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We propose the so-called chance constrained programming model of stochastic programming theory to analyze limit and shakedown loads of structures under random strength with a lognormal distribution. A dual chance constrained programming algorithm is developed to calculate simultaneously both the upper and lower bounds of the plastic collapse limit and the shakedown limit. The edge-based smoothed finite element method (ES-FEM) is used with three-node linear triangular elements.
FEM shakedown analysis of structures under random strength with chance constrained programming
(2022)
Direct methods, comprising limit and shakedown analysis, are a branch of computational mechanics. They play a significant role in mechanical and civil engineering design. The concept of direct methods aims to determine the ultimate load carrying capacity of structures beyond the elastic range. In practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and constraints. If strength and loading are random quantities, the shakedown analysis can be formulated as stochastic programming problem. In this paper, a method called chance constrained programming is presented, which is an effective method of stochastic programming to solve shakedown analysis problems under random conditions of strength. In this study, the loading is deterministic, and the strength is a normally or lognormally distributed variable.
Direct methods comprising limit and shakedown analysis is a branch of computational mechanics. It plays a significant role in mechanical and civil engineering design. The concept of direct method aims to determinate the ultimate load bearing capacity of structures beyond the elastic range. For practical problems, the direct methods lead to nonlinear convex optimization problems with a large number of variables and onstraints. If strength and loading are random quantities, the problem of shakedown analysis is considered as stochastic programming. This paper presents a method so called chance constrained programming, an effective method of stochastic programming, to solve shakedown analysis problem under random condition of strength. In this our investigation, the loading is deterministic, the strength is distributed as normal or lognormal variables.
Summary: This paper presents a methodology to study and understand the mechanics of stapled anastomotic behaviors by combining empirical experimentation and finite element analysis. Performance of stapled anastomosis is studied in terms of leakage and numerical results which are compared to in vitro experiments performed on fresh porcine tissue. Results suggest that leaks occur between the tissue and staple legs penetrating through the tissue.
This paper presents a numerical procedure for reliability analysis of thin plates and shells with respect to plastic collapse or to inadaptation. The procedure involves a deterministic shakedown analysis for each probabilistic iteration, which is based on the upper bound approach and the use of the exact Ilyushin yield surface. Probabilistic shakedown analysis deals with uncertainties originated from the loads, material strength and thickness of the shell. Based on a direct definition of the limit state function, the calculation of the failure probability may be efficiently solved by using the First and Second Order Reliability Methods (FORM and SORM). The problem of reliability of structural systems (series systems) is handled by the application of a special technique which permits to find all the design points corresponding to all the failure modes. Studies show, in this case, that it improves considerably the FORM and SORM results.
Shakedown analysis of Reissner-Mindlin plates using the edge-based smoothed finite element method
(2014)
This paper concerns the development of a primal-dual algorithm for limit and shakedown analysis of Reissner-Mindlin plates made of von Mises material. At each optimization iteration, the lower bound of the shakedown load multiplier is calculated simultaneously with the upper bound using the duality theory. An edge-based smoothed finite element method (ES-FEM) combined with the discrete shear gap (DSG) technique is used to improve the accuracy of the solutions and to avoid the transverse shear locking behaviour. The method not only possesses all inherent features of convergence and accuracy from ES-FEM, but also ensures that the total number of variables in the optimization problem is kept to a minimum compared with the standard finite element formulation. Numerical examples are presented to demonstrate the effectiveness of the present method.
