Data analysis as the basis for improved design for additive manufacturing (DFAM)
Paper i proceeding, 2021

Additive Manufacturing (AM) has a large potential to revolutionize the manufacturing industry, yet the printing parameters and part design have a profound impact on the robustness of the printing process as well as the resulting quality of the manufactured components. To control the printing process, a substantial number of parameters is measured while printing and used primarily to control and adjust the printing process in-situ. The question raised in this paper is how to benefit from these data being gathered to gain insight into the print process stability. The case study performed included the analysis of data gathered during printing 22 components. The analysis was performed with a widely used Random Forest Classifier. The study revealed that the data did contain some detectable patterns that can be used further in assessing the quality of the printed component, however, they were distinct enough so that in case the test and train sets were comprised of separate components the predictions' result was very poor. The study gives a good understanding of what is necessary to do a meaningful analytics study of manufacturing data from a design perspective.

Machine learning

Additive Manufacturing

Design for Additive Manufacturing (DfAM)


Dominika Hamulczuk

Student vid Chalmers

Ola Isaksson

Chalmers, Industri- och materialvetenskap, Produktutveckling

Proceedings of the Design Society

2732527X (eISSN)

Vol. 1 811-820

23rd International Conference on Engineering Design, ICED 2021
Gothenburg, Sweden,

Digitalisering av tillverkningsflödet av Additiv Tillverkning i Sverige (DiSam)

VINNOVA (2017-04776), 2017-11-01 -- 2021-04-30.


Produktionsteknik, arbetsvetenskap och ergonomi

Tillförlitlighets- och kvalitetsteknik




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