New Industrial Systems: Chatty Factories

第一次工业革命以水和蒸汽动力为基础。第二种是利用电力来支持大规模生产。第三个是利用电子和信息技术进行创新和自动化。如今，世界经济论坛（World Economic Forum）将第四次工业革命定义为“一场模糊了物理、数字和生物领域界限的技术融合”。这是基于第三次革命的快速发展，将数据、人工智能和机器人制造系统带到制造业的前沿。当前的制造系统的特点是大量相互关联的程序，其中包括许多离散的、高度专业化和人力密集型的活动，例如：消费者研究、概念设计、工程设计和原型制作，以及将机器人与工厂车间的人类工人结合起来的制造操作。当前制造系统的两个重要限制是：(a)无法根据实时消费者洞察（即产品如何使用及其对世界的“体验”）快速持续地改进产品设计；(b)无法根据从嵌入式产品传感器捕获的实时数据快速重新配置和重新培训工厂车间的人和机器人生产要素。例如，如果传感器数据显示产品需要根据其当前用途进行设计更改，我们如何在轮班之间更新制造说明并重新安排工厂车间，并告诉人类和机器人工人如何在几分钟内改变他们的职责？我们对未来制造工厂的愿景是，通过嵌入式传感器产生大量数据，拥抱互联网连接产品的快速增长，并将这些传统上离散的活动转变为一个无缝的过程，能够实时持续地改进产品。首先，考虑到对劳动力市场的潜在破坏，我们将开发新的基本理论，涉及产品、人类、机器人和工厂车间操作之间的学习和无缝沟通，以确保平等和协作实时学习。其次，我们将开发数据驱动系统，在工厂内外的所有操作之间提供可审计、安全和无缝的信息流，以促进基于产品嵌入式传感器和物联网（IoT）连接收集的数据的整个制造系统的实时适应和重新定位。这项研究将彻底打破现有的基于调查和市场研究的“消费者主权”模式，并通过对消费者行为的深入理解引入“使用主权”——根据产品的使用方式制造适合用途的产品。这将不是无中介的，而是由大规模的稳健、安全和可解释的数据分析来缓冲——与人类劳动的道德整合。设计师的角色将完全转变，他们将“嵌入生产”，在一个“健谈”的产品和动态发展的工厂车间的世界里。我们的方法将改变传统工厂实时重新配置的方式，采用先发方法进行实时重新配置，生产要素再培训（人类，机器人或两者协作）。从根本上说，我们将使用外科学，这是第一次将其应用于实际环境中，并将其与机器人技术的见解结合起来。外来学考虑外来的学习形式，并利用这些隐喻来发展超越人类学习的学习模式。与现有技术（如喷气发动机遥测技术）相比，我们的工作将允许i)重新设计以支持新的用途或使用模式；Ii)在观察替代用途的基础上生成新产品。

The first industrial revolution saw a transformation based on water and steam power. The second harnessed electricity to support mass production. The third used electronics and IT to innovate and automate. Now, the World Economic Forum defines the fourth industrial revolution as "a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres". This builds on the rapid growth of the third revolution to bring data, artificial intelligence and robotic fabrication systems to the forefront of the manufacturing industry. Current manufacturing systems are characterised by a multitude of interconnected procedures that include many discrete, highly specialised and human intensive activities, such as: consumer research, concept design, engineering design & prototyping, and manufacturing operations that combine robots with human workers on the factory floor. Two significant limitations of current manufacturing systems are (a) the inability to quickly and continuously refine product design in response to real-time consumer insights (i.e. how the product is being used and its 'experience' of the world) and (b) the inability to quickly reconfigure and reskill the human and robotic production elements on the factory floor in response to real-time data captured from embedded product sensors. For example - if sensor data suggests a product needs a design change based on its current use, how do we update the fabrication instructions and reshuffle the factory floor between shifts, and tell human and robot workers how to alter their duties within minutes?Our vision for the manufacturing factory of the future is to embrace the rapid growth of internet-connected products via embedded sensors producing massive volumes of data, and transform these traditionally discrete activities into one seamless process that is capable of real-time continuous product refinement. Firstly, mindful of the potential disruption to labour markets, we will develop new fundamental theory that relates to learning and seamless communication between products, humans, robots and factory floor operations - to ensure equality and collaborative real-time learning. Secondly, we will develop data-driven systems that provide an auditable, secure and seamless flow of information between all operations inside and outside the factory to facilitate real-time adaption and re-orientation of the entire manufacturing system based on data harvested via product-embedded sensors and Internet of Things (IoT) connectivity.The research will achieve a radical interruption of the existing 'consumer sovereignty' model based around surveys and market research - and introduce 'use sovereignty' via an embedded understanding of consumer behaviour - making products that are fit for purpose based on how they are used. This will not be unmediated but buffered by robust, secure and interpretable data analysis at scale - with ethical integration of human labour. Designers will have a completely transformed role being 'embedded in production' in a world of "chatty" products and a dynamically evolving factory floor.Our approach will transform the ways in which traditional factories are reconfigured in real time by adopting a first mover approach to real time reconfiguration, production element reskilling (human, robot or both collaboratively). Radically, we will use exopedagogy, the first time it has ever been applied in a practical setting and couple this with insights from robotics. Exopedagogy considers alien forms of learning and uses those metaphors to develop models of learning which go beyond human learning. In contrast to existing techniques such as jet engine telemetry that allow for optimisation tweaks around a clearly defined product, our work will allow for both i) redesigns to support new uses or usage patterns; ii) generation of new products based on observing alternative use.