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.

翻译：材料挤出是现有增材制造工艺中最常用的方法之一。尽管其应用广泛且相关技术不断进步，但工艺可靠性和质量保证问题仍未完全解决。该工艺引起的表面粗糙度尤为关键。近年来，已有研究通过实验方案来优化表面粗糙度，但这种经验性的试错过程极其耗费时间和资源。因此，本研究旨在避免使用大规模实验程序来优化材料挤出的表面粗糙度。方法学。本研究深入分析了层高、打印温度、打印速度和壁厚等多个打印参数的影响。所提出的数据驱动预测建模方法利用机器学习模型，基于从文献收集的数据和自行生成的测试数据，自动预测表面粗糙度。结果。通过对文献数据进行10折交叉验证，所提出的机器学习解决方案实现了0.93的相关系数，平均绝对百分比误差为13%。使用自有数据测试时，相关系数降至0.79，平均绝对百分比误差降至8%。因此，该基于挤出的打印表面粗糙度预测方案在分析因素的变异性方面提供了具有竞争力的结果。原创性。随着可用制造数据日益增长，从海量数据中学习的能力对未来制造和科学至关重要。具体而言，机器学习能够帮助在有限实验测试下建立表面粗糙度预测模型。