SQ-3DP: A Novel Dataset for Predicting Surface Quality in 3D Printing using Machine Learning

International Journal of Mechanical Engineering

© 2025 by SSRG - IJME Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Aruna Mokhamatam, K.R. Prakash, B.S. Sharmila

10.14445/23488360/IJME-V12I7P104

How to Cite?

Aruna Mokhamatam, K.R. Prakash, B.S. Sharmila, "SQ-3DP: A Novel Dataset for Predicting Surface Quality in 3D Printing using Machine Learning," SSRG International Journal of Mechanical Engineering, vol. 12,  no. 7, pp. 25-34, 2025. Crossref, https://doi.org/10.14445/23488360/IJME-V12I7P104

Abstract:

Optimizing ambient factors and machine operating parameters in 3D printing remains challenging due to the lack of real-time datasets for Machine Learning (ML). To address this, this research introduces the novel SQ-3DP dataset, aimed at predicting product surface quality using ML techniques. The dataset includes machine operating parameters such as nozzle temperature, bed temperature, and nozzle speed, alongside environmental factors like room temperature, humidity, and vibration. Experiments are conducted at three nozzle speeds (30, 50, 70 mm/sec) to analyze the impact of these factors on surface quality. Ambient parameters are collected using sensors and stored on an Arduino, with PCA and scaling applied for preprocessing. Exploratory and correlation studies validated the dataset’s suitability, with PCA preserving critical variance. The SQ-3DP dataset shows significant promise for ML-driven advancements in 3D printing, with models such as KNN, SVM, and Naive Bayes achieving high performance, particularly SVM, for accurate surface quality prediction.

Keywords:

Additive manufacturing, 3D printing, Machine Learning, Surface quality, SQ-3DP dataset, PCA.

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