Shakedown analysis of two dimensional structures by an edge-based smoothed finite element method
(2010)
A procedure for the evaluation of the failure probability of elastic-plastic thin shell structures is presented. The procedure involves a deterministic limit and shakedown analysis for each probabilistic iteration which is based on the kinematical approach and the use the exact Ilyushin yield surface. Based on a direct definition of the limit state function, the non-linear problems may be efficiently solved by using the First and Second Order Reliabiblity Methods (Form/SORM). This direct approach reduces considerably the necessary knowledge of uncertain technological input data, computing costs and the numerical error. In: Computational plasticity / ed. by Eugenio Onate. Dordrecht: Springer 2007. VII, 265 S. (Computational Methods in Applied Sciences ; 7) (COMPLAS IX. Part 1 . International Center for Numerical Methods in Engineering (CIMNE)). ISBN 978-1-402-06576-7 S. 186-189
Proceedings of the International Conference on Material Theory and Nonlinear Dynamics. MatDyn. Hanoi, Vietnam, Sept. 24-26, 2007, 8 p. In this paper, a method is introduced to determine the limit load of general shells using the finite element method. The method is based on an upper bound limit and shakedown analysis with elastic-perfectly plastic material model. A non-linear constrained optimisation problem is solved by using Newton’s method in conjunction with a penalty method and the Lagrangean dual method. Numerical investigation of a pipe bend subjected to bending moments proves the effectiveness of the algorithm.
Molecular-genetic identification of emerged novel invasive pathogens of Asiatic Elm Ulmus pumila L
(2014)
The dwarf elm Ulmus pumila L. (Ulmaceae) is one of indigenous species of flora in Kazakhstan and forms a basis of dendroflora in virtually all settlements of the region. In the past decade, multiple outbreaks of previously unknown diseases of the small-leaved elm have been registered. In our study, by the molecular-genetic analysis it was found that the pathogens responsible for the outbreaks are microfungi belonging to the genus Fusarium – F. solani and F. oxysporum. The nucleotide sequences (ITS regions) isolated from the diseased trees showed very high similarity with the GenBank control numbers EU625403.1 and FJ478128.1 (100.0 and 99.0 % respectively). Oncoming research will focus on the search of natural microbial antagonists of the discovered phytopathogens.
At the present time, one of the most serious environmental problems of Central Asia and South Kazakhstan is the ongoing large-scale deterioration of principal urban tree populations. Several major centers of massive spread of invasive plant pests have been found in urban dendroflora of this region. The degree of damage of seven most wide-spread aboriginal tree species was found to range from 21.4±1.1 to 85.4±1.8%. In particular, the integrity of the native communities of sycamore (Platanus spp.), willow (Salix spp.), poplar (Populus spp.) and elm (Ulmus spp.) is highly endangered. Our taxonomic analysis of the most dangerous tree pests of the region has revealed them as neobiontic xylophilous insects such as Cossus cossus L. (Order: Lepidoptera L.) Monochamus urussovi Fisch., Monochamus sutor L., Acanthocinus aedelis L. and Ñetonia aureate L. (Order: Coleoptera L.). We relate the origin of this threatening trend with the import of industrial wood in the mid 90s of the last century that was associated with high degree of the constructional work in the region. Because of the absence of efficient natural predators of the pest species, the application of microbiological methods of the pest control and limitation is suggested.
This article addresses the need for an innovative technique in plasma shaping, utilizing antenna structures, Maxwell’s laws, and boundary conditions within a shielded environment. The motivation lies in exploring a novel approach to efficiently generate high-energy density plasma with potential applications across various fields. Implemented in an E01 circular cavity resonator, the proposed method involves the use of an impedance and field matching device with a coaxial connector and a specially optimized monopole antenna. This setup feeds a low-loss cavity resonator, resulting in a high-energy density air plasma with a surface temperature exceeding 3500 o C, achieved with a minimal power input of 80 W. The argon plasma, resembling the shape of a simple monopole antenna with modeled complex dielectric values, offers a more energy-efficient alternative compared to traditional, power-intensive plasma shaping methods. Simulations using a commercial electromagnetic (EM) solver validate the design’s effectiveness, while experimental validation underscores the method’s feasibility and practical implementation. Analyzing various parameters in an argon atmosphere, including hot S -parameters and plasma beam images, the results demonstrate the successful application of this technique, suggesting its potential in coating, furnace technology, fusion, and spectroscopy applications.
