Mechanical response of additively manufactured foam: A machine learning approach

Neelam, Rajat; Kulkarni, Shrirang Ambaji; Bharath, H. S.; Powar, Satvasheel; Doddamani, Mrityunjay

doi:10.1016/j.rineng.2022.100801

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…Recent study on the utilization of ML in the material design, identification, modeling, analysis, and characterization of polymers was published. 13 In polymer science, the integration of ML models has proven to be a formidable asset, particularly in the analysis and optimization of various polymer processes, 14,15 including the intricate domain of polymer bead foaming. 16,17 Researchers have increasingly turned to ML algorithms to gain deeper insights into the complexities of these processes, and the exploration of such methodologies is particularly pronounced in the context of PLA.…”

Section: Introductionmentioning

confidence: 99%

Machine learning investigation of polylactic acid bead foam extrusion

Shah,

Brütting,

Albuquerque

et al. 2024

J of Applied Polymer Sci

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This study employs machine learning algorithms to analyze the bead foam extrusion process and to assess the impact of processing parameters, specifically focusing on their effects on bead foam density and melt pressure in under water granulation (UWG) for polylactic acid (PLA). These interrelated parameters, influenced by processing parameters such as temperature, screw speed, and blowing agent, possess challenges for traditional empirical methods to capture. The key factors that significantly impact the prediction of melt pressure in UWG are blowing agent, injector pressure, temperature in B‐extruder and die size. Likewise, essential parameters for predicting bead foam density comprise blowing agent, injector pressure, temperature in B‐extruder, die plate temperature, melt temperature in B‐extruder, and melt pressure in B‐extruder. Machine learning (ML) models were employed to forecast bead foam density and melt pressure in UWG using various processing parameters in PLA bead foam extrusion. The random forest model achieved a high coefficient of determination score of 0.96 for predicting melt pressure in UWG. Additionally, the decision tree model demonstrated effective predictions for bead density, with the score: 0.81. These ML models can be applied to diverse materials, leading to more sustainable, efficient processes for bead foam extrusion.

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Section: Introductionmentioning

confidence: 99%

Machine learning investigation of polylactic acid bead foam extrusion

Shah,

Brütting,

Albuquerque

et al. 2024

J of Applied Polymer Sci

View full text Add to dashboard Cite

show abstract

“…It is an upcoming technology in the manufacturing industry that entails the use of computer software (CAD) to command the machines to create the desired material layer by layer. AM has grown popular because of its potential and benefits such as design freedom and short production periods [ 7 , 8 ]. There are various classifications of AM processes, including photopolymerization, material jetting, binder jetting, directed energy deposition, powder bed fusion, sheet lamination, and fused deposition modeling or material extrusion [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%