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Companies, especially small and medium-sized enterprises, are struggling with the overwhelming requests to proceed with digitalisation and data sharing, contribute to sustainable development goals and remain resilient and competitive with their limited resources. The twin transition is driving both digital and green transitions in the manufacturing industry at the same time. Further, the European Union (EU) is pushing towards a data economy, data spaces, green transition, ecodesign and digital product passport (DPP). Agile and interoperable DPP concepts and implementations are eagerly sought by the manufacturing industry. Asset Administration Shell (AAS) is a standardised Industry 4.0 technology used to gather data on assets and will help companies deploy the DPP in their business and facilitate circular innovation and design. [ABSTRACT FROM AUTHOR]

Predictive maintenance is widely agreed upon as the superior method for maintenance scheduling over time/condition-based choices. This is where the remaining life cycles of each part/component as well as the system in its entirety are forecast, informing the optimal period to perform a maintenance action. In current literature, this is often done using a value or set of values representing the degradation of the components or system; such a value is called a Health Index (HI), built from a function of the sensors on the machine(s) to be monitored. This paper will introduce and expand upon methods to utilise a flexible and domain-defined (HI), derived from the use of a Digital Risk Twin (DRT). The twin is used to capture domain knowledge directly from subject matter experts to determine the optimal HI at both the component stages in addition to one's representative of the system, thus defining at various levels of indenture. The HI is coupled with state-of-the-art deep learning and machine learning techniques to confidently forecast trends observed in a system or its individual parts to enable prognostics with a high calibre of predictive integrity. Syndrome Diagnostics (SD), a tool to incorporate the research and prototypical work laid out will also be presented. [ABSTRACT FROM AUTHOR]

This study aims to establish an intelligent Cyber-Physical Production System (CPPS) with closed-loop control and self-optimization of process parameters. Empowered by a deep learning (DL) agent, the digital twin (DT) simulation can focus on the most significant process routes and eliminate 92.53% of unnecessary exhaustive iterations. On the other hand, DT can provide the most accurate simulation result for fast convergence to the optimal parameters of DL and no need to waiting for the result after the process completed. Though collaborative and autonomous interactions between DT and DL have been proposed recently, the practical integration of DT-DL and CPPS in the Electronic Manufacturing Service (EMS) industry has yet to be validated in official publications. With such novelty approach, the improvement of the production throughput (T/P) and rolled throughput yield (RTY) is 7.513% and 0.86%, respectively, which is on top of the contributions from traditional industrial engineering (IE) productivity tools. [ABSTRACT FROM AUTHOR]

Modern manufacturing technologies such as Additive Manufacturing are a vital pillar of Industry 4.0 and Smart Manufacturing systems. One of the most promising AM techniques is Directed Energy Deposition (DED) which uses a thermal source to generate a melt pool on a substrate into which metal powder is injected. Laser Cladding (LC), a DED additive process, is widely used in machine parts repair and functional coating due to its advantages, such as the low heat input into the substrate. Compared to conventional welding, this results in less induced stress, lower dilution rate, small heat-affected zone, and good metallurgical bonding between the coating and the substrate. The LC technology is not as robust or standardized. The part quality, mechanical properties, and microstructure of Laser- Cladded parts are neither as commercially predictable nor controllable. This research aims to develop Deep Learning models that predict and classify the LC process for LC coating and single and interfering-bead samples. The ML models will detect faulty process input parameters and predict the quality. This detection and prediction take place in real time, which is vital in producing the additive process's data model component of a Digital Twin (DT). Digital imaging of a coaxial CMOS camera and melted pool temperature measurements using a LASCON LPC04 pyrometer are being used as in-situ sensory devices. A plethora of articles have limited their ML models to individual combinations of substrate and powder and focused on the three main process parameters (laser power, laser beam scanning speed, and Powder feeding rate). This research project expands the scope to include a different substrate and powder combinations, utilize additional parameters, and exploit different powder/substrate combinations. The Inputs will be gathered and manipulated in a controlled environment to predict both good-quality and poor-quality LC. The process will take place by building geometries selected strategically to associate uncontrollable parameters, especially the temperature of the substrate, in the prediction model. The online optical images and the offline microscopic analysis of the sectioned bead are mapped together. In a later stage, the ML can potentially integrate into a control system and digital twin models. [ABSTRACT FROM AUTHOR]