Simultaneous detection of cyanide and heavy metals for environmental analysis by means of µISEs
(2010)
Various planar technologies are employed for developing solid-state sensors having low cost, small size and high reproducibility; thin- and thick-film technologies are most suitable for such productions. Screen-printing is especially suitable due to its simplicity, low-cost, high reproducibility and efficiency in large-scale production. This technology enables the deposition of a thick layer and allows precise pattern control. Moreover, this is a highly economic technology, saving large amounts of the used inks. In the course of repetitions of the film-deposition procedure there is no waste of material due to additivity of this thick-film technology. Finally, the thick films can be easily and quickly deposited on inexpensive substrates. In this contribution, thick-film ion-selective electrodes based on ionophores as well as crystalline ion-selective materials dedicated for potentiometric measurements are demonstrated. Analytical parameters of these sensors are comparable with those reported for conventional potentiometric electrodes. All mentioned thick-film strip electrodes have been totally fabricated in only one, fully automated thickfilm technology, without any additional manual, chemical or electrochemical steps. In all cases simple, inexpensive, commercially available materials, i.e. flexible, plastic substrates and easily cured polymer-based pastes were used.
The determination of spacing, edge and end distance requirements for self-tapping screws requires numerous and comprehensive insertion tests. Yet the results of such tests cannot be transferred to other types of screws or even to screws of different diameter because of differences in shape or geometry. To reduce the effort of insertion tests a new method was developed which allows the estimation of required spacings, distances and timber thickness.
Adapting augmented reality systems to the users’ needs using gamification and error solving methods
(2021)
Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.
While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees’ engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.
Virtual Reality (VR) offers novel possibilities for remote training regardless of the availability of the actual equipment, the presence of specialists, and the training locations. Research shows that training environments that adapt to users' preferences and performance can promote more effective learning. However, the observed results can hardly be traced back to specific adaptive measures but the whole new training approach. This study analyzes the effects of a combined point and leveling VR-based gamification system on assembly training targeting specific training outcomes and users' motivations. The Gamified-VR-Group with 26 subjects received the gamified training, and the Non-Gamified-VR-Group with 27 subjects received the alternative without gamified elements. Both groups conducted their VR training at least three times before assembling the actual structure. The study found that a level system that gradually increases the difficulty and error probability in VR can significantly lower real-world error rates, self-corrections, and support usages. According to our study, a high error occurrence at the highest training level reduced the Gamified-VR-Group's feeling of competence compared to the Non-Gamified-VR-Group, but at the same time also led to lower error probabilities in real-life. It is concluded that a level system with a variable task difficulty should be combined with carefully balanced positive and negative feedback messages. This way, better learning results, and an improved self-evaluation can be achieved while not causing significant impacts on the participants' feeling of competence.
Gamification applications are on the rise in the manufacturing sector to customize working scenarios, offer user-specific feedback, and provide personalized learning offerings. Commonly, different sensors are integrated into work environments to track workers’ actions. Game elements are selected according to the work task and users’ preferences. However, implementing gamified workplaces remains challenging as different data sources must be established, evaluated, and connected. Developers often require information from several areas of the companies to offer meaningful gamification strategies for their employees. Moreover, work environments and the associated support systems are usually not flexible enough to adapt to personal needs. Digital twins are one primary possibility to create a uniform data approach that can provide semantic information to gamification applications. Frequently, several digital twins have to interact with each other to provide information about the workplace, the manufacturing process, and the knowledge of the employees. This research aims to create an overview of existing digital twin approaches for digital support systems and presents a concept to use digital twins for gamified support and training systems. The concept is based upon the Reference Architecture Industry 4.0 (RAMI 4.0) and includes information about the whole life cycle of the assets. It is applied to an existing gamified training system and evaluated in the Industry 4.0 model factory by an example of a handle mounting.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
Digital twins are seen as one of the key technologies of Industry 4.0. Although many research groups focus on digital twins and create meaningful outputs, the technology has not yet reached a broad application in the industry. The main reasons for this imbalance are the complexity of the topic, the lack of specialists, and the unawareness of the twin opportunities. The project "Digital Twin Academy" aims to overcome these barriers by focusing on three actions: Building a digital twin community for discussion and exchange, offering multi-stage training for various knowledge levels, and implementing realworld use cases for deeper insights and guidance. In this work, we focus on creating a flexible learning platform that allows the user to select a training path adjusted to personal knowledge and needs. Therefore, a mix of basic and advanced modules is created and expanded by individual feedback options. The usage of personas supports the selection of the appropriate modules.