scholarly journals Mechanical System Topology Optimization for better Maintenance

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Ngnassi Djami Aslain Brisco ◽  
Nzié Wolfgang ◽  
Doka Yamigno Serge
Keyword(s):  
Clustering Algorithm ◽  
Design Structure Matrix ◽  
Mechanical Equipment ◽  
Modular Architecture ◽  
Maintenance Costs ◽  
Economy Of Scale ◽  
Design Structure ◽  
Maintenance Work ◽  
Maintenance Problem ◽  
System Topology

For a given mechanical equipment, knowing its modular topology has the advantage of facilitating its maintenance. Indeed, during a maintenance problem, we will not act on the whole product except on the failed module (product subsystem) and we would also gain time to detect, diagnose and compensate for the observed failure. On the other hand, the clustering algorithm, which has served as a reference for several works has several limits. It generates much more complex and more expensive modules in terms of coupling costs, which could require more resources, more intervention time and more maintenance work. This has worse consequences for product maintenance, because the more complex the product modules are, the more expensive the maintenance is. We therefore propose an improved clustering algorithm which has the advantage of reducing maintenance costs by reducing the coupling and decoupling costs (Disassembly and reassembly costs) of the modules, generated by the reference algorithm for good maintainability (dis-assemblability). The application is made on a soy roaster. The approach followed in the proposed algorithm consists first of all in defining a DSM (Design Structure Matrix) which will make it possible to define the correction coefficients of the coupling cost, then in formulating an objective function to reduce the coupling costs, and finally to take into account the integrating elements to reduce the size of the modules. The result achieved is the proposal for a modular topology (modular architecture) leading to a significant reduction in maintenance costs. The developed algorithm also allows an economy of scale in reducing the complexity of the modules, promoting good maintainability.

Improved Clustering Algorithm for Design Structure Matrix

2012 ◽  
Author(s):  
Fredrik Borjesson ◽  
Katja Hölttä-Otto
Keyword(s):  
Clustering Algorithm ◽  
Hill Climbing ◽  
Design Structure Matrix ◽  
Computationally Efficient ◽  
Vacuum Cleaner ◽  
Local Optima ◽  
Structure Matrix ◽  
Design Structure ◽  
Clustering Problems

For clustering a large Design Structure Matrix (DSM), computerized algorithms are necessary. A common algorithm by Thebeau uses stochastic hill-climbing to avoid local optima. The output of the algorithm is stochastic, and to be certain a very good clustering solution has been obtained, it may be necessary to run the algorithm thousands of times. To make this feasible in practice, the algorithm must be computationally efficient. Two algorithmic improvements are presented. Together they improve the quality of the results obtained and increase speed significantly for normal clustering problems. The proposed new algorithm is applied to a cordless handheld vacuum cleaner.

Change and Maintenance Method for 3D Machining Procedure Model Based on Design Structure Matrix

2018 ◽  
Vol 32 (05) ◽  
pp. 1855006
Author(s):  
Ying Xiang ◽  
Rong Mo ◽  
Hu Qiao
Keyword(s):  
Propagation Model ◽  
Machining Process ◽  
Design Structure Matrix ◽  
Change Propagation ◽  
Machining Features ◽  
Structure Matrix ◽  
Design Structure ◽  
Process Route ◽  
Procedure Model ◽  
Maintenance Problem

A change and maintenance method is proposed based on the change propagation model and the procedure model information for solving data maintenance problem of 3D machining procedure model change and to help improve the flexibility of 3D machining procedure model and the reliability of the change result. Design Structure Matrix (DSM) is established by analyzing the relationships between machining features in the machining process route to obtain all possible propagation paths. On the basis of obtained paths, machining features that may be affected and machining procedure models related to machining features are stored by the structured method. Algorithms to solve the problems of adding, deleting and modifying machining features are proposed to realize the change and maintenance of 3D machining procedure model by combining machining procedure model’s information in the machining process route. In the end, some numerical examples are given to explain both rationality and feasibility of the proposed approaches.

Incorporating Constraints in System Modularization by Interactive Clustering of Design Structure Matrices

2016 ◽  
Author(s):  
Roozbeh Sanaei ◽  
Kevin N. Otto ◽  
Katja Hölttä-Otto ◽  
Kristin L. Wood
Keyword(s):  
Complex Systems ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Design Structure Matrix ◽  
Complex Products ◽  
Pressure Fields ◽  
Design Structure ◽  
Computational Capability ◽  
New Type ◽  
Interactive Clustering

Modularity is an approach to manage the design of complex systems by partitioning and assigning elements of a concept to simpler subsystems according to a planned architecture. Functional-flow heuristics suggest possible modules that have been demonstrated in past products, but using them still leaves it to the designer to choose which heuristics make sense in a certain architecture. This constitutes an opportunity for a designer to take other constraints and objectives into account. With large complex systems, the number of alternative groupings of elements into modular chunks becomes exponentially large and some form of automation would be beneficial to accomplish this task. Clustering algorithms using the design structure matrix (DSM) representation search the space of alternative relative positioning of elements and present one ideal outcome ordering which “optimizes” a modularity metric. Beyond the problems of lack of interactive exploration around the optimized result, such approaches also partition the elements in an unconstrained manner. Yet, typical complex products are subject to constraints which invalidate the unconstrained optimization. Such architectural partitioning constraints include those associated with external force fields including electric, magnetic, or pressure fields that constrain some functions to perform or not perform in different regions of the field. There are also supplier constraints where some components cannot be easily provided with others. Overall, it is difficult to simply embed all objectives of modular thinking into one metric to optimize. We develop a new type of interactive clustering algorithm approach considering multiple objectives and partitioning constraints. Partitioning options are offered to a designer interactively as a sequence of clustering choices between elements in the architecture. A designer can incorporate constraints that determine the compatibility or incompatibility of elements by choosing among alternative groupings progressively. Our aim is to combine computational capability of clustering algorithms with the flexibility of manual approaches. Through applying these algorithms to a MRI machine injector, we demonstrate the benefits of interactive cooperation between a designer and modularity algorithms, where constraints can be naturally considered.

