Design Change Model for Effective Scheduling Change Propagation Paths School of mechanical engineering, Southwest Jiaotong University, China Design Change Model for Effective Scheduling Change Propagation Paths Zhang Haizhu Email: zhanghaizhull@163.com

Table of Contents Introduction 1 Design change model and propagation analysis 2 Case study —High speed train’s bogie 3 Conclusions 4

Introduction The global competition in the market place for products motivates manufacturing firms to quickly develop products with improved performance and quality at lower costs. Design changes are key driving factors of product evolution. Understanding how and why changes propagate during the engineering design process is critical because most products and systems emerge from predecessors and do not through clean sheet design. An effective and controlled design change is helpful to satisfy customer requirements, reduce product defects, improve product quality, and promote product innovation.

Table of Contents Introduction 1 Design change model and propagation analysis 2 Case study —High speed train’s bogie 3 Conclusions 4

Design change model and propagation analysis The reasons for design change Firstly, Here you can see a figure which shows you the reasons for design change throughout the design process. In this figure, based on system engineering-V-model,Identifying the most common reasons for change throughout a product’s lifecycle. First, Changes are classified into initiated and emergent changes; initiated changes are changes due to new needs while emergent changes are responses to the product weaknesses. Initiated changes arise in response to customer requirements for a new version of the product or as a result of design innovation from within the company. Initiated changes including some types, The following new requirements typically exist at the beginning of the design process, Customer requirements, Certification requirements,and innovations,Problems with past designs ,Retrofits . Emergent changes occur in response to problems arising during the design process.Problems in design,testing, prototyping, manufacturing,use. This figure is quite complex, but the only thing I want you to focus on is initiated changes: customer requirements.

Design change model and propagation analysis Relationships of change dependency analysis, change processing analysis and change executing. The design change propagation analysis process main consists of three step: Initial Analysis. we uses product data and a model of change propagation to allow preliminary examination of component relationships. Including :create the product model; complete the dependency matrices and compute the predictive matrices. Change processing analysis, consisting of the identification of prospective changes and the presentation of predicted changes, select best change option. New product requirements trigger the change processing. Change executing, by this stage, designers and managers will know which sub-systems should be assigned additional resources to respond to likely changes. A necessary and final step of the redesign process is to return to the initial analysis in order to update the product model and direct dependency matrices for use in later projects. Based on the analysis process, we conduct the design change model and searching the change propagation path.

Design change model and propagation analysis Design change modeling Methods Characteristic Change favorable representation(C-FAR) Modeling of network-based Change prediction method(CPM) Components within design structure matrices Information structure framework(ISF) Cross-domain approach Parameter linkage network(PLN) Sort out and integrate various parameter linkages Systems Modeling Language (SysML) Described modeling approach Many methods and tools trying to model design change propagation and support design change prediction and analysis have been developed. The key design change modeling are listed in this table. Most of them model the product as a network of elements linked by their dependences and describe change propagation as the spread of knock-on effects along the paths of this network. however, they lack a system method to analysis the structure implicit the system’s function and behavior information. Then we focus on requirements as design change initiate layer, structure as design change executive layer, how to bulid the design change model between requirements and structure is a key problem. So we proposed a multi-layer network architecture model describe the design change. Design change initiate layer Modeling? Requirements Structure Design change executive layer

Design change model and propagation analysis The design change model based on PDS-Behavior-Structure (P-B-S) relations To bulid the design change model between requirements and structure ,Our P-B-S model is developed. For a CoPS, its PDS contains function requirements, performance requirements, and so on, the PDS is not only a design input, but also the evaluation criteria of a design scheme. The design change model starts with PDS and PDS as the design boundary. At the problem formation stage, there is a need to make the P-B mapping. Resulting in either a single discipline behaviour(SDB) model or a system function behaviour (SFB) model. The disciplinary behaviour matrix technique can be employed to form a matrix for this task. A multi-discipline structure is normally composed of part or whole of single discipline structures and a multi-disciplinary coupling structure to support the realization of the system function behaviour (SFB). In the design change model, with the change in requirement, Identify design parameter that must be modified and identify knock-on change to other structure. In a complex system, the components are connected through linking parameters. Thus, changing any one of these parameters may necessitate change in several other parameters within the system, which should be balanced and decided by designers. The research of P-B-S model has published in AMT. Next, we focus on the design change propagation path.

Design change model and propagation analysis The research methods of change propagation path Direct linkages Indirect linkages Component connectivity Propagation paths Design structure matrices - Change risk plot Propagation networks Propagation trees The main visulisation techniques are summarized in this table. Design structure matrices, change risk plot, propagation networks and propagation trees, when used to display large and complex products in direct linkages, indirect linkages, not every propagation path can be seen and analysed easily. There are many multi-disciplinary and multi-field coupling relationships of CoPS in functions, subsystems and components; and The linkage parameters space is large. displaying a large amount of information about a complex system is difficult. Then How to visual the change propagation path to guide the design change? Complex product systems Multi-disciplinary coupling relationships The linkage parameters space is large Searching ? Guide the design change Change propagation path

