Predictive modelling of surface roughness in fused deposition modelling using data fusion

Abstract

To realise high quality, additively manufactured parts, real-time process monitoring and advanced predictive modelling tools are crucial for accelerating quality assurance in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and predictive modelling of the surface roughness of additively manufactured parts. This paper presents a data fusion approach to predicting surface roughness in fused deposition modelling (FDM) processes. The predictive models are trained using random forests (RFs), support vector regression (SVR), ridge regression (RR), and least absolute shrinkage and selection operator (LASSO). A real-time monitoring system is developed to monitor the health condition of a FDM machine in real-time using multiple sensors. RFs, SVR, RR, and LASSO are demonstrated on the condition monitoring data collected from these sensors. To integrate the data sources, a feature-level data fusion method is introduced. Experimental results have shown that the predictive models trained by the machine learning algorithms are capable of predicting the surface roughness of additively manufacturing parts with very high accuracy. The prediction accuracy can be further improved using the data fusion method.

Keywords:

• predictive modelling
• additive manufacturing
• condition monitoring
• surface roughness
• data fusion

Acknowledgements

Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the University of Central Florida or the Digital Manufacturing and Design Innovation Institute.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research reported in this paper is partially supported by the University of Central Florida and funding from the Digital Manufacturing and Design Innovation Institute.