目的 为提高机械产品表面涂装质感设计的效率，对机械产品表面涂装质感设计方法进行深入研究。方法 基于感性工学，从理论层面提出机械产品质感设计方法、流程及其关键技术。并通过搜集典型质感样本、典型质感视觉意象词汇，进行视觉意象感性实验，从而获取典型质感样本和典型质感视觉意象词汇的相关隶属度。利用BP神经网络，实现机械产品表面涂装“视觉意象—质感参数”关联模型的构建。结果 以数码印花机为例，当设定质感参数为“涂层粗糙度0.2、涂层折射指数1.18、涂层厚度1.2”时，质感视觉意象值为“安全的3.132、简约的2.657、美观的3.089、稳定的2.756、坚固的2.842”，该结果表明，“视觉意象—质感参数”关联模型具有较好的主观趋势，可以满足用户的视觉意象需求。结论 所提出的机械产品表面涂装质感设计方法具有较好的可操作性和科学性，可为机械产品表面涂装质感的设计提供参考。

In order to improve the efficiency of the mechanical product surface coating texture design, the design method based on kansei engineering was studied in depth. Firstly, based on kansei engineering, the texture design method and process and key technology of mechanical product surface coating from the theoretical level was proposed. Secondly, for key technology, typical texture samples and typical texture visual imagery vocabulary were collected, conduct visual imagery sensitivity experiments to obtain the relative membership degree of typical texture samples and typical visual imagery vocabulary; Using BP neural network, the “visual imagery-texture parameter” correlation model of mechanical product surface coating was constructed; Finally, take the digital printing machine for example, when the texture parameters were “coating roughness 0.2, coating refractive index 1.18, coating thickness 1.2”, the texture visual imagery values were “safe 3.132, simple 2.657, beautiful 3.089, stable 2.756, solid 2.842”. The results show that the “visual imagery- texture parameter” correlation model had a better subjective trend and can meet the visual imagery needs of users. The method of mechanical products surface coating texture design was maneuverable and scientific, which can provide reference for the design of mechanical product surface coating texture.