Abstract
The shot peening process is a common procedure to enhance fatigue strength on load-bearing components in the metal processing environment. The determination of optimal process parameters is often carried out by costly practical experiments. An efficient method to predict the resulting residual stress profile using different parameters is finite element analysis. However, it is not possible to include all influencing factors of the materials’ physical behavior and the process conditions in a reasonable simulation. Therefore, data-driven models in combination with experimental data tend to generate a significant advantage for the accuracy of the resulting process model. For this reason, this paper describes the development of a grey-box model, using a two-dimensional geometry finite element modeling approach. Based on this model, a Python framework was developed, which is capable of predicting residual stresses for common shot peening scenarios. This white-box-based model serves as an initial state for the machine learning technique introduced in this work. The resulting algorithm is able to add input data from practical residual stress experiments by adapting the initial model, resulting in a steady increase of accuracy. To demonstrate the practical usage, a corresponding Graphical User Interface capable of recommending shot peening parameters based on user-required residual stresses was developed.
Highlights
For the design of dynamically load-bearing components, a certain safety risk is minimized by increasing the service life and improving its estimation
This paper describes the development of a residual stress prediction module for the shot peening process
Over 350 simulations with varying input parameters were automatically executed, resulting in residual stress profiles within common shot peening process ranges for two different sphere materials, 18 different velocities, and ten different sphere sizes
Summary
For the design of dynamically load-bearing components, a certain safety risk is minimized by increasing the service life and improving its estimation. A well-known example is deep rolling, a lowcost method that achieves a comparatively smooth surface, but is limited to elementary, usually rotation-symmetrical geometries [4] This technique is mainly used for components that require frictionless sliding, where good surface quality is critical for wear. Another alternative is laser shock peening, an efficient method to introduce compressive residual stresses at four times the depth of shot peening [5]. This is achieved by high-energy laser pulses that introduce a shock wave into the material that exceeds the material’s yield strength and causes localized deformation. The ball burnishing or roller burnishing method produces a smooth surface [5,7,8,9]
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