Material extrusion is one of the most commonly used approaches within the
additive manufacturing processes available. Despite its popularity and related
technical advancements, process reliability and quality assurance remain only
partially solved. In particular, the surface roughness caused by this process
is a key concern. To solve this constraint, experimental plans have been
exploited to optimize surface roughness in recent years. However, the latter
empirical trial and error process is extremely time- and resource-consuming.
Thus, this study aims to avoid using large experimental programs to optimize
surface roughness in material extrusion.
Methodology. This research provides an in-depth analysis of the effect of
several printing parameters: layer height, printing temperature, printing speed
and wall thickness. The proposed data-driven predictive modeling approach takes
advantage of Machine Learning models to automatically predict surface roughness
based on the data gathered from the literature and the experimental data
generated for testing.
Findings. Using 10-fold cross-validation of data gathered from the
literature, the proposed Machine Learning solution attains a 0.93 correlation
with a mean absolute percentage error of 13 %. When testing with our own data,
the correlation diminishes to 0.79 and the mean absolute percentage error
reduces to 8 %. Thus, the solution for predicting surface roughness in
extrusion-based printing offers competitive results regarding the variability
of the analyzed factors.
Originality. As available manufacturing data continue to increase on a daily
basis, the ability to learn from these large volumes of data is critical in
future manufacturing and science. Specifically, the power of Machine Learning
helps model surface roughness with limited experimental tests.

该研究旨在避免使用大型实验计划来优化材料挤出中的表面粗糙度。通过使用机器学习模型，该研究提供了一种数据驱动的预测建模方法来自动预测基于文献和实验数据的表面粗糙度。研究结果表明，该方法在预测挤出式打印的表面粗糙度方面具有竞争力的结果。

基于前期研究数据的数据驱动研究：使用机器学习预测材料挤压表面粗糙度的初步研究

Toward data-driven research: preliminary study to predict surface  roughness in material extrusion using previously published data with Machine  Learning