Magnesium (Mg) alloy sheets have attracted considerable attention as one of the most promising lightweight structural materials for weight reduction–oriented industries owing to their excellent properties compared with conventional materials. However, basal-textured Mg alloy sheets exhibit extremely inferior formability at room temperature due to their hexagonal close-packed structure and the limited number of active slip systems. Herein, an innovative warm incremental sheet forming assisted with oil bath heating approach to form difficult-to-form metal materials is proposed. To examine the forming quality of the approach, using springback as an evaluation index, a sequence of tests was conducted while forming AZ31B Mg alloy sheets according to a central composite design including response surface methodology and analysis of variance. The results indicated the approach was able to form Mg alloy sheets with great feasibility. The forming temperature (A), forming angle (B), step depth (C), and sheet thickness (D) are substantial factors that affect the springback, whereas the tool diameter (E) has a much less influential role compared with the individual effects of the other parameters, and the reasons for these results are explained. All the remaining interactive terms are substantial interactive factors except the AB, AE, BE, and CD terms, and a quadratic regression model gives the best fit with a 95% confidence level for springback. It was also indicated from the optimization results that to achieve a minimum springback value, the 166.3 °C forming temperature, 50.4° forming angle, 0.22-mm step depth, 1.18-mm sheet thickness, and 11.5-mm tool diameter should be selected.

镁（Mg）合金薄板由于其与常规材料相比的优异性能，已成为面向减重行业的最有希望的轻质结构材料之一，引起了广泛的关注。但是，由于基底结构化的Mg合金板的六角形密排结构和有效滑移系统的数量有限，因此它们在室温下的可成形性极差。在本文中，提出了一种创新的热增量板材成形方法，该方法借助油浴加热方法形成难于形成的金属材料。为了检查该方法的成形质量，以回弹为评估指标，根据包括响应面方法和方差分析在内的中心复合设计，在形成AZ31B镁合金薄板时进行了一系列测试。结果表明该方法具有很大的可行性。成形温度（A），成形角度（B），台阶深度（C）和板厚（D）是影响回弹的重要因素，而刀具直径（E）的影响力要远小于单个解释了其他参数的影响以及导致这些结果的原因。除AB，AE，BE和CD术语外，所有其余的交互式术语都是重要的交互式因素，并且二次回归模型以95％的置信度回弹是最合适的。从优化结果还可以看出，要获得最小的回弹值，应选择166.3°C的成型温度，50.4°的成型角，0.22 mm的台阶深度，1.18 mm的板厚和11.5 mm的刀具直径。