how state-of-the-art a posteriori error estimation techniques can be combined with refinement by T-splines. Numerical examples underline the potential of isogeometric analysis with T-splines and give hints for further developments.