## 2 Related Work  
An ideal model for AM knowledge representation is preferably a standardised model for practical applications [21]. During the past few decades, many standardised models for data representation in AM have been developed [22], but there has not yet been a standardised model for knowledge representation in AM. To fill this gap, many models have been presented within academia, which can be divided into the following categories based on the knowledge representation methods used in them:  
* Graph model [23]: this model was presented to formulate the design guidelines for AM. In the model, a set of design guidelines for AM are first defined in a graph form based on relevant literature. Then, the 3D model of a component is decomposed into a group of constitutive features. These features and the relationships between them are identified and represented using graphs. After that, the defined design guidelines are leveraged to revise the offending features to alleviate the manufacturability issue. Finally, the best design for component build is obtained via repeating the first three steps iteratively. The approach can be used by those designers who find it difficult to integrate all the constraints into a unified AM design process. However, it improves the AM design only on the basis of manufacturability. Mechanical strength is not considered in the improvement. * Finite state automata model [24]: this model was constructed to represent AM design knowledge to support personalised AM. In the model, a formal design process structure combining design for personalisation and design for AM is first established. Then, the artifact-user interactions are formally represented using a finite state automata based on the established structure. Through applying the affordance, effectivity, and preference properties, the artifact properties related to behaviours and preferences of users are systemically linked to design requirements of AM artifacts. The finite state automata model provides a set of formal representations that capture design requirements to improve personalisation level of AM while maintaining the freedom of AM design. However, it can only be applied to the stages of conceptual design and preliminary design and has not yet been included for consideration of process planning. * Network model [25]: this model was developed to simulate AM processes. In the model, the system of direct material deposition process is taken as an illustrative case study. Networks are first used to visualise the general view of the system functions and the causal relationships between system variables. Then, a set of causal graphs between governing dimensionless products are defined to simplify the causal graphs between system variables. Through the simplification, the causal relationships between system variables are directly extracted before carrying out experiments. The network model provides a feasible way to simulate the direct material deposition process. However, it still needs to be further extended when it is utilised to simulate other AM processes. * Function modelling language model [26]: this model was established to structurally represent the process-related data and knowledge in LPBF using a function modelling language and align them with specific sub-processes of powder bed fusion. It classifies an LPBF product realisation process into six activities, which are product modelling, product model tessellation, process planning, monitoring and control, post process, and quality inspection. On the basis of such classification, the activities process planning and monitoring and control and the data and knowledge associated with them are modelled. The function modelling language model provides a way to understand the organisation of LPBF product realisation process activities and sub-activities and the data flows among them. It also provides a common terminology and new process knowledge for LPBF data management. However, the model is just a conceptual model of the activities.