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The development of complex products and systems is a continuously iterative process from customer requirements to a mature design. Design changes derived from multisources occur frequently during the design process. Furthermore, change propagation will impose impacts on design costs and lead times. In view of this, how to predict and control the propagation of multisource design change to reduce the changes impact is an urgent issue in the development of complex product.

In this paper, a new four-phase routing approach based on weighted and directed complex networks is proposed for multisource design change propagation. Phase I: as the foundation of this research, a product network model is established to quantify describe the complex product. Phase II: the hub nodes are identified based on the LeaderRank algorithm, which can be regarded as multisource nodes of design changes. Phase III: a calculation method for change propagation intensity is proposed, which improves the systematicness and accuracy of the evaluation results. In this paper, change propagation intensity is defined by four assessment factors: importance degree of parts, execution time of design tasks, coupling strength between parts and propagation likelihood. Phase IV: a routing method of multisource design change propagation and ant colony optimization algorithm are proposed in this paper, which can solve the coupling conflicts among change propagation paths and improve the search efficiency by using the parallel search strategy.

The proposed method and another method are used to search the optimal propagation path of multisource design change of a motorcycle engine; the results indicate that this method designed in this study has a positive effect on reducing the change impact, market response time and product design costs when design change occurs in the products design process.

The authors find a new method (a network-based four-phase routing approach) to search the optimal propagation path of multisource design change in complex products design.

Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk.

A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component, a probabilistic inference algorithm within the Bayesian network updates the uncertain state of each component. Based on this mechanism, a set of algorithm was developed to derive optimal freeze sequence.

The authors derived the optimal freeze sequence of a helicopter design project from real product development process. The experimental result showed that our proposed method can significantly improve the effectiveness of freeze sequencing compared with arbitrary freeze sequencing.

The methodology identifies the optimal sequence for resolution of entire-system uncertainty in the most effective manner. This mechanism, in progressively updating the state of each component, enables an analyzer to continuously evaluate the effectiveness of the freeze sequence.

As the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).

The paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.

The digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.

This research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.

The purpose of this paper is to investigate the driving mechanisms for crack propagation regarding the related microstructures. Cracks in white etching layers have been found at the surface of submerged steel blades subjected to frictional sliding conditions.

In-situ monitoring revealed a fluctuation between mixed lubrication and hydrodynamic lubrication conditions. One lamella including a crack tip was prepared for transmission electron microscopy (TEM) using focused ion beam milling. Transmission electron microscope analysis was performed with the aim to understand the characteristics of the crack propagation, especially considering the influence of the microstructural configuration (grain refinement, carbides, martensite and ferrite grains).

The investigations have shown a grain-refined plastically deformed layer (friction martensite with grain sizes of < 100 nm) which influences the propagation direction of cracks introduced at the frictionally stressed surface. Thereby, the crack propagation is dominantly parallel to the margin of the grain-refined martensitic layer at the surface and the base material. Cracks were split into side cracks what mostly appears at present carbides. In this case, the crack propagation might strike through the carbide or separate it from the matrix due to the mechanical misfit.

For obtaining the results of this paper, a very special preparation of tribologically stressed samples was performed. Accordingly, specific findings of the crack propagation behavior under such conditions were achieved and are documented in the presented work. Moreover, the described crack propagation process is a combination of several mechanisms which occur in very limited region underneath the surface and are investigated by high-resolution TEM.

The purpose of this paper is to establish an adaptive assembly, to realize the adaptive changing of the models and to improve the flexibility and reliability of assembly change. For a three-dimensional (3D) computer-aided design (CAD) assembly in a changing process, there are two practical problems. One is delivering parameters’ information not smoothly. The other one is to easily destroy an assembly structure.

The paper establishes associated parameters design structure matrix of related parts, and predicts possible propagation paths of the parameters. Based on the predicted path, structured storage is made for the affected parameters, tolerance range and the calculation relations. The study combines structured path information and all constrained assemblies to build the adaptive assembly, proposes an adaptive change algorithm for assembly changing and discusses the extendibility of the adaptive assembly.

The approach would improve the flexibility and reliability of assembly change and be applied to different CAD platform.

The examples illustrate the construction and adaptive behavior of the assembly and verify the feasibility and reasonability of the adaptive assembly in practical application.

The adaptive assembly model proposed in the paper is an original method to assembly change. And compared with other methods, good results have been obtained.

The purpose of this paper is to achieve the capability of redesigning complex products system with enhanced efficiency and effectiveness.

It is noted that, by changing one subsystem in a complex product design architecture, the change effects can be propagated to other subsystems through their interrelationships. This condition has to be taken into account when deciding on which subsystems to be modified in the redesign plan because the subsequent effects might be too risky for the development process. Estimating redesign risk for complex product architectures is not an easy task, thus designers often need a decision-making aid to efficiently select the best “change initiating” subsystems. This can be done by providing the designers with a ranking of subsystems based on their estimated redesign risk. Moreover, this decision-making process is taking place during early redesign stages whereby the uncertainties related to the actual level and type of changes to be made on the subsystems are high. Because of this, a stochastic approach is taken to be more appropriate in deriving the redesign risk estimates. This leads to the proposed application of the Monte Carlo method to estimate the subsystem redesign risk for complex products, as demonstrated through an example case study of aircraft redesign.

