# Machine Learning-Based Prediction and Optimisation System for Laser Shock Peening
## 1 Introduction  
Laser shock peening (LSP) is an advanced surface enhancement technique that has been used extensively in aerospace industries to improve the fatigue life of metallic alloys [1, 2]. In LSP, high-power laser pulses interact with the surface of metal parts, resulting in the generation of a high-temperature plasma. The pressure generated by the expansion of the plasma generates shock waves that propagate into the material causing plastic deformation, leading to the surface having a compressive residual stress of high magnitude and depth [3, 4]. From a structural integrity standpoint, this can be potentially beneficial as it can extend the life of engineering components [5, 6]. Recent developments of industrial systems and feasibility studies illustrating the application of LSP in an industrial environment make it an amenable process for use in the production line [7]. Importantly, the extent of improvement in the fatigue life from the LSP process relies on the magnitude of the induced residual stresses, hence the choice of peening parameters is critical. The distribution of the LSP-induced residual stresses can play a significant role in fatigue crack initiation and propagation. At present, the process parameters for a new application are often determined using a trial-and-error approach. Hence, thereare serious gaps in comprehending the evolution of residual stress field resulting from the plastic deformation and material hardening characteristics. These aspects represent a major challenge for inclusion of LSP in structural design, as it can be potentially expensive and time-consuming. Furthermore, the residual stress distribution resulting from LSP exhibits a complex response to the process parameters, such as the spot diameter, power density and number of layers [8; 9; 10]. However, a clear understanding of the effect of these individual parameters on the induced residual stress distribution is lacking and can vary with different laser systems. Considering all these factors, selection of optimal process parameters depending on the service condition can represent an arduous task in laser shock peening. Several analytical models of laser peening based on finite element modelling have been developed to predict the residual stress distribution and to optimise the process parameters in a range of materials [11; 12; 13; 14; 15; 16]. These models suffer from the drawback of being inherently complex and highly biased on the assumptions of the analyst and involve excessive computational cost and requirements. Experimental methods based on diffraction [17] and strain relaxation [18] are used for characterisation of the residual stress distribution and to validate simulation models. However, the diffraction-based methods can be expensive and difficult to perform due to logistical challenges and limitations to acquire beam time at large-scale facilities. On the contrary, methods based on strain relaxation can be performed in situ but are at least semi-destructive in nature. It is precisely in this context that machine learning techniques, namely artificial neural networks (ANNs) and genetic algorithms (GA), can be used as surrogate models within an integrated system for prediction and optimisation purposes using experimental data. ANNs are applied successfully in a range of applications, including the prediction of welding-induced residual stresses using data acquired from diverse residual stress measurement techniques [19; 20]. In the area of laser peening, the application of ANNs has been demonstrated to model the response of the laser peening process [21] and to predict the residual stress profile using finite element data [22]. The Taguchi multi-objective optimisation approach has been used for parameter design and optimisation of laser peening parameters with limited success [23; 24].