Metal powder bed fusion (MPBF) is not a standalone process, and other manufacturing technologies, such as heat treatment and surface finishing operations, are often required to achieve a high-quality component. To optimise each individual process for a given component, its progression through the full process chain must be considered and understood, which can be achieved through the use of validated models. This article aims to provide an overview of the various modelling techniques that can be utilised in the development of a digital twin for MPBF process chains, including methods for data transfer between physical and digital entities and uncertainty evaluation. An assessment of the current maturity of modelling techniques through the use of technology readiness levels is conducted to understand their maturity. Summary remarks highlighting the advantages and disadvantages in physics-based modelling techniques used in MPBF research domains (i.e. prediction of: powder distortion; temperature; material properties; distortion; residual stresses; as well as topology optimisation), post-processing (i.e. modelling of: machining; heat treatment; and surface engineering), and digital twins (i.e. simulation of manufacturing process chains; interoperability; and computational performance) are provided. Future perspectives for the challenges in these MPBF research domains are also discussed and summarised.
In the UK, the NDE community is making a coordinated effort to underpin and enable the full benefits of the large-scale trend towards comprehensive digitalization and automation of industrial processes and assets, frequently referred to as "Industry 4.0". Certain facets of what is now considered to be NDE 4.0 have been the subject of research for some time and have already gained industrial traction, while others are quite new, with unexplored potential and pitfalls. However, in these areas there is scope for learning from progress in fields outside of, but related to, NDE such as dimensional metrology. This paper reviews progress to date based on UK activities, considers some planned and potential research tasks in this domain, and highlights the major challenges the NDE community must tackle. In particular, as interoperability and data reuse are key features of Industry 4.0 practices, international and cross-domain efforts on data format standardization are needed. It is clear that, without the NDE community stepping up to the challenge, much of Industry 4.0 cannot be realized; yet if the NDE 4.0 vision is implemented comprehensively, NDE has the potential to become more capable, more valuable, and therefore more highly valued.
Recent advances in convolutional neural networks have shown promise for a wide range of engineering applications including production quality assessment. This has been enabled in part by the availability of open-source algorithms and techniques such as transfer learning. Despite these factors, deploying these algorithms in production environments remains a challenge. The research presents an architecture for the deployment of convolutional neural network algorithms for the quality assessment of additive manufacturing (AM) build processes. The first iteration of this architecture has been implemented in a preproduction powder bed fusion AM facility at the National Centre for Additive Manufacturing in the UK. By demonstrating the application of this architecture on data generated from in-process monitoring data, the study hopes to reduce barriers faced when taking machine learning models from a laboratory concept to a production facility. By reviewing the latency of predictions, the study highlights that the additional proposed architectures may be viable for processing live production data for detection and control of defects. Finally, the study proposes that AM build machine manufacturers could provide a service-oriented architecture to encourage the implementation of in-process correction strategies.
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