Structure Matrix Method Research Articles

Engineering Management and Modular Design: A Path to Robust Manufacturing Processes

Manufacturing environments, characterized by dynamic changes and uncertainties, demand effective strategies to minimize disruptions. This study introduces an innovative approach that integrates engineering management principles with modular design to prioritize risk mitigation and enhance robustness in manufacturing processes. From a systems engineering perspective, all manufacturing activities are perceived as interconnected components within a unified system. Leveraging the Axiomatic Design (AD) theory and the Design Structure Matrix (DSM) method, the study modularizes manufacturing process architecture to effectively curb risk propagation and manage system complexity. This study identifies the most optimal design as a pivotal architectural configuration, significantly improving the structural robustness and stability of the System of Interest (SOI). Empirical evidence supports this design’s capability to reduce complexities, thereby enhancing robustness within the broader system architecture. Notably, the proposed approach results in a substantial reduction in complexity, with the most optimal design exhibiting an approximately 82.79 percent reduction in work volume compared to the original design. Our research underscores the critical relationship between manufacturing and engineering management. Effective collaboration between these domains optimizes resource allocation, decision-making processes, and overall organizational strategy, leading to improved production processes and increased efficiency. Importantly, the study demonstrates a significant enhancement in modularization, resulting in elevated overall robustness in manufacturing processes. This highlights the proactive involvement of engineering management in the design phase to address production challenges, ultimately optimizing system performance. Thus, this research contributes to both practical applications and academic discourse by offering a novel approach to enhancing the robustness in manufacturing processes. By integrating engineering management principles and modular design strategies, organizations can fortify their processes against disruptions and effectively adapt to evolving circumstances.

Systematic design of a wind turbine by Design Structure Matrix (DSM) method, case study: Lootak region

The systematic design of a wind turbine is investigated using the Design Structure Matrix (DSM) method. As a complex system, a wind turbine has many interconnections between its components, which could be analyzed by the DSM method. First, the system was decomposed into subsystems; then, the design matrix was prepared and optimised according to the dependencies between the design parameters. Finally, the primary matrix was optimised, and a design matrix was obtained as a wind turbine systematic design guide. By the presented tool, the design parameters of a 2000kW wind turbine for the Lootak region of Zabol, Sistan and Baluchestan province were obtained. To achieve the rated power of 2000kW, the optimum wind turbine parameters were found to be as the rotor diameter of 77, 60 m hub height, rated wind speed of 11.85 m/s, and speed increase transmission ratio of 1: 11.

Machine learning assisted strip waveguide Bragg gratings design for refractive index-based biosensing applications

Waveguide Bragg grating (WBG) biosensors have attracted significant interest because of their high sensitivity, immunity to electromagnetic signals, and capability for lab-on-chip applications. Designing a WBG biosensor requires solving Maxwell’s equations for the optical waveguide geometries numerically. This process is resource-intensive and hinders quick optimization of the waveguides for sensing. In this work, we explore the design of a silicon strip WBG structure for biosensing using machine learning. We develop an artificial neural network (ANN) classification model for mode classification and a regression model for predicting the effective refractive index (neff) for a given strip waveguide. We utilize results from finite element method and Transfer Matrix method for strip WBG structures to train a multi-output ANN regression model for quickly predicting sensing parameters. The mode classification model provides an accuracy of over 99%, and the regression model predicts the neff with a mean absolute error (MAE) of 0.5%. The multi-output regression model predicts the sensitivity with 0.5% MAE and quality factor and maximum reflectivity with less than 3% MAE. ANN model training requires a few minutes of computational time and reduces the dependency on computationally expensive resources for optimization of the sensor design, thereby fast-tracking the biosensor design cycle.

