Abstract
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.
Highlights
The capability of metal powder bed fusion (MPBF) to produce complex structural components in a wide range of materials has the potential to remove traditional manufacturing constraints from product design
It will be desirable to create a digital twin of the entire Metal powder bed fusion (MPBF) process chain, whereby data is passed from one process simulation to the models are updated according to machine and inspection data from physical sensors, and components and production plans are updated and optimised via data analytics and artificial intelligence
In the digital twin and the manufacturing process chain, data needs to be transferred between different models that may have been created in different software packages
Summary
The capability of metal powder bed fusion (MPBF) to produce complex structural components in a wide range of materials has the potential to remove traditional manufacturing constraints from product design. It will be desirable to create a digital twin of the entire MPBF process chain, whereby data is passed from one process simulation to the models are updated according to machine and inspection data from physical sensors, and components and production plans are updated and optimised via data analytics and artificial intelligence. This would enable designs and processes to be optimised for a particular part with less reliance on costly and time-consuming physical trials, increasing productivity and reducing the barriers to new product introduction [3]. This allows the maturity of the various modelling methods and data transfer technologies to be assessed through the use of Technology Readiness Levels (TRLs) so that roadmaps of their predicted development can be generated in the near future
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