In the present work, multiple forming tests were conducted under different forming conditions by Single Point Incremental Forming (SPIF). In which surface roughness, arithmetical mean roughness (Ra) and the ten-point mean roughness (Rz) of AlMn1Mg1 sheet were experimentally measured. Also, an Artificial Neural Network (ANN) was used to predict the (Ra) and (Rz) by adopting the data collected from 108 components that were formed by SPIF. Forming tool characteristics played a key role in all the predictions and their effect on the final product surface roughness. In the aim to explore the proper materials and geometry of forming tools, different ANN structures, different training, and transfer functions have been applied to predict (Ra) and (Rz) as an output argument. Furthermore, Support Vector Regression (SVR) with different kernel types have been used for prediction, together with Gradient Boosting regression to sort the effective parameters on the surface roughness. The input arguments were tool materials, tool shape, tool end/corner radius, and tool surface roughness (Ra and Rz). The actual data subjected to a fit regression model to generate prediction equations of Ra and Rz. The results showed that ANN with one output gives the best R-Square (R²). Levenberg-Marquardt backpropagation (Trainlm) training function recorded the highest value of R², 0.9628 for prediction Ra using Softmax transfer function whereas 0.9972 for Rz by Log- Sigmoid transfer function. Furthermore, tool materials, together with tool surface (Ra), are playing a significant importance role, affecting the sheet surface roughness (Ra). Whereas tool roughness Rz was the critical parameter effected on the Rz of the product. Also, there was a significant positive effect of tool geometry on the sheet surface roughness.

在当前的工作中，通过单点增量成型（SPIF）在不同的成型条件下进行了多次成型测试。通过实验测量了AlMn1Mg1薄板的表面粗糙度，算术平均粗糙度（Ra）和十点平均粗糙度（Rz）。此外，人工神经网络（ANN）通过采用从SPIF形成的108个组件中收集的数据来预测（Ra）和（Rz）。成形工具的特性在所有预测及其对最终产品表面粗糙度的影响中起着关键作用。为了探索成形工具的合适材料和几何形状，已将不同的人工神经网络结构，不同的训练和传递函数应用于预测（Ra）和（Rz）作为输出参数。此外，具有不同内核类型的支持向量回归（SVR）已用于预测，以及梯度提升回归来对表面粗糙度上的有效参数进行排序。输入参数是工具材料，工具形状，工具端/角半径和工具表面粗糙度（Ra和Rz）。对实际数据进行拟合回归模型以生成Ra和Rz的预测方程。结果表明，具有一个输出的ANN给出了最佳R平方（R²）。Levenberg-Marquardt反向传播（Trainlm）训练函数使用Softmax传递函数记录了最高的R ²值，对于Ra预测值为0.9628，而通过Log-Sigmoid传递函数记录的Rz为0.9972。此外，工具材料与工具表面（Ra）一起起着重要的作用，影响板材的表面粗糙度（Ra）。而刀具粗糙度Rz是影响产品Rz的关键参数。而且，刀具几何形状对板材表面粗糙度有明显的积极影响。