# Study on Nanosecond Laser Ablation of 40 Cr13 Die Steel Based on ANOVA and BP Neural Network
## 1 Introduction  
In recent years, nanosecond lasers have been widely used in the manufacturing of surface microstructures [1, 2]. Laser ablation processing has the advantages of being applicable to a wide range of materials, has low processing stress, and has high processing efficiency. Therefore, it has become a feasible alternative to conventional methods for fabricating complex microstructures on hard-to-machine materials, such as die steels, cemented carbides, and ceramics [3, 4]. Laser ablation is a complex material removal process, in addition to material properties and laser characteristics, process parameters including laser power, pulse frequency, and scan speed are the key factors that determine the ablation quality [5, 6]. Many previous theoretical analyses and experiments have shown that selecting a suitable combination of process parameters can significantly improve the laser processing quality [7, 8]. However, it is difficult to optimise the process parameters through simple repeated experiments, owing to the large number of process parameter combinations [9]. The artificial neural network (ANN) is an algorithm model with adaptive and organisational capabilities that can imitate the behavioural characteristics of animal neural networks for distributed and parallel information processing [10]. ANN has been successfully applied in the industrial field to realize the prediction and improvement of device accuracy [11, 12]. Another important application of it is to assist the selection of process parameters in laser processing, which cannot be achieved by traditional technical methods [13]. [PERSON] et al. used an ANN to optimise the parameters of non-vertical laserprocessing, and they showed that the \"quality point\" calculated using an ANN is close to the actual cutting result [14]. [PERSON] et al. studied the effects of scan speed, pulse intensity, and pulse frequency on the depth, width, surface roughness, and material removal rate(MRR) for the processing of microgrooves in the laser milling process: they used k-nearest neighbours, an ANN, and a decision tree to build predictive models [15]. [PERSON] et al. used the response surface method, an ANN, and a genetic algorithm to optimise the dimensional accuracy of laser processing based on 32 sets of experimental measurement data. The results showed that the ANN prediction model based on the 5-8-8-3 architecture had the highest prediction accuracy [16]. [PERSON] et al. proposed a neural network model to predict the pulse energy level required to produce pits with a predetermined depth and diameter during laser micromachining [17]. From the above studies, it is apparent that ANNs can been successfully applied to the prediction of laser processing results. There are many applicable ANN algorithms, among which the BP neural network is currently the most successful [18; 19; 20]. Therefore, in this study, we used BP neural networks to predict laser ablation results of 40 Cr13 die steel. Because of its high mechanical strength and hardness, 40 Cr13 is considered a hard-to-machine steel. Nevertheless, microstructured 40 Cr13 has numerous applications in the dies industry. To the best of our knowledge, there are few scientific studies on laser ablation of this kind of die steel to obtain microstructures. Due to the variety of elements inside the material and its uneven texture, it is difficult to ensure the uniformity of the microstructure processing quality during the laser ablation process. The goal of our work is to bridge this knowledge gap and improve the processing of inhomogeneous materials. This study presents the first application of BP neural networks for the laser ablation results prediction of hard-to-process 40 Cr13 steel. Our primary aim was to present a systematic investigation of the effects of nanosecond laser ablation of 40 Cr13 die steel. This paper is organised as follows. Section 2 introduces the experimental materials and methods.