Rank‐structured approximation of some Cauchy matrices with sublinear complexity

AbstractIn this article, we consider the rank‐structured approximation of one important type of Cauchy matrix. This approximation plays a key role in some structured matrix methods such as stable and efficient direct solvers and other algorithms for Toeplitz matrices and certain kernel matrices. Previous rank‐structured approximations (specifically hierarchically semiseparable, or HSS, approximations) for such a matrix of size cost at least complexity. Here, we show how to construct an HSS approximation with sublinear (specifically, ) complexity. The main ideas include extensive computation reuse and an analytical far‐field compression strategy. Low‐rank compression at each hierarchical level is restricted to just a single off‐diagonal block row, and a resulting basis matrix is then reused for other off‐diagonal block rows as well as off‐diagonal block columns. The relationships among the off‐diagonal blocks are rigorously analyzed. The far‐field compression uses an analytical proxy point method where we optimize the choice of some parameters so as to obtain accurate low‐rank approximations. Both the basis reuse ideas and the resulting analytical hierarchical compression scheme can be generalized to some other kernel matrices and are useful for accelerating relevant rank‐structured approximations (though not subsequent operations like matrix‐vector multiplications).

Design of integrated manufacturing information systems for reconfigurability and adaptability by modularizing the system architecture

ABSTRACT The present study addresses integrated manufacturing information systems (IMIS) and highlights importance of realizing ‘adaptability’ and ‘reconfigurability’ characteristics in the system architecture of IMISs, where engineering design for system adaptability is interested. This study develops a matrix-based probabilistic definition for the manufacturing information system (MIS) reconfigurability and proposes modularization of physical architecture of the system as an effective design solution for fulfilling system reconfigurability and adaptability requirements. To modularize the physical architecture, Design Structure Matrix (DSM) method is employed. Moreover, information axiom of Axiomatic Design (AD) theory is also utilized to develop helpful indicators for evaluating the adaptability characteristic for the architecture and support engineering designers to make sound decisions in design for adaptability. To verify the developed methodology, the MIS in a real case (Barez Industrial Group in Iran) is addressed and design of the physical architecture for this system is studied. A new optimally modularized design for the physical architecture of the MIS of interest is finally presented and examined. Results of the indicators reflecting reconfigurability and adaptability of the architecture indicate that the new proposed design of the concerned MIS is highly capable of coping with the rapidly changing manufacturing environment.

First-principles based study of magnetic states and high-pressure enthalpy landscape of manganese sulfide polymorphs

Using first-principles calculations in combination with special quasirandom structure and occupation control matrix methods, we study the magnetic ordering and the effect of pressure on manganese sulfide polymorphs. At ambient conditions, MnS is commonly observed in paramagnetic rock salt structure, but as the temperature decreases at constant pressure, it becomes antiferromagnetic. On the other hand, at room temperature, MnS has shown to undergo structural transformations as pressure increases. Here, we show that our approach involving the ordering/disordering of the local magnetic moments in addition to the explicit control of the localization of the Mn d-electrons produces energy bandgaps and local magnetic moments in excellent agreement with those observed experimentally, particularly for paramagnetic MnS. Finally, we focus on how MnS evolves under pressure, and from its enthalpy landscape, we identify at about 21 GPa, the structural transformation from rock salt to orthorhombic MnP-type. This structural transformation resembles closely experimental results in which a new stable but unidentified MnS phase was previously reported.

Modular Clustering of UAV Launch System Architecture Based on HDDSM

Rapid launch is one of the key indicators of UAV swarm operations, and one of the key decisions that determines the ultimate success of launch system development is the choice of its product architecture. The high-definition design structure matrix (HDDSM) method is introduced to model the product architecture of a UAV launch system, which reduces the workload of creating binary DSMs and improves the repeatability of product architecture design matrix (PADSM) creation. On this basis, this paper presents the classification of row (column) elements based on PADSM to correct the clustering objective function, and introduces the network structure clustering algorithm (SCAN). Combined with matrix block processing, a better UAV launch system architecture modular configuration scheme is obtained.

