EVOLVABLE ASSEMBLY SYSTEMS - TOWARDS OPEN, ADAPTABLE AND CONTEXT-AWARE EQUIPMENT AND SYSTEMS

汽车、航空航天、制药和医疗等行业的最终产品组装是英国等高劳动力成本地区的关键生产过程。为了应对当前的挑战，制造商需要将当前的资本密集型装配线转变为能够对外部和内部变化做出反应，并能够自我修复、自我适应和重新配置的智能系统。这一需求是由以下因素决定的：(1)生产系统的快速增加和缩小规模的需求；(2)目前的装配系统缺乏对破坏性事件和需求波动的自主响应能力；(3)经济和社会对“制造即服务”的推动。因此，需要一种全新的方法来开发未来的装配系统，该系统能够不断发展，以响应产品要求和需求的变化，同时在极短的安装时间内结合低成本的维护，系统重新配置和新兴技术的能力升级。随着自动化水平和类型的变化，未来的装配系统也将需要不同类型的人类操作员参与混合决策、监控和系统适应。拟议的研究汇集了多学科和多部门的合作伙伴关系，借鉴了诺丁汉大学在多学科变革研究方面的良好记录，以及代表关键高价值制造公司的行业及其代表机构。该研究计划的目标是定义和验证可进化装配系统的愿景和支持架构、理论模型、方法和算法，作为开放、可适应、环境感知和成本有效生产的新平台。该研究计划将在适应性和成本效益制造方面提供新的范式转变，打破传统方法，并基于复杂的集体适应性制造系统中以下基础研究挑战的创新交织：产品-过程-系统进化；数据分析;知识建模;出现工程;开放制造。这些基本的“集体”支柱是一个新的极其灵活和可进化的制造基础设施，预计将为未来生产平台的自配置、自组织、自适应和演变提供新的见解。这些支柱将共同预示着行业应对和解决当前和未来社会重大挑战的能力改变游戏规则的战略，这些挑战与在英国保留和扩大制造业业务有关。该研究最终将通过提供可快速配置和优化的强大且兼容的制造系统来压缩产品生命周期，从而减少生产时间和程序切换。这将为新的、小型和独立的生产利益相关者带来更多的机会，特别是那些参与实现产品、工艺和装配系统共同进化的利益相关者。我们构建底层架构的方法，使用模拟和真实世界的数据来测试和填充模型，并与行业利益相关者密切合作，将确保可扩展和适应性强的方法在不同制造部门之间可转移。

Assembly of final products in sectors such as automotive, aerospace, pharmaceutical and medical industries is a key production process in high labour cost areas such as the UK. To respond to the current challenges manufacturers need to transform current capital-intensive assembly lines into smart systems that can react to external and internal changes and can self-heal, self-adapt and reconfigure. This need is dictated by: (1) demand for rapid ramp-up and downscale of production systems; (2) the fact that current assembly systems lack autonomous responsiveness to disruptive events and demand fluctuations; (3) an economics and societal drive towards 'manufacturing as a service'. Consequently, there is a need for a radically new approach towards development of future assembly systems able to continuously evolve to respond to changes in product requirements and demand with extremely short set-up times combined with low cost of maintenance, system reconfiguration and capability upgrade with emerging new technologies. As the level and type of automation changes, future assembly systems will also require a different type of engagement of human operators in hybrid decision-making, monitoring and system adaptation. The proposed research brings together a multidisciplinary and multi-sector partnership drawing upon skills from across the University of Nottingham with an established track record in multi-disciplinary transformative research, and industries representing key high value manufacturing companies together with their representative bodies. The goal of the research programme is to define and validate the vision and support architecture, theoretical models, methods and algorithms for Evolvable Assembly Systems as a new platform for open, adaptable, context-aware and cost effective production. The research programme will deliver a new paradigm shift in adaptable and cost effective manufacture that breaks with traditional approaches and is predicated on an innovative intertwining of the following foundational research challenges in complex collective adaptive manufacturing systems: Product-Process-System Evolution; Data Analytics; Knowledge Modelling; Emergence Engineering; and Open Manufacturing. These fundamentally 'collective', pillars for a new extremely flexible and evolvable manufacturing infrastructure are expected to shed new insights on the self-configuration, self-organisation, self-adaptation and evolution of future production platforms. Together the pillars will presage a game-changing strategy for industry's ability to respond and solve current and future societal grand challenges linked to retaining and expanding manufacturing operations in the UK.The research will ultimately enable a compressed product life cycle through the delivery of robust and compliant manufacturing systems that can be rapidly configured and optimised, thus reducing production ramp-up times and programme switchovers. This will lead to increased opportunities for new, small and independent production stakeholders, particularly those involved in the realisation of product, process and assembly system co-evolution. Our approach of building an underlying architecture, using simulated and real-world data to test and populate models, and working closely with industry stakeholders, will ensure scalable and adaptable approaches that will be transferable between different manufacturing sectors.