This use of the Monte Carlo method helps to distinguish the level of risks associated with each subsystem in the complex product design architecture, which is helpful for designers while making decisions on which subsystems to be changed for the redesign task at hand.

This technique can be applied to assist designers in making decisions during the early stages of the redesign process under high design uncertainties.

The work presents a new alternative method to estimate the redesign risks of subsystems in complex products that can improve the effectiveness of the designer’s decision-making process in the early redesign stages. While many other available change methods tend to ignore the uncertainty associated with the decision-making process, the method presented here directly takes into account the stochastic nature of the process.

The purpose of this paper is to quantify external and internal uncertainties in product design process. The research addresses the measure of product future changes.

Two methods are proposed to model and quantify uncertainty in the product life cycle. Changes of user preferences are considered as the external uncertainty. Changes stemming from dependencies between components are addressed as the internal uncertainty. Both methods use developed mechanisms to capture and treat changes of user preferences. An agent-based model is developed to simulate sociotechnical events in the product life cycle for the external uncertainty. An innovative application of Big Data Analytics (BDA) is proposed to evaluate the external and internal uncertainties in product design. The methods can identify the most affected product components under uncertainty.

The results show that the proposed method could identify product changes during its life cycle, particularly using the proposed BDA method.

It is essential for manufacturers in the competitive market to know their product changes under uncertainty. Proposed methods have potential to optimize design parameters in complex environments.

This research bridges the gap of literature in the accurate estimation of uncertainty. The research integrates the change prediction and change transferring, applies data management methods innovatively, and utilizes the proposed methods practically.

The purpose of this paper is to present the results of experimental analysis of the elastic-guided wave mode conversion phenomenon in glass fiber-reinforced polymers. The results of this research presented in this paper are strictly focused on S0/A0’ mode conversion phenomenon caused by discontinuities in the form of circular Teflon inserts (artificial delaminations) and impact damage. Results of this research could be useful in problems of damage detection and localization.

In the research, guided waves are excited using a piezoelectric transducer and sensed in a non-contact manner using a scanning laser Doppler vibrometer. Full wavefield measurements are analyzed. Analysis of the influence of investigated discontinuities on S0/A0’ mode conversion is based on the elastic wave mode filtration in frequency-wavenumber domain. Mode filtration process allows us to remove the effects of the propagation of unwanted type of mode in forward or backward direction. Effects of S0/A0’ mode conversion are characterized by a mode conversion indicator (MCI) based on the amplitude of new mode A0’ and the amplitude of incident S0 mode.

It was noticed that the magnitude of MCI depends on the depth at which the Teflon inserts were located for all analyzed excitation frequencies and diameters of inserts (10 and 20 mm). The magnitude of MCI also increases with increasing impact energies. The S0/A0’ mode conversion phenomenon could be utilized for the detection of surface and internal located discontinuities.

This paper presents the original results of this research related to the influence of discontinuity location with respect to the sample thickness and severity of discontinuity on S0/A0’ mode conversion.

Simulation and analysis of a real main steam line break transient at the Thermal Power Plant Drmno are presented. The main events of the transient were the closure of isolation valves in front of a high pressure turbine, an opening of a by‐pass line, and subsequent pipe break in front of isolation valves. Intensive pressure waves were generated and they propagated through the pipe network of the steam line, causing high fluid dynamic forces on the structure. The transient has been simulated by the computer code TEA‐01, based on the Method Of Characteristics with three characteristic directions. Several main steam line boundary conditions have been modelled and verified. Numerical results are compared with plant data logger records. Simulation has been performed for various scenarios in order to investigate the plant behaviour sensitivity on the boundary conditions. The phenomenology of the pressure waves propagation and the influence of the boundary conditions on these processes are described in detail, as well as fluid dynamic forces during the closure of isolation valves and subsequent pipe break in a section of the steam line in the vicinity of the pipe break.

A vector autoregression model estimated on US data before and after 1980 documents systematic differences in the response of short- and long-term interest rates, corporate bond spreads and durable spending to news total factor productivity shocks. Interest rates across the maturity spectrum broadly increase in the pre-1980s and broadly decline in the post-1980s. Corporate bond spreads decline significantly, and durable spending rises significantly in the post-1980 period while the opposite short-run response is observed in the pre-1980 period. Measuring expectations of future monetary policy rates conditional on a news shock suggests that the Federal Reserve has adopted a restrictive stance before the 1980s with the goal of retaining control over inflation while adopting a neutral/accommodative stance in the post-1980 period.