Image Processing Methods Based on Key Temperature Features for State Analysis and Process Monitoring of Selective Laser Melting (SLM)

Chen, Zhehan; Zhang, Xiaohua; He, Ketai; Zong, Xianhui; Wang, Haiyue

doi:10.1145/3206185.3206210

Cited by 1 publication

(1 citation statement)

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“…One of the data sources that the studies in these fields are based on is the in-situ monitoring of AM printers. It can be noted, that considerable number of studies use either optical data, such as thermal images (Moran et al, 2020) (Zhehan et al, 2018) or high resolution pictures (Petrich et al, 2020)Aminzadeh andKurfess (2019), or acoustic data (Dongsen et al, 2018) (Shevchik et al, 2018).…”

Section: Data Analyticsmentioning

confidence: 99%

Data Analysis as the Basis for Improved Design for Additive Manufacturing (Dfam)

Hamulczuk

Isaksson

2021

Proc. Des. Soc.

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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.

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