Friction stir processing is one of the solid-state processes which can be used to modify the structure and properties of alloys. In addition, it has become one of the most promising techniques for the preparation of the surface layer composites. To pursue cost savings and a time-efficient design, the mathematical model and optimization of the process can represent a valid choice for engineers. Friction stir processing was employed to generate an Al 6061/Al₂O₃-TiB₂ hybrid composite layer, and mechanical properties such as the hardness and wear behavior were also measured. The relationship between the hardness and wear behavior, process parameters of friction stir processing were evaluated using an artificial neural network and response surface methodology. The rotational speed (1500–1800 rpm), traverse speeds (25, 50, 100 mm/min), and the number of passes (1–4) with constant axial force (2.61 kN) were used as the input, while the hardness and weight loss values were the output. Experimentally, the results showed that the process parameters have significant effect on hardness and wear behavior of Al 6061/Al₂O₃-TiB₂. In addition, the developed artificial neural network and response surface methodology models can be employed as alternative methods to compute the hardness and weight loss for given process parameters. The results of both models showed that the estimated values for the hardness and wear behavior of the processed zone had an error less than 0.60%, which indicated reliability, and an evaluation of the estimated values of both models and the experimental values confirmed that the artificial neural network is a better model than response surface methodology.

搅拌摩擦加工是一种可用于改变合金结构和性能的固态工艺。此外，它已成为制备表面层复合材料最有前途的技术之一。为了追求成本节约和时间高效的设计，过程的数学模型和优化可以代表工程师的有效选择。采用搅拌摩擦工艺生成Al 6061/Al ₂ O ₃ -TiB ₂还测量了混合复合层和机械性能，如硬度和磨损行为。使用人工神经网络和响应面方法评估硬度与磨损行为、搅拌摩擦加工的工艺参数之间的关系。旋转速度 (1500–1800 rpm)、横移速度 (25, 50, 100 mm/min) 和具有恒定轴向力 (2.61 kN) 的通过次数 (1–4) 作为输入，而硬度和重量损失值是输出。实验结果表明，工艺参数对Al 6061/Al ₂ O ₃ -TiB _{2 的}硬度和磨损行为有显着影响。. 此外，开发的人工神经网络和响应面方法模型可用作计算给定工艺参数的硬度和重量损失的替代方法。两种模型的结果表明，加工区硬度和磨损行为的估计值误差小于0.60%，表明可靠性，并且对两种模型的估计值和实验值的评估证实了人工神经网络是比响应面方法更好的模型。