The application of additive manufacturing technologies in the industry is growing fast. This leads to an increasing need for reliable modeling techniques in the field of additive manufacturing. A methodology is proposed to systematically assess the influence of process parameters on the final characteristics of additively manufactured parts. The current study aims at presenting a theoretical framework dedicated to the modeling of the additive manufacturing technology. More specifically, the framework is used in the context of the study to plan and optimize the experimental process to minimize the amount of experiments required to populate the model. The framework presented is based on the Dimensional Analysis Conceptual Modelling framework (DACM). DACM is an approach supporting the production of models. This approach is designing networks representing a system architecture and behavior using an approach sharing similarities with neural networks. Based on the proposed approach, it is possible to detect where supplementary experimental data have to be collected to complete the model generated by the DACM approach. The modeling of the Direct Material Deposition process is conducted as an illustrative case study. The scope of the approach is vast and supported by validated scientific methods combined to form the core of the DACM method. The DACM framework is step by step extracting information from a description of the system architecture to create semi-automatically a model that can be simulated and used for multiple types of analyses associated for example with innovation and design improvement. The current paper will focus on the usage of the DACM framework, recently developed in a project, in the field of additive manufacturing.

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