In order to obtain superior design solutions, the largest possible number of design alternatives, often expressed as discrete design variables, should first of all be considered, and the best design solution should then be selected from this wide set of alternative designs. Also, product designs should be initiated from the earliest possible stages, such as the conceptual and fundamental design stages, when discrete rather than continuous design variables have primacy. Although the use of discrete design variables is fundamentally important, this has implications in terms of computational demands and the accuracy of the optimized solution. This paper proposes an optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics. The optimizations are started at the lowest levels of the hierarchical optimization structure, and proceed to the higher levels. The discrete design variables are efficiently selected and optimized as smaller sub-optimization problems at the lowest hierarchical levels, while the optimum solutions for the entire problem are obtained by conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems having several types of discrete design variables are constructed, and some applied examples demonstrate their effectiveness.
This paper proposes a system optimization method for product designs incorporating discrete design variables, in which hierarchical product optimization methodologies are constructed based on decomposition of characteristics and/or extraction of simpler characteristics from original characteristics. The method is constructed to take advantage of hierarchical optimization procedures, enabling the incorporation of discrete design variables. The proposed method can be applied to machine product designs that include discrete design variables such as material types, machining methods, standard material forms, and specifications. The optimizations begin at the lowest levels of the hierarchical optimization structure and proceed to the higher levels. Discrete design variables are efficiently selected and optimized in the form of small suboptimization problems at the lowest hierarchical levels, and optimum solutions for the entire problem are ultimately obtained using conventional mathematical programming methods. Practical optimization procedures for machine product optimization problems that include several types of discrete design variables are constructed, and applied examples are provided to demonstrate their effectiveness.
In machine product designs, a variety of characteristics such as product performances, manufacturing cost, and robustness of characteristics are evaluated, and the need for improvements is increasingly stringent over time. Such characteristics almost always have interrelationships, and systematic evaluation and optimization must be performed to obtain preferable product design solutions. To conduct effective system optimization, the complex interrelationships among characteristics that are included, and sometimes hidden, in the optimization problem, must be understood and dealt with. To construct optimization methodologies for such problems, these relationships must be clarified, and the characteristics simplified. Simplifying the characteristics makes the essence of the optimization problem clearer, and facilitates examining the interrelationships among the simplified characteristics during the optimization process. Based on the simplified characteristics, “priority relationships”, i.e., relationships among simplified characteristics that will be optimized first, and “conflicting relationships”, i.e., tradeoff relationships that will be simultaneously optimized, are obtained. Hierarchical optimization procedures develop naturally as the relationships among the simplified characteristics are clarified. This paper focuses on the priority relationships of characteristics in system optimization procedures. General and specific rules concerning priority relationships are presented, and these form the basis for the constructed optimization procedures. An applied example of a machine tool product design is presented to demonstrate the effectiveness of the proposed methodology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.