Liu Fuguo, Chen Ziru, Xu Jun, Zheng Yanyan, Su Wenyi, Tian Maozai, Li Guodong
School of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi 830012, China.
Department of Mathematics and Data Science, Changji University, Changji 831100, China.
Polymers (Basel). 2024 Sep 23;16(18):2680. doi: 10.3390/polym16182680.
Three-dimensional printing technology is a rapid prototyping technology that has been widely used in manufacturing. However, the printing parameters in the 3D printing process have an important impact on the printing effect, so these parameters need to be optimized to obtain the best printing effect. In order to further understand the impact of 3D printing parameters on the printing effect, make theoretical explanations from the dimensions of mathematical models, and clarify the rationality of certain important parameters in previous experience, the purpose of this study is to predict the impact of 3D printing parameters on the printing effect by using machine learning methods. Specifically, we used four machine learning algorithms: SVR (support vector regression): A regression method that uses the principle of structural risk minimization to find a hyperplane in a high-dimensional space that best fits the data, with the goal of minimizing the generalization error bound. Random forest: An ensemble learning method that constructs a multitude of decision trees and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. GBDT (gradient boosting decision tree): An iterative ensemble technique that combines multiple weak prediction models (decision trees) into a strong one by sequentially minimizing the loss function. Each subsequent tree is built to correct the errors of the previous tree. XGB (extreme gradient boosting): An optimized and efficient implementation of gradient boosting that incorporates various techniques to improve the performance of gradient boosting frameworks, such as regularization and sparsity-aware splitting algorithms. The influence of the print parameters on the results under the feature importance and SHAP (Shapley additive explanation) values is compared to determine which parameters have the greatest impact on the print effect. We also used feature importance and SHAP values to compare the importance impact of print parameters on results. In the experiment, we used a dataset with multiple parameters and divided it into a training set and a test set. Through Bayesian optimization and grid search, we determined the best hyperparameters for each algorithm and used the best model to make predictions for the test set. We compare the predictive performance of each model and confirm that the extrusion expansion ratio, elastic modulus, and elongation at break have the greatest influence on the printing effect, which is consistent with the experience. In future, we will continue to delve into methods for optimizing 3D printing parameters and explore how interpretive machine learning can be applied to the 3D printing process to achieve more efficient and reliable printing results.
三维打印技术是一种已在制造业中广泛应用的快速成型技术。然而，三维打印过程中的打印参数对打印效果有重要影响，因此需要对这些参数进行优化以获得最佳打印效果。为了进一步了解三维打印参数对打印效果的影响，从数学模型的维度进行理论解释，并阐明以往经验中某些重要参数的合理性，本研究的目的是使用机器学习方法预测三维打印参数对打印效果的影响。具体而言，我们使用了四种机器学习算法：支持向量回归（SVR）：一种回归方法，它使用结构风险最小化原则在高维空间中找到最适合数据的超平面，目标是最小化泛化误差界。随机森林：一种集成学习方法，它构建多个决策树，并输出作为各个树的类别的众数（分类）或均值预测（回归）的类别。梯度提升决策树（GBDT）：一种迭代集成技术，它通过顺序最小化损失函数将多个弱预测模型（决策树）组合成一个强模型。每个后续树的构建是为了纠正前一棵树的错误。极端梯度提升（XGB）：梯度提升的一种优化且高效的实现，它结合了各种技术来提高梯度提升框架的性能，例如正则化和稀疏感知分裂算法。比较特征重要性和SHAP（Shapley加法解释）值下打印参数对结果的影响，以确定哪些参数对打印效果影响最大。我们还使用特征重要性和SHAP值来比较打印参数对结果的重要性影响。在实验中，我们使用了一个具有多个参数的数据集，并将其分为训练集和测试集。通过贝叶斯优化和网格搜索，我们确定了每种算法的最佳超参数，并使用最佳模型对测试集进行预测。我们比较了每个模型的预测性能，并确认挤出膨胀率、弹性模量和断裂伸长率对打印效果影响最大，这与经验一致。未来，我们将继续深入研究优化三维打印参数的方法，并探索如何将可解释机器学习应用于三维打印过程，以实现更高效、可靠的打印结果。
