Hyper parameter settings for ML Algorithms.

3D printing has brought significant changes to manufacturing sectors, making it possible to produce intricate, multi-layered designs with greater ease. This study focuses on optimizing the compressive strength (CS) of functionally graded multi-material (PLA/Almond Shell Reinforced PLA) which is fabricated with the aid of the FFF process, a widely used additive manufacturing technique. Six different machine learning models (ML) were utilized to estimate CS using key process parameters, namely print speed (PS), layer height (LH), and printing temperature (PT). Among six ML models, Polynomial Regression (PR) performed best, with an R2 of 0.88 and the lowest error metrics (MAE = 1.38, RMSE = 1.9, MSE = 3.6). SHAP analysis indicated that PS is the most influential parameter, followed by LH. PR predicted optimal parameters (PS = 19 mm/s, LH = 0.1 mm, PT = 216°C) and yielded a predicted CS of 36 MPa, which was experimentally validated as 34.8 MPa with a low error of 3.44%. Also, the PR outperformed the traditional Taguchi method, which predicted a CS of 33.74 MPa, showing a 7.5% improvement and lower error. This demonstrates that PR-based ML optimization offers better accuracy and improved mechanical performance, making these FGMs suitable for various consumer applications.

3D打印已为制造业带来深刻变革，使得复杂精细的多层结构的制造更为简便易行。本研究聚焦于优化经熔融沉积成型（Fused Filament Fabrication，FFF）工艺制备的功能梯度多材料（PLA/杏仁壳增强PLA）的抗压强度（Compressive Strength，CS），其中FFF是一种应用广泛的增材制造技术。本研究采用六种不同的机器学习模型（Machine Learning，ML），以打印速度（Print Speed，PS）、层高（Layer Height，LH）与打印温度（Printing Temperature，PT）这三项关键工艺参数作为输入，对抗压强度进行预测。在六种机器学习模型中，多项式回归（Polynomial Regression，PR）表现最优，其决定系数（R²）达0.88，且拥有最低的误差指标：平均绝对误差（Mean Absolute Error，MAE）=1.38、均方根误差（Root Mean Squared Error，RMSE）=1.9、均方误差（Mean Squared Error，MSE）=3.6。SHAP分析（SHapley Additive exPlanations）表明，打印速度是影响最大的工艺参数，其次为层高。多项式回归预测得到最优工艺参数：打印速度为19 mm/s、层高为0.1 mm、打印温度为216°C，预测的抗压强度为36 MPa，经实验验证的实际抗压强度为34.8 MPa，相对误差仅为3.44%。此外，多项式回归的表现优于传统田口方法（Taguchi Method），后者预测的抗压强度为33.74 MPa，较其提升了7.5%且误差更低。这一结果证明，基于多项式回归的机器学习优化方案具备更高的预测精度与更优的力学性能，使得这类功能梯度多材料可适用于各类民用消费场景。