报告题目：个性化产品设计及方法/ Design for Product Personalization
彭教授是加拿大曼尼托巴大学终身教授。研究领域包括产品智能设计和虚拟制造系统，长期致力于开放式架构的个性化产品设计，虚拟现实技术支持的产品设计和评价系统，工程系统的建模和优化，产品和制造系统的可持续发展技术等领域的研究，取得了一系列原创性成果。彭教授提出和发展了将操作工具的可接近性分析用于动态制造环境下产品装配和选择性拆卸过程。从而改进了传统的产品装拆和夹具设计方法和产品生命周期管理方式。将基于图象的三维建模方法用于虚拟环境的构建和真实物体的反求。提出和发展了基于虚拟现实技术的产品测试工具平台和开放式架构产品设计方法并将其应用到个性化产品设计和制造, 工业生产设备，康复器械, 医疗系统和组织工程中。作为负责人获得并主持多项加拿大科研基金和工业发展项目, 已发表国际期刊和国际会议论文200多篇。
A successful product in the market has to meet customer requirements. Product personalization is the trend of product innovation in the reaction to user needs. It is urgent to have effective methods of design for product personalization. In this talk, following key methods will be introduced for development of personalized products to meet individual user needs in the product lifespan.
1). Introducing open architecture: Product architecture is an interactive pattern of functional modules in the product to form a technical system, which allows components of a product to interact and correlate with each other to perform expected functions. Using open concept in the product architecture can form a bridge to link product developers and users for the product personalization.
2). Knowing user need: it is key in design for personalized product to know the user need. The product success in the market depends on its satisfaction to the user preference. Effective methods are required to collect and analyze data to know the user preference for product design.
3). Improving interaction of users and product: the best practice to know user needs is to involve the user in the product development. It is important to improve interactions between the way of users to use the product and the way of designers to respond to the users. Virtual reality and motion sensors are introduced to develop user interfaces for users to interact with product in the product design and evaluation.
4). Using AI-based decision-making: Artificial intelligent agent and machine learning methods provide key techniques to know user needs and to form design solutions based on the needs and available resources.
Examples will be presented for applications of these methods. Conclusions and perspectives will also be discussed.