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.

Reducing the manufacturing and marketing time of products by means of integrated simulation-based design and development of the material, product, and the associated manufacturing processes is the need of the hour for industry. This requires the design of materials to targeted performance goals through bottom-up and top-down modeling and simulation practices that enables handshakes between modelers and designers along the entire product realization process. Manufacturing a product involves a host of unit operations and the final properties of the manufactured product depends on the processing steps carried out at each of these unit operations. In order to effectively couple the material processing-structure-property-performance spaces, there needs to be an interplay of the systems-based design of materials with enhancement of models of various unit operations through multiscale modeling methodologies and integration of these models at different length scales (vertical integration). This ensures the flow of information from one unit operation to another thereby establishing the integration of manufacturing processes (horizontal integration). Together these types of integration will support the decision-based design of the manufacturing process chain so as to realize the end product. In this paper, we present a goal-oriented, inverse decision-based design method to achieve the vertical and horizontal integration of models for the hot rolling and cooling stages of the steel manufacturing process chain for the production of a rod with defined properties. The primary mathematical construct used for the method presented is the compromise Decision Support Problem (cDSP) supported by the proposed Concept Exploration Framework (CEF) to generate satisficing solutions under uncertainty. The efficacy of the method is illustrated by exploring the design space for the microstructure after cooling that satisfies the requirements identified by the end mechanical properties of the product. The design decisions made are then communicated in an inverse manner to carry out the design exploration of the cooling stage to identify the design set points for cooling that satisfies the new target microstructure requirements identified. Specific requirements such as managing the banded microstructure to minimize distortion in forged gear blanks are considered in the problem. The proposed method is generic and we plan to extend the work by carrying out the integrated decision-based design exploration of rolling and reheating stages that precede to realize the end product.

Comprehensive numerical studies have been carried out on a biology-inspired computational model of Rhinoceros beetle subjected to changes in flow physics during propelling at different angles of attack. Over a pool of choices, Rhinoceros beetle is selected for its superior ability to function in various media. Detailed analyses have been carried out using a three dimensional pressure based SST k-ω turbulence model with the biomimetic structure. Numerical simulations have been carried out using refined polyhedral mesh with different lateral and longitudinal tilts at a free stream velocity of 5 m/s. Different flow property contours are generated and each case is compared with various flying conditions of Beetle to find out the best aerodynamic performance for various practical applications. Endurance is appropriated in this paper, through the estimation of the maximum aerodynamic efficiency for different orientations of the beetle wings. Authors ascertained that for every longitudinal angle of attack, there exists a lateral angle of attack at which aerodynamic efficiency becomes high and beyond which efficiency drops. Authors comprehended that the insects possess an innate ability to fix its wings at this critical efficient angle of attack as it changes its longitudinal angle of attack step by step, when it tries to take off. For landing, however, this phenomenon reverses to identify the angle of attack at which the aerodynamic efficiency becomes low. For a particular longitudinal orientation, detailed breakdown flow-physics research is carried out to single out the most aerodynamically efficient lateral angle of attack and to understand the reason behind that particular orientation's successful performance in aerospace that can be mimicked for a Micro Aerial Vehicle (MAV). This study is a pointer toward for the design optimization of MAV for industrial applications. NomenclatureC L = lift coefficient C D = drag coefficient C p = pressure coefficient M = Mach number P = pressure Re = Reynolds number T = temperature t = time u,v,w = velocity magnitudes α la = lateral angle of attack α lo = longitudinal angle of attack γ = ratio of specific heat ω = specific dissipation rate * Graduate Research Trainee, Aeronautical Engineering; 2 μ = dynamic viscosity μ t = turbulent viscosity ρ = density σ = turbulent Prandtl number

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