## 2 Related Work
The modelling of specific data and knowledge in each activity is not included. * Worksheet model [27]: this model was presented to simplify the guidelines of design for AM for novice and intermittent users. In the model, the existing guidelines of AM design are firstly summarised from a certain number of representative research articles. Based on the summarisation, a one-page visual worksheet of the guidelines of design for AM is developed. The effectiveness of the worksheet is demonstrated via several test experiments. The worksheet model can help designers evaluate the potential quality of a component manufactured by AM processes and indirectly recommend feasible methods to redesign it. Its direct benefit is to filter out bad designs before manufacturing, which reduces the time spent on manufacturing and redesign. However, the model is based on the samples provided by a single source, which could limit its potential performance. * Formal rule model [28]: this model was constructed to represent the design rules with modularity for AM. In the model, the fundamental relations among geometry, process, and material are first decomposed into reusable modules. Then, the modules are described using if-then rules. On the basis of the rules, parts are specialised to represent the process-related parameters for different AM processes. The formal rule model provides consistent and repeatable interpretations of an AM design guideline in different design and process conditions. It can also be used to explain the design principles that are independent of process to those users who are not familiar with AM technologies. Since the core of the model is to reconfigure the existing design rules, rather than to develop new rules, the basic AM principles can be preserved, and meanwhile the customisation and explicit representation of AM design rules can be implemented. * Bayesian network model [29]: this model was established to fill the knowledge gap between designers and AM technologies. It is hierarchically organised and consists of an overview layer and a detailed information layer. Different types of nodes and their causal relationships in each layer are characterised by Bayesian networks. A knowledge management system for supporting AM was then developed. This system can help designers understand the capabilities of AM processes and form appropriate design solutions at the design stage, which reduces the uncertainty of AM processes to some extent. * Category theory model [30]: this model was developed to formalise general knowledge in design and process control for powder bed fusion. In the model, a collection of guidelines and rules for these two activities are encapsulated and represented using categorical structures. The category theory model provides a framework to captured, accessed, and interrogated the structured knowledge. However, it has not yet included the representation of specific data in the two activities. A summarisation of the models above at the aspects of published year, representation method, represented specific AM knowledge, and targeted AM process is provided in Table 1. In addition to these models, the use of ontologies in AM knowledge representation has been gained importance and popularity within academia during the past two decades. Many ontologies [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52] have been presented in this period. A summarisation of these ontologies at the aspects of published year, representation languages, represented specific AM knowledge, and targeted AM process is provided in Table 2.  
As can be seen from Tables 1 and 2, each model/ontology has its specific usage in AM knowledge representation. Some models/ontologies are targeted at general AM processes, while each of other models/ontologies is constructed for one specific AM process, including direct material deposition, LPBF, powder bed fusion, lithography-based ceramic manufacturing, or wire arc additive manufacturing.