Multi-Level Flow-Based Markov Clustering for Design Structure Matrices

2016 ◽  
Author(s):  
Tim (T.) Wilschut ◽  
Pascal (L. F. P.) Etman ◽  
Jacobus (J. E.) Rooda ◽  
Ivo (I. J. B. F.) Adan
Keyword(s):  
Clustering Algorithm ◽  
Visual Representation ◽  
System Structure ◽  
Underlying Structure ◽  
Design Structure Matrix ◽  
Structure Matrix ◽  
Design Structure ◽  
Input Parameters ◽  
Markov Clustering ◽  
Multi Level

For decomposition and integration of systems one requires extensive knowledge on system structure. A Design Structure Matrix (DSM) can provide a simple, compact and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed. In this paper, we present a new DSM clustering algorithm based upon Markov clustering. The developed clustering algorithm is able to cope with the presence of ‘bus’ elements, returns multilevel clusters, is capable of clustering both directed as well as undirected DSMs, and allows the user to control the cluster results by tuning only three input parameters.

Multilevel Flow-Based Markov Clustering for Design Structure Matrices

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
T. Wilschut ◽  
L. F. P. Etman ◽  
J. E. Rooda ◽  
I. J. B. F. Adan
Keyword(s):  
Clustering Algorithm ◽  
Central Processing Unit ◽  
System Structure ◽  
Underlying Structure ◽  
Design Structure Matrix ◽  
Processing Unit ◽  
Central Processing ◽  
Cpu Time ◽  
Design Structure ◽  
Markov Clustering

For decomposition and integration of systems, one needs extensive knowledge of system structure. A design structure matrix (DSM) model provides a simple, compact, and visual representation of dependencies between system elements. By permuting the rows and columns of a DSM using a clustering algorithm, the underlying structure of a system can be revealed. In this paper, we present a new DSM clustering algorithm based upon Markov clustering, that is able to cope with the presence of “bus” elements, returns multilevel clusters, is capable of clustering weighted, directed, and undirected DSMs, and allows the user to control the cluster results by tuning only three input parameters. Comparison with two algorithms from the literature shows that the proposed algorithm provides clusterings of similar quality at the expense of less central processing unit (CPU) time.

Fast Hybrid Genetic Clustering Algorithm for Design Structure Matrix

2013 ◽  
Author(s):  
Fredrik Borjesson ◽  
Ulf Sellgren
Keyword(s):  
Clustering Algorithm ◽  
Minimum Description Length ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Design Structure Matrix ◽  
Computer Algorithms ◽  
Structure Matrix ◽  
Design Structure ◽  
Genetic Clustering ◽  
Time Required

Module formation is the step in which a product’s architecture is established in such a way that complex interactions are intra-modular and inter-modular interactions are more simple. If a matrix representation exists, such as a Design Structure Matrix, this involves clustering system entities into groups with strong intra-dependencies. For simple products, clustering may be done manually, but for complex products, computer tools are required. Existing clustering algorithms are either slow, or unable to guarantee a globally optimal solution. To enable iterative work and to make cluster analysis useful also in the detailing steps, efficient and effective computer algorithms are required. This paper presents an efficient and effective Genetic clustering algorithm, with the Minimum Description Length measure. To significantly reduce the time required for the algorithm to find a good clustering result, a knowledge aware heuristic element is included in the GA process. The efficiency and effectiveness of the algorithm is verified with four case studies.

Modularization Method for Adaptable Products

2008 ◽  
Author(s):  
Lieke Arts ◽  
Magdalena K. Chmarra ◽  
Tetsuo Tomiyama
Keyword(s):  
Complex Systems ◽  
Design Methodology ◽  
Large Scale ◽  
Action Plan ◽  
Design Structure Matrix ◽  
Modular Architecture ◽  
Complex Products ◽  
Design Structure ◽  
Standard Design ◽  
New States

Adaptable products are gaining interests. Those products are able to adapt themselves to new environments, new states or new user defined tasks. There is not yet a standard design methodology for designing those products. This paper focuses on making large complex products (e.g. printers) more adaptable. Large-scale complex systems need to have modular architecture to some extent in order for engineers to be able to clearly comprehend the product. Therefore, a method to cluster components of an adaptable system is developed based on Design Structure Matrix (DSM) which stores information about connections between components. For each scenario or action plan to perform adaptability, the importance of component interconnections is rated in a separate DSM structure. By combining the original DSM with the adaptability DSM the engineers can group components. Finally, an example of a coffee maker is illustrated.

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