The changes caused by Industry 4.0 determine the decisions taken by manufacturing companies. Their activities are aimed at adapting processes and products to dynamic market requirements. Additive manufacturing technologies (AM) are the answer to the needs of enterprises. The implementation of AM technology brings many benefits, although for most 3D printing techniques it is also relatively expensive. Therefore, the implementation process should be preceded by an appropriate analysis, in order, finally, to assess the solution. This article presents the concept of using the Bayesian network when planning the implementation of AM technology. The use of the presented model allows the level of the success of the implementation of selected AM technology, to be estimated under given environmental conditions.

Topczak, M., & Śliwa, M. (2021). Assessment of the possibility of using Bayesian nets and Petri nets in the process of selecting additive manufacturing technology in a manufacturing company. Applied Computer Science, 17(1), 5-16. https://doi.org/10.23743/acs-2021-01 

Topczak, Marcin, and Małgorzata Śliwa. "Assessment of the Possibility of Using Bayesian Nets and Petri Nets in the Process of Selecting Additive Manufacturing Technology in a Manufacturing Company." Applied Computer Science 17, no. 1 (2021): 5-16.

M. Topczak and M. Śliwa, "Assessment of the possibility of using Bayesian nets and Petri nets in the process of selecting additive manufacturing technology in a manufacturing company," Applied Computer Science, vol. 17, no. 1, pp. 5-16, 2021, doi: 10.23743/acs-2021-01.

Topczak M, Śliwa M. Assessment of the possibility of using Bayesian nets and Petri nets in the process of selecting additive manufacturing technology in a manufacturing company. Applied Computer Science. 2021;17(1):5-16.