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In engineering design, surrogate models are often used instead of costly computer simulations. Typically, a single surrogate model is selected based on the previous experience. We observe, based on an analysis of the published literature, that fitting an ensemble of surrogates (EoS) based on cross-validation errors is more accurate but requires more computational time. In this paper, we propose a method to build an EoS that is both accurate and less computationally expensive. In the proposed method, the EoS is a weighted average surrogate of response surface models, kriging, and radial basis functions based on overall cross-validation error. We demonstrate that created EoS is accurate than individual surrogates even when fewer data points are used, so computationally efficient with relatively insensitive predictions. We demonstrate the use of an EoS using hot rod rolling as an example. Finally, we include a rule-based template which can be used for other problems with similar requirements, for example, the computational time, required accuracy, and the size of the data.

A material's design revolution is underway with a focus to design the material microstructure and processing paths to achieve certain performance requirements of products. A host of manufacturing processes are involved in producing a product. The processing carried out in each process influences its final properties. To couple the material processing-structure-property-performance (PSPP) spaces, models of specific manufacturing processes must be enhanced and integrated using multiscale modeling techniques (vertical integration) and then the input and output of the various manufacturing processes must be integrated to facilitate the flow of information from one process to another (horizontal integration). Together vertical and horizontal integration allows for the decision-based design exploration of the manufacturing process chain in an inverse manner to realize the end product. In this paper, we present an inverse method to achieve the integrated design exploration of materials, products, and manufacturing processes through the vertical and horizontal integration of models. The method is supported by the concept exploration framework (CEF) to systematically explore design alternatives and generate satisficing design solutions. The efficacy of the method is illustrated for a hot rod rolling (HRR) and cooling process chain problem by exploring the processing paths and microstructure in an inverse manner to produce a rod with specific mechanical properties. The proposed method and the exploration framework are generic and support the integrated decision-based design exploration of a process chain to realize an end product by tailoring material microstructures and processing paths.

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