计算机科学
背景(考古学)
产品生命周期
产品设计说明书
产品(数学)
新产品开发
产品设计
产品工程
系统工程
工程类
几何学
数学
生物
业务
古生物学
营销
作者
Henning Voet,Max Altenhof,Max Ellerich,Robert Schmitt,Barbara Linke
出处
期刊:Journal of Manufacturing Science and Engineering-transactions of The Asme
[ASME International]
日期:2018-11-12
卷期号:141 (2)
被引量:24
摘要
Product improvement, usually through changes in design and functionality, is relying more and more on the continuous analysis of large amounts of data. Product data can come from many sources with varying effort in obtaining the data, e.g., condition monitoring and maintenance data. Intelligent products, also known as “product embedded information devices” (PEID), are already equipped with sensors and onboard computing capabilities and therefore able to generate valuable data such as the number of user interactions during the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product interaction automatically and utilize it as part of data-driven product improvement. Product engineers and designers will gain insights into the use phase and can derive design changes and quality improvements. The methodology guides the user through research on product use dimensions based on the principles of user-centered design (UCD). The findings are applied to define what usage elements, such as specific actions and context, need to be available from the use phase. During systems development, machine learning is suggested to fuse sensor data to efficiently capture the usage elements. After product deployment, use data are retrieved and analyzed to identify the improvement potential. This research is a first step on the long way to self-optimizing products.
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