Abstract
The interest in additive manufacturing (AM) processes is constantly increasing due to the many advantages they offer. To this end, a variety of modelling techniques for the plethora of the AM mechanisms has been proposed. However, the process modelling complexity, a term that can be used in order to define the level of detail of the simulations, has not been clearly addressed so far. In particular, one important aspect that is common in all the AM processes is the movement of the head, which directly affects part quality and build time. The knowledge of the entire progression of the phenomenon is a key aspect for the optimization of the path as well as the speed evolution in time of the head. In this study, a metamodeling framework for AM is presented, aiming to increase the practicality of simulations that investigate the effect of the movement of the head on part quality. The existing AM process groups have been classified based on three parameters/axes: temperature of the process, complexity, and part size, where the complexity has been modelled using a dedicated heuristic metric, based on entropy. To achieve this, a discretized version of the processes implicated variables has been developed, introducing three types of variable: process parameters, key modeling variables and performance indicators. This can lead to an enhanced roadmap for the significance of the variables and the interpretation and use of the various models. The utilized spectrum of AM processes is discussed with respect to the modelling types, namely theoretical/computational and experimental/empirical.
Highlights
These results indicate the complexity of the process mechanism regarding the impact of the process-parameter selections on the most representative quality-related key perforindicators (KPIs)
A heuristic metric for the complexity of a process has been suggested and applied in the case of various additive manufacturing (AM) processes. This metric has been proved to be quite useful towards forming a metamodeling framework for processes that includes the machine aspect on attributes/performance indicators
All the goals towards successful AM modeling are summarized in the following points and can be facilitated through the current framework:
Summary
In additive manufacturing (AM) processes, parts are created in a layer-by-layer fashion by selectively fusing the material of the current layer on that of the previous one, based on information provided by 3D model data [1]. In 1981, Dr Hideo Kodama came up with the rapid prototyping idea, while in 1984, Chuck Hull filed his patent for his stereolithography apparatus [2]. AM differs from rapid prototyping [3] in that it aims to manufacture end-user parts, rather than just prototypes [4]; its first application took place on 1999 for the creation of a scaffold for a human bladder [5]. In 2006, the first metal-AM application was made commercially available in the form of selective laser sintering. One of the most important advantages of AM is the manufacturing freedom it offers [9], rendering part complexity nearly cost free
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