Surface plasmon resonance (SPR)-based biosensor using MXene as a BRE layer and magnesium oxide (MgO) as an adhesion layer

In this paper, a plasmonic sensor consists of a bimetallic layer of Ag and Au, a nano-thin layer of two-dimensional material MXene and a thin layer of Magnesium oxide (MgO) is proposed to operate in a visible region. Using Kretschmann configuration-based structure and transfer matrix method, the change in the refractive index of liquid Biosample have been observed at a fixed incident wavelength. Using the distinctive properties of Ti3C2Tx MXene and MgO, we have investigated the performance of Surface Plasmon Resonance (SPR) biosensor. Significant performance parameters such as Sensitivity, Figure of Merit (FoM) and Detection Accuracy (DA) calculated for different cases to prove the capability of proposed sensing structure. We also compared the sensitivity and sharpness of SPR curve obtained when using conventional adhesion layers such as titanium (Ti), chromium (Cr), and tantalum (Ta). A detailed investigation is carried out to observe the role of polymer as an adhesion layer and its thickness impact on FoM and resonance angle sharpness. The concept of Long range SPR (LR-SPR) and Short range SPR (SR-SPR) are also discussed.

Numerical Simulation Study on Hydraulic Transients in Hydropower Station with Trigeminy Surge Tank

A static and dynamic model for hydraulic transient calculation of three-throttled-orifice surge tank was established based on the basic equations of surge tank, by using structure matrix method (matrix analysis of structure?). Control conditions in an engineering practice were calculated by a simplified model and a three-throttled-orifice model respectively. The results indicate that the maximum pressure at the volute end, the maximum rising rate of unit speed and the water level extremum of the surge tank are basically identical by using these two models, but the minimum pressure at draft tube inlet is significantly different. The three-throttled-orifice model was found capable of calculate the flow between throttled orifices at constant state and the pressure behavior at draft tube inlet during the transition accurately. The selection of the resistance factor is discussed in the end, and it is proved that the resistance factor is allowed to have a certain tolerance in the transition calculation, which will not obviously affect the maximum pressure at draft tube inlet and water level extremum of the surge tank. This research can provide reliable reference for the transient calculation of three-throttled-orifice surge tank in similar engineering practice.

Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods.

Sinograms are commonly used to represent the raw data from tomographic imaging experiments. Although it is already well-known that sinograms posess some amount of redundancy, in this work, we present novel theory suggesting that sinograms will often possess substantial additional redundancies that have not been explicitly exploited by previous methods. Specifically, we derive that sinograms will often satisfy multiple simple data-dependent autoregression relationships. This kind of autoregressive structure enables missing/degraded sinogram samples to be linearly predicted using a simple shift-invariant linear combination of neighboring samples. Our theory also further implies that if sinogram samples are assembled into a structured Hankel/Toeplitz matrix, then the matrix will be expected to have low-rank characteristics. As a result, sinogram restoration problems can be formulated as structured low-rank matrix recovery problems. Illustrations of this approach are provided using several different (real and simulated) X-ray imaging datasets, including comparisons against a state-of-the-art deep learning approach. Results suggest that structured low-rank matrix methods for sinogram recovery can have comparable performance to state-of-the-art approaches. Although our evaluation focuses on competitive comparisons against other approaches, we believe that autoregressive constraints are actually complementary to existing approaches with strong potential synergies.

A DSM‐Based CCPM‐MPL Representation Method for Project Scheduling under Rework Scenarios

Rework risks caused by information flow interactions have become a major challenge in project scheduling. To deal with this challenge, we propose a model integrating the critical chain project management method, design structure matrix method, and max‐plus method. Our model uses a start‐to‐start relationship of activities instead of the traditional finish‐to‐start relationship, which also allows overlaps between activities. We improve the accuracy of the rework safety time in two ways: (1) the overall overlapping effect is taken into consideration when calculating the rework time of an activity arising from the information flow interaction of its multiple predecessors overlapped with it; (2) the rework time arising from activity overlaps, the first rework time, and the second rework time are calculated as components of the rework safety time in our model, while the last one is ignored in traditional methods. Furthermore, the accuracy of time buffers is improved based on the improved rework safety time. Finally, we design the max‐plus method to generate project schedules and appropriately sized time buffers. The empirical results show that the project schedule generated by the proposed method has a higher on‐time completion probability, as well as more appropriately sized project buffers.