Design change model and propagation analysis Searching of change propagation path At first, the designer should determine which parameter should be changed in terms of the change requirement received. the initial changed parameter can be a direct parameter or a target parameter. Then, the designer should analyze the propagation caused by the initial change. Direct parameters lie on the bottom of the model, including geometry , material characteristics, safety factors, and environmental parameters . Target parameters lie on the top of the model and represent the design specifications that should be satisfied. When the initially changed parameter is a direct one, its change will trigger influence, which in turn will cause target parameters to change. If the change impact can be accepted, the change propagation will end. If the impact mitigation is required, change routing will be initiated, leading some direct parameters to change. If there are no coupled parameters in the change routing path, the change propagation will end; otherwise, new influence will be triggered, and a new cycle begins. When the initially changed parameter is a target parameter, the only difference is that change routing will be initiated firstly. When the change propagation finishes. The changed parameters form the change propagation path, which is a possible path solution for the change task. there are multiple ways to implement a change, each leading to a different change propagation path. Therefore, designers should try different feasible routing branches to find multiple propagation paths and select an optimal path from them. During the searching of change propagation paths, there is a large number of parameters which cannot be identify and exist the coupling relationship, and consequently, the designer cannot implement a change to a parameter no matter how he adjusts the strategy. It means the current design cannot accommodate the change requirement. In order to explore possible state and associated control connections. We use multi-disciplinary behaviour matrix to searching of influence propagation path. In order to not all relationships are treated equally and narrow the selection of requirements propagated. Rough set is applied, Rough set theory is a kind of objective and quantitative calculation method, this method need not consider coupling between the design parameters and nonlinear.

Table of Contents Introduction 1 Design change model and propagation analysis 2 Case study —High speed train’s bogie 3 Conclusions 4

Case study —High speed train’s bogie …… A high speed train’s Bogie is selected for our case study of designing change a CoPS. The proposed method and strategy are verified. The high speed train’s bogie includes Frame ,wheel set, primary suspension and so on ,is a complex product systems. First ,we build a P-B-S design change model, determine the target parameter, Take design speed change as an example, we main analysis a single discipline behavior-the vehicle dynamic behavior ,and multi-discipline behavior- brake behavior. Then, mapping from single discipline behavior to single discipline structure, including primary suspension and Secondary suspension, and multi-discipline behavior to multi-discipline coupling structure. In the structure parameters ,C1,C2 to C7,How to identify give priority to change which parameters? P1,P2 to P5,How to analysis the parameter change impact relationship? Next ,we may see the rough set achieve the process. P-B-S design change model

Case study —High speed train’s bogie Multi-disciplinary behaviour Matrix(DSM) The design of the braking system needs to consider the collaborative design of various disciplines with different physical principles. It includes four disciplines: the control, electrical, mechanical, and pneumatic, and their disciplinary behaviour parameters are interrelated. Figure shows each disciplinary behaviour, behaviour parameters and the relationship between these parameters. From Fig, we build the multi-disciplinary behaviour matrix M4 of control, electric, mechanical and pneumatic disciplines with reference to formula DBM. According to the DBM , we find the behaviour parameters cross disciplines are influenced by each other, and these behaviours need proper structure as a carrier to achieve. Their relationships with behaviours in terms of disc braking force, brake pad clamping force, brake cylinder thrust, and brake cylinder air pressure are then identified for developing multi-disciplinary coupling structures. Choosing different structural types and using different structural parameters will produce different behaviour structural solution. So the Multi-disciplinary behaviour Matrix(DSM) can show the relationships between the structure parameters and behaviour parameters, we can find the parameter change impact relationships between the behaviour parameters and the structural parameters to guide the design change.

Case study —High speed train’s bogie Relationship weighting—Rough set Vehicle dynamics important degree of suspension parameters calculation results   w1 w1* w2 w2* w3 c1 0.005 0.0080 c2 0.0545 0.0877 c3 0.004 0.0064 c4 1/30 0.5 0.1261 0.2029 0.3515 c5 0.0194 0.0312 c6 0.0045 0.0072 c7 0.4080 0.6565 0.5782 c1, c2, c3 and c5——Non-core design parameters c4 and c7 ——Core design parameters c7> c4> c2> c5> c1> c6> c3 Here you can see the relationship weighting is computed by rough set. The table shows the Vehicle dynamics important degree of suspension parameters calculation results. This equation basically tells you the compute process, w1 is Rough set important degree, in order to identify the Non-core design parameters and Core design parameters, The w1 column of the table indicates c1, c2, c3 and c5 are non-core design parameters for the Horizon ride index of Vehicle dynamic behavior, c4 and c7 are Core design parameters. W2 is mutual information important degree in order to sort the parameters. finally ,we obtain the comprehensive relationship weighting, resulting is c7> c4> c2> c5> c1> c6> c3. According to result, we can select Path1 and Path 2 as the optimal change propagation path. Rough set mutual information Path2 Path1

Table of Contents Introduction 1 Design change model and propagation analysis 2 Case study —High speed train’s bogie 3 Conclusions 4

Conclusions It facilitates designers to assess the scope of each proposed change accurately. (2) Based on this model, the multi-disciplinary behavior matrix analysis is applied to search change propagation paths effectively. (3) By using the rough set based space reduction tool, the design process becomes more efficient. (4) This method can be used in multi-disciplinary engineering design projects with some domain knowledge support. In the future, this design change model may be extended to support change propagation risk assessment and change propagation prediction.

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