InTiFi - Industry 4 Technologies into Foundation Industries

英国大部分行业在采用工业4.0原则和智能技术方面面临的挑战是如何对这些原则和智能技术进行改造，以提高现有长寿命工厂的性能。该项目以两种截然不同的工艺为例解决了这一问题，这些工艺为对英国和出口供应链至关重要的行业提供专业的高价值金属产品。这两个行业合作伙伴都运营着英国独有的资本密集型技术。这些过程之间的共同主题是，如何将操作过程中使用相机/成像技术的过程数字可视化与智能处理和机器学习联系起来，以开发大大降低变异性和提高过程控制精度的在线控制系统。过程成像目前还不是这两个过程的一部分，但精确控制形状演变和实现这一点所需的过程参数对这两个过程都至关重要。工作包括过程表征，创建数字孪生兄弟，然后智能交互过程模型，将与额外的工厂传感器和仪器一起使用，以在工业试验中展示好处。在一个案例中，目标是充分改善过程控制和可预测性，使其能够进入一个全新的高价值市场(专业工程、航空航天和石油和天然气行业的镍高温合金)，初始目标是650万GB/a的营业额(约占全球市场的1%)和6-7%的预测年增长率，而不是目前服务的静态低利润率市场。这一变化是根本性的，只有目前的工艺控制标准，工厂担心如果遵循传统的发展道路，设备会有严重损坏的风险。第二个部门是专门用于陆上和海上应用的大型管道。业务竞争激烈，但全球增长强劲，通常通过竞标大型项目来赢得。成功包括在形状控制方面展示高精度的能力，以及适应新要求的敏捷性。与智能控制相关联的数字过程成像将带来主要的竞争优势。次要优势将来自改进的控制，从而带来产量和能源效益、减少浪费和预测性维护的机会。虽然示范工厂位于金属部门，但方法和技术应该可以很容易地转移到其他过程，在这些过程中，数字成像可以与机器学习和智能过程控制相联系，也为技术交付合作伙伴提供了机会。例如，可能包括其他材料(塑料、陶瓷、玻璃、其他金属)的成形工艺，或形状和位置演变很重要的各种其他工艺。

The challenge for much of UK industry in adopting Industry 4.0 principles and smart technologies is how to retrofit these to improve performance of existing long life-time plant. The project addresses this using the example of two very different processes for forming of specialist high-value metal products for sectors vital to UK and export supply chains. Both industrial partners operate capital intensive technologies unique in the UK.The common theme between these processes is the challenge to link digital visualisation of the process using camera/imaging technologies during operations with intelligent processing and machine learning to develop on-line control systems with substantially reduced variability and increased accuracy in process control. Process imaging is not currently part of either process, yet accurate control of shape evolution and the process parameters needed to achieve this are vital to both. Work includes process characterisation, creation of digital twins and then intelligent interactive process models which will be used with additional plant sensors and instrumentation to demonstrate benefits in industrial trials.In one case the aim is to improve process control and predictability sufficiently to allow entry into a completely new high value market (nickel superalloys for specialist engineering, aerospace and oil and gas sectors) with an initial aim of £6.5M pa turnover (~1% of global market) and 6-7% predicted annual growth as opposed to static low margin markets currently served. The change is radical and with only current standards of process control the plant fear risk of serious damage to equipment if following traditional development paths.The second sector is specialist large pipe for on and off-shore applications. Business is highly competitive but has strong global growth and is typically won by bidding for large projects. Success involves ability to demonstrate high precision on shape control together with agility in adapting to new requirements. Digital process imaging linked to intelligent control will bring major competitive advantages.Secondary advantages will arise from improved control leading to yield and energy benefits, reduced waste and opportunities in predictive maintenance.Whilst the demonstrator plants are in the metals sector, the approach and technologies should be readily transferable to other processes where digital imaging can be linked to machine learning and intelligent process control, also presenting opportunities for technical delivery partners. Examples might include forming processes in other materials (plastics, ceramics, glass, other metals) or diverse other processes where evolution of shape and position are important.