A structured approach to measuring and optimizing the organizational architecture in global product development projects

In global product development projects, coordinating complex technical communication among teams across time zones in the overlapping process is a fundamental challenge. To optimize a global product development organization to reduce the negative impact of time separation on coordination, this article presents an innovative approach to solve two key problems: how to quantify the coordination among distributed teams and how to identify efficient global product development organization. We build structural models to capture the coordination dependency strength among global product development teams from the time separation and overlapping process perspectives, employing the design structure matrix method. Then, we analyze the factors that influence synchronous and asynchronous communication due to time separation. We also apply the random walk method to identify a team similarity matrix, taking into account the direct and indirect dependency relationships among teams associated with their performed activities. Finally, we use the team similarity matrix as an input to a spectral clustering algorithm to identify the clustering strategy for the global product development organizational architecture, yielding and reinforcing several managerial insights, including how to analyze the coordination among global product development teams as well as how the offshoring costs impact the clustered organization.

Conceptual design of Ergonomic Food truck using QFD-GRA-DSM Hybrid methodology

As the competition among the food truck business entrepreneurs is growing rapidly in India, food truck owners have to capture the tastes of the consumers from time to time, maintain trained staff for food preparation, create healthy, hygienic environment, ensure quality in service etc. In addition to these, the effective utilisation of limited kitchen space to meet all the requirements is essential. In this context, ergonomic intervention in designing kitchen space of the food truck is one of the prime considerations. In this paper, a methodology is developed by using quality function deployment (QFD), grey relational analysis (GRA) and design structure matrix (DSM) method for conceptual design of ergonomic food truck with a view to deploy the requirements of users in to the design of kitchen portion of the food truck. The proposed methodology is demonstrated through a case study in this paper.

Compare the Calculations of Steam Extraction Efficiency of Power Plant Turbine by Simple Heat Balance Method and Equivalent Enthalpy Drop Method

At present, the calculation method of steam extraction efficiency of power plant turbine have five methods: heat balance method, equivalent enthalpy drop method, cyclicfunctional method, composite structure method and matrix method. In this paper, a 600MW grade subcritical thermal power plan is take as an examplefor comparing the calculation by the simple heat balance method and the equivalent enthalpy drop method. The result shows that the computational results of simple heat balance method agree with equivalent enthalpy drop method. So simple heat balance method can be used to replace equivalent enthalpy drop method in order to reduce calculation amount in practicalapplication.

The CASAD Matrix Method: Introduction of a Technique for the Documentation, Analysis, and Optimization of Context-Aware Systems

Abstract The availability of precise sensors and the extensive usage of smart devices enable the development of systems that can sense their environment and recognize specific contexts. In recent years, so-called context-aware systems gained increasing popularity as they can provide situational assistance and automate nondeterministic processes. Despite their advantages, these systems still play a minor role in industrial applications due to reasons such as the increased efforts required for the engineering and development process and the absence of methods to handle the inherent complexity. This paper introduces the CASAD matrix method that facilitates the development of context-aware systems and reduces their complexity to a manageable level. The matrix is based on the design structure matrix method of Steward [1] and allows the structured documentation, analysis, and optimization of context-related systems properties. Further, different approaches for analyzing the matrix are shown and their outcomes discussed. Finally, the practical application is demonstrated in an industrial use case. Thereby, the CASAD matrix method is used to document, analyze, and optimize the contextually assisted activities, the contextual services, and the contexts of a context-aware assistance system for the shop floor of an injection molding company.

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

AbstractThe perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.

