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where p i is the productivity parameter of product i, V i C is the total cost, and NI is the total number of units produced. An upper bound on the total number of units produced is designated in advance as N U (e.g., for pipe insulation, N U may be 10 ).
Combined with digital manufacturing techniques, product design, building and shaping may be integrated to enhance the production of fabrication products with minimal scrap (Bremer and Niemerink, 2010 ). In this paper, product design with performance optimization is investigated under the virtual fabrication paradigm, which includes virtual modeling, virtual prototyping, and virtual manufacturing. Based on this framework, a coupled process-performance optimization problem with three production modes is formulated and solved by using the evolutionary heuristic algorithm. The cost function and the performance metric are discussed for the case study of a virtual manufacturing environment.
The documentation set for this product strives to use bias-free language. For the purposes of this documentation set, bias-free is defined as language that does not imply discrimination based on age, disability, gender, racial identity, ethnic identity, sexual orientation, socioeconomic status, and intersectionality. Exceptions may be present in the documentation due to language that is hardcoded in the user interfaces of the product software, language used based on RFP documentation, or language that is used by a referenced third-party product. Learn more about how Cisco is using Inclusive Language.
The documentation set for this product strives to use bias-free language. For the purposes of this documentation set, bias-free is defined as language that does not imply discrimination based on age, disability, gender, racial identity, ethnic identity, sexual orientation, socioeconomic status, and intersectionality.
in the design process, a two-step simplex-based heuristic search approach was implemented for simultaneously optimizing the component geometrical and performance characteristics of a sheet formed product. A design-driven sensitivity analysis technique was developed to provide product response functions for surrogate model calibration, which were used to provide multi-objective design optimization for both buckling performance and acoustical quality of a sheet formed component. Convergence of the product performance optimization process was monitored to ensure that a unique set of optimum design parameters was obtained.
These include, among other aspects, shape function definition, algorithm for point-based models, surrogate models for performance prediction, function property of a surrogate model, sensitivity analysis, and the design of experiments. One of the key aspects of the objective function generation methodology is the selection of the surrogate model that will be used for surrogate model training and product performance predictions. The authors proposed an approach using radial basis function surrogates to represent the product response functions and then defined the surrogate models based on these functions. Convergence was achieved using a simplex-based heuristic search.
In the iterative design optimization process, all target values should be specified, and the associated constraints, such as manufacturability and formability limits, must be specified as well. The authors developed surrogate models based on radial basis functions for the process responses, which were used to develop objective functions by employing sensitivity analysis techniques. The surrogate models for product performance prediction were trained based on a training set generated using a Monte Carlo sampling technique. The analysis demonstrated that the surrogate models were able to predict the product performance characteristics with an accuracy of 95.4%.
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