​This paper proposes a framework to analyze the architectural complexity of systems developed with a product family. A product family is a set of products that are derived from common sets of parts, interfaces, and processes, known as the product platform. Through the use of product platforms, several variations of products can be developed in a short period of time with relatively low-engineering costs to capture market share in niche market segments as the demand arises. This work can be used in a variety of ways to guide product platform and variant architecture development during the initial concept generation stage. The effectiveness of the proposed framework is demonstrated through a case study of a train bogie platform.
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The process starts with building the design structure matrix (DSM) model, which captures the structural architecture as well as mass, energy, and information flow, for the product platform and its variants. Using the DSMs created and the selected complexity metric, the architectural complexity, which includes the structural complexity and flow complexity values, is assessed. Based on the quantitative results obtained, the overall complexity for the product platform and the product family could be compared with other competing product platform architecture and product family concepts. Furthermore, this process also allows system architects and decision makers to manage overall complexity of an entire product family, either through complexity minimization or by designing the entire platform and product architecture to be less sensitive to engineering changes in terms of complexity fluctuation.

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​Computational Design Synthesis (CDS) methods can be used to enable the computer to generate valid and even creative solutions for engineering tasks. In grammatical approaches to CDS, formal grammars are used to represent a desired design language. This language consists of vocabulary that usually describes components and subsystems of a design and a set of grammar rules that describe possible design transformations. The formalized engineering knowledge can then be used by the computer to synthesize designs. For most engineering tasks, two different kinds of rules are required: rules that change the topology of a design, i.e. how the components are connected, and rules that change parameters of a design. One of the main challenges in CDS using topologic and parametric grammar rules is to decide a priori which type of rule to apply in which stage of the synthesis process as well as whether to start from a valid design and perturb it or to start from a void design. The research presented in this paper compares different strategies for topologic and parametric rule applications during automated design synthesis driven by a search algorithm. The presented strategies are compared considering quantity and quality of the generated designs. The effect of the strategies, the selected search algorithm, and the initial design, from which the synthesis is started, are analyzed for two case studies: the synthesis of gearboxes and of bicycle frames. Results show that the effect of the strategy is dependent on the design task and recommendations are given on which strategies to use for which design task.

​Adding redundancy is a widely used method in engineering to improve the system reliability. How to add redundancy, (i.e., to meet the reliability requirement with the minimum cost), is an interesting topic in system design. Traditionally, the optimal redundancy allocation scheme is obtained under two simplified assumptions, i.e., binary states of each component and no failure dependency between components. The binary-state assumption assumes that each component and the entire system can only have two states: fully operational and completely failed. The failure independency assumption assumes no failure interaction between components, i.e., one component failure will not affect the failure process of other components. Although those two assumptions can simplify the analysis, they may lead to inaccurate reliability predictions and thus results in doubtful and misleading redundancy allocation scheme which in fact may not meet the reliability requirement. This work proposes a method to obtain the optimal redundancy allocation scheme by using the Semi-Markov process and optimization techniques without those two simplified assumptions. The target system is a type of commonly-seen system having multiple states and failure interactions. The target system contains a main subsystem providing the required output and an auxiliary subsystem helping the main subsystem function normally, such as the rotating subsystem and the lubricating subsystem, the computer mother board and the fan, and so on. A case study of a shipboard power electronic cabinet demonstrates the applicability of the proposed approach. 

Authors: Changming Yang, Xiaoping Du
J. Mech. Des. 136(10), 101405 (2014) (8 pages) Paper No: MD-13-1534; 
doi: 10.1115/1.4028016 

Robust design makes product performance stable under variations and noises in the environment. So the product can work robustly even in harsh conditions. This work explores a way to measure the robustness of a product when it has multiple performance variables, such as strength, efficiency, and cost. These performance variables are dependent and oftentimes conflicting, meaning that improving one performance variable may make others worse. The robustness of the worst-case performance variable is used as an indicator of the robustness of the entire product. Analytical and numerical algorithms are developed to calculate the robustness. The work makes it easy to model the robust design optimization with multiple performance variables as the single-objective optimization, thereby increasing the effectiveness of the robustness design process.