Dependency Structure Matrix and Hierarchical Clustering based algorithm for optimum module identification in MEP systems

Abstract Modular prefabrication of mechanical, electrical and plumbing (MEP) systems has become more prevalent during the last decade with the growth of the prefabricated construction industry. However, it is currently accomplished only for smaller systems where integrated packaged units are applied in heating, ventilation and air conditioning (HVAC) and other building services installations. The term ‘optimum modularity’ is rarely accurately used in the industry due to lack of an efficient modularisation method. In this investigation, an automated efficient modularisation algorithm has been developed using an approach which combines fuzzy logic, Dependency Structure Matrix (DSM) and Hierarchical Clustering (HC) methods. The developed algorithm achieves minimum total installation cost by identifying the optimum number modules and module division points based on assembly cost and the handling cost of each module. It is shown that the optimum modularity for a system is highly dependent on the module dimensions and the module division point.

Critical Chain Design Structure Matrix Method for Construction Project Scheduling under Rework Scenarios

Rework risks have been a major challenge in the construction industry that constantly affects project schedules and threatens on-time project completion. Traditional project scheduling methods are not capable of modeling rework relationships between activities and mitigating the impact of resulting uncertainties during the development of project schedules. To address this challenge, a critical chain design structure matrix (CCDSM) method is proposed in this paper. The CCDSM method aims to develop construction project schedules that are adaptive to rework scenarios and robust against rework risks. The CCDSM method models and displays large-scale rework relationships among activities and introduces a new rework buffer to quantitatively represent the impact of rework instances in project schedules. A max-plus algorithm is adopted in CCDSM to transform complex logic relationships into simple matrix operations, reducing computational load of schedule generation. A case study was conducted to demonstrate the implementation of the CCDSM method and assess its effectiveness in addressing rework risks. The results showed that the CCDSM is a promising tool to generate schedules, which could improve on-time project completion rate and reduce impacts of varying rework scenarios on project execution.

함정 개념설계 프로세스 최적화를 위한 시스템엔지니어링 기반의 설계구조행렬 방법론 적용

Naval ship design and related other activities can be characterized by the complexity of the interactions among products, activities, and disciplines. Such complexities often result in inferior designs, cost overrun, and late-delivery. Hence there exist tremendous interests in both improving the design process itself and optimizing the interactions among design activities. This paper looks at the complexity of designing naval ships thereby leading to the innovation of current ship design practices using design structure matrix. It can be used to induce the optimal ordering of design activities as well as identify sources of complexities. The method presented here identifies coupled design activities useful for reducing the complexity of naval ship design as well as optimally reordering design activities. This paper recommends the use of design structure matrix method suitable for numerically optimizing the concept design process of naval ship, and reducing cost and time required in designing naval ships by modeling and analyzing the design activities and engineering tasks, defined in systems engineering planning documents.

Моделирование структурной организации единого информационного пространства предприятия-разработчика микроэлектронных систем

During the process of forming a unified information space (UIS) is widely used modeling of information exchange facilities and UIS infrastructure. Despite the large variety of approaches to modeling a UIS, in particular using the design structure matrix method, the proposed solutions do not provide a direct link between elements of the described structure and hardware and software equipment. Also, existing approaches for modeling the UIS do not take into account possible external and internal constraints imposed on the placement of UIS components in the enterprise space. The paper proposes an approach to formalizing the UIS structure of the microelectronics design center, which is intended to eliminate these shortcomings. The structure of the UIS according to a parallel organization model includes following main components: a product data management system, participants of the microelectronic system design process, data-processing hardware and software. The structure of the UIS as a dynamic system can be reduced to a generalized model that includes the set of subscribers – participants of the design process, primary and secondary data-processing tools and objects of information exchange. The mathematical description is made using elements of theory of sets, matrices and queuing systems. The proposed model takes into account the constraints that affect a form of implementation of the UIS structure, as well as many characteristics of software and hardware. Simulation modeling of the UIS structural organization, presented as a set of multichannel queuing systems, helped to draw conclusions about bandwidth and reliability of considered variants for the UIS organization based on proposed hardware (the server solution) and software (the database management system) tools that are being used.