As readers of this journal know, surface enhancement processes such as shot peening for improving fatigue life in metallic components have been around for a long time. Over the years, these processes have become more sophisticated with the introduction of new technologies to control various aspects of the process. These new technologies have helped to reduce process variability and improve product performance. The level of sophistication and the introduction of new technologies continue to improve product capability and the ability to tailor the application of the process for specific needs.

A review of the literature shows an important technology that is developing rapidly and will enhance the ability to optimize surface enhancement processes for improving fatigue resistance in metallic components. This technology is analytical modeling for surface engineering, incorporating both the application of the processes and fatigue life prediction based on the imparted residual stresses in the component. While surface engineering modeling is relatively new compared to some of the surface enhancement processes it addresses such as shot peening, these models are being used today to establish the processing conditions for shot peening and the coverage needed on a given component to achieve one or more specific requirements. These models continue to grow in their ability to accurately prescribe optimum processing conditions.

The development and application of analytical modeling is not limited to just the prediction of the benefits for shot peening. Models are also being used and refined, or considered, for other surface enhancement processes such as laser shock peening, water jet, ultrasonic peening, deep rolling and low plasticity burnishing to address both the application of these processes and to identify what processing parameters and patterns are needed to arrive at a desired benefit for the component. These analytical models are also being extended and refined to predict the fatigue benefit of the process on the component and allow for tradeoff studies between different process areas and conditions, as well as cost.

Some of these surface enhancement processes are currently being applied in combinations with each other to achieve specific benefits in a component that cannot be achieved by themselves. For example, shot peening is applied for broad area coverage on turbine engine compressor airfoils to provide a higher level of resistance to surface-initiated fatigue over the entire airfoil. Then, laser shock peening is being applied on these same airfoils in specific locations, such as the leading edge, to increase fatigue resistance to foreign object damage. Each process has specific purposes and attributes. One process cannot entirely replace the other because of their individual unique capabilities.

Today, these two processes, as well as the others, are for the most part modeled independently of each other. However, in the future, surface engineering demands from both performance and cost standpoints will require more from analytical modeling than to predict the benefits of each process independently. These models will need to be able to conduct trade-off studies between various surface enhancement processes, and also need to determine how to best select and combine two or more of the surface enhancement processes into a component to provide benefits that are not achievable using only one of these processes.

Achieving this goal will provide another dimension to surface engineering for fatigue resistance. The desired surface engineering models will be able to predict where the compressive residuals stresses need to be placed on the component, the required magnitude and depth of the compressive stresses, the location and magnitude of the compensating tensile stresses, and the interactions of these residual stresses with the various loading stresses in the component. Finally, they must ultimately provide a useful prediction of fatigue crack initiation and propagation behavior in the component resulting from different processing scenarios to enable informed decisions for their application.

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