# On the Impact of Additive Manufacturing Processes Complexity on Modelling
## 4 Discussion
### Connection to Modelling Procedure  
The modeling approach that corresponds to the principles of the proposed framework has to be in line with the basic concepts set by MBSE, i.e., aiming to bridge the gap between the different AM stakeholders. More specifically, such models promote the successful communication among the system development team, the design and production engineer, thus replacing the document-centric approach and minimizing the need for expensive and time-consuming experiments [83]. This is achieved by maintaining low computational costs and by introducing user-friendly metrics that reflect the impact of the decisions of the part designer, production engineer, tester, and user [21; 22]. The result is a holistic modeling approach--enabling the simulation of the entire process time--which can be used by all the stakeholders for the evaluation of their decision-alternatives toward increased part quality and lower risk of failed parts. Moreover, important aspects of the AM process can be considered (effect of the movement of the head on part quality [21; 22]) owing to the practical and close-to-the-process modeling approach. A more detailed description of the modeling approach will be presented below.  
Summarizing, the classification of Figure 9 highlights the impact of each of the two components of the head movement (i.e., path and speed) on part quality for the different AM process groups. The additional parameters that have been used for this classification, beyond the complexity, are temperature of the process, and final part size. The process temperature and the final part size, inspired from classical classification [1] are parameters that are self-explanatory and can be easily quantified. The complexity is indicated by the coupling of the process parameter (PP) and the key performance indicator (KPI) of each AM process group.  
It is noted that in thermal AM applications, the head path planning directly determines the profile of the thermal field of the part, which drives the development of thermal stresses and deformations: high temperature gradients lead to increased thermal stresses and deformations [84]. Due to the fact that the thermal phenomena that take place in AM processes are highly dynamic in time and space [27], the existing thermal models are disadvantaged by their very high computational cost in terms of time and memory  
Figure 9: Classification of AM process groups.  
requirements [17]. Toward the improvement of the practicality of modeling and simulations, the Stress Formation Tendency Index (SFTI) has been developed and used. SFTI encapsulates the intensity of the non-uniformity of the thermal field, which is responsible for the development of thermal stresses and deformations [84]. Therefore, conclusions concerning process-parameter and head path planning selections are drawn from the thermal field, which is significantly faster in its calculation than the thermally induced stresses and deformations [22].  
Regarding the investigation of the head speed on part quality in the cement-based AM process group, the empirical modeling approach would better be followed because it was best suited for the investigation of the KPIs and process parameters involved (as per the extensive literature review [18]). The effects of controlling the path width on the quality were investigated in the manner that the path width was controlled by changing the head speed, while maintaining the extrusion speed (material flow) constant [21]. This resulted in a change in the ratio of the aforementioned speeds which is the main factor of the changes in the width of the deposited path. The advantage of this approach is its higher precision and productivity because the response time of the head is faster than that of the extrusion-head system for large-scale cement-based AM applications [85].