Recently, many companies have increasingly emphasized product appearance aesthetics and emotional preference-based design to enhance the competitiveness and popularity of their products. Identifying the interaction between product appearance and customer preferences and mining design information from the interacting context play essential roles in affect-related design approaches. However, due to the complexity of the aesthetic and emotional perception process, obtaining such design information from the interacting context is challenging. This paper proposes an affective design approach based on the Kansei engineering (KE) method and a deep convolutional generative adversarial network (DCGAN) following the research trend of merging KE with computer science techniques in recent years. A case study of the social robot design is conducted to verify the effectiveness of this approach. Appearance aesthetic and emotional preference evaluations are adopted by the KE method first to identify the crucial features in two categories: (1) The physical features of the outer shape, head and color for aesthetics; (2) The emotional features of intelligent, interesting and pleasant for preference perceptions. Based on a manually created social robot image dataset, the DCGAN model is trained to automatically generate novel design images. Then several professional designers are involved to fine-tune the generated images in detail. The experimental results show that the newly designed social robots tend to obtain positive aesthetic and preference evaluations. Practically, such an affective design approach can help industrial design companies identify customers’ psychological requirements and support designers in creating new products innovatively and efficiently.

最近，许多公司越来越强调产品外观的美观性和基于情感偏好的设计，以提高其产品的竞争力和受欢迎度。识别产品外观和客户喜好之间的相互作用，并从相互作用的上下文中挖掘设计信息在与情感相关的设计方法中起着至关重要的作用。但是，由于美学和情感感知过程的复杂性，从交互上下文中获取此类设计信息具有挑战性。提出了一种基于关西工程（KE）方法和深度卷积生成对抗网络（DCGAN）的情感设计方法，以适应近年来将KE与计算机科学技术融合的研究趋势。进行了社交机器人设计的案例研究，以验证这种方法的有效性。KE方法首先采用外观美学和情感偏好评估来确定两类的关键特征：（1）美学的外形，头部和颜色的物理特征；（2）对偏好感知的聪明，有趣和愉快的情绪特征。基于手动创建的社交机器人图像数据集，对DCGAN模型进行了训练以自动生成新颖的设计图像。然后，请几个专业设计师对所生成的图像进行详细的微调。实验结果表明，新设计的社交机器人倾向于获得正面的审美和偏好评价。几乎，