ASPIRE – Aerospace Special Processes Intelligence and Re-skilling of Employees

ASPIRE项目创新专注于通过将我们的MVP视觉智能应用程序与我们的AeroDNA生产控制解决方案进行集成和试点，来满足航空航天特殊工艺公司的可持续生产力需求。组合后的解决方案将提供提取有关人类表现的实时数据的能力，以：*将熟练的特殊过程操作员的人类行动数字化，以提供自动化时间，动作和错误捕获数据*提醒操作员在AeroDNA中捕获的特殊工艺缺陷和不合规与标准化工艺路线，以便立即进行补救*提取并分割特殊加工厂车间中的人工操作视频，以通过数字化知识传授保留最佳实践。*远程访问，由分散的工艺工程团队实时可视化特殊工艺操作*捕获有关特殊工艺人工操作的前所未有的商业智能，这些信息将提供给AeroDNA调度解决方案，以优化电镀槽和热处理炉的利用率，从而显著影响能源消耗和可持续发展。该方法利用-ART AI计算机视觉识别操作员在特殊工艺上的操作，以确保满足严格的航空航天质量标准。卷积神经网络的使用为模式识别提供了更好的普适性，并且与传统的自动光学检测相比，在检测异常方面表现得更好。然后，对产品执行的操作的数字记录可以存储为质量管理的证据，也可以用于培训新的特殊工艺操作员。这一记录对于数字化连接工厂很有用，这样就可以通过供应链追踪缺陷，并用来证明质量标准。

ASPIRE project innovation focuses on responding to the sustainable productivity need of aerospace special-process houses through the integration and piloting of our MVP vision intelligence application with our AeroDNA production-control solution. The combined solution will deliver the capability to extract real-time data on human performance to:* digitise human action from skilled special-process operators to provide automated time, motion and error capture data* alert operators about special-process defects and non-compliance vs. standardised process routes captured in AeroDNA so they can be remedied immediately* extract and segment video of human actions on the special-process house shop-floor to retain best-practice through digitalised knowledge transfer.* remote access to visualise special-process operations in real-time by distributed process engineering teams* capture unprecedented business intelligence about special-process human operations which will feed into the AeroDNA scheduling solution to optimise electroplating vats and heat treatment oven utilisation which significantly impacts energy consumption and sustainability.The approach leverages state-of-the-art AI computer vision to recognise operator actions on special processes to ensure that stringent aerospace quality standards are met. The use of convolutional neural networks offers more generalisability for pattern recognition and performs better for detecting anomalies compared to traditional automated optical inspection. A digital record of the actions that have been performed on a product can then be stored as a proof of quality management as well as be used to train new special-process operators. This record is useful for digitally connecting factories so that defects can be traced through supply-chains and used to prove quality standards.