Pascu Sergiu, Balc Nicolae
Department of Manufacturing Engineering, Faculty of Industrial Engineering, Robotics and Production Management, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania.
Polymers (Basel). 2023 Aug 31;15(17):3610. doi: 10.3390/polym15173610.
This paper presents a new method of process parameter optimization, adequate for 3D printing of PLA (Polylactic Acid) components. The authors developed a new piece of Hybrid Manufacturing Equipment (HME), suitable for producing complex parts made from a biodegradable thermoplastic polymer, to promote environmental sustainability. Our new HME equipment produces PLA parts by both additive and subtractive techniques, with the aim of obtaining accurate PLA components with good surface quality. A design of experiments has been applied for optimization purposes. The following manufacturing parameters were analyzed: rotation of the spindle, cutting depth, feed rate, layer thickness, nozzle speed, and surface roughness. Linear regression models and neural network models were developed to improve and predict the surface roughness of the manufactured parts. A new test part was designed and manufactured from PLA to validate the new mathematical models, which can now be applied for producing complex parts made from polymer materials. The neural network modeling (NNM) allowed us to obtain much better precision in predicting the final surface roughness (Ra), as compared to the conventional linear regression models (LNM). Based on these modelling methods, the authors developed a practical methodology to optimize the process parameters in order to improve the surface quality of the 3D-printed components and to predict the actual roughness values. The main advantages of the results proposed for hybrid manufacturing using polymer materials like PLA are the optimized process parameters for both 3D printing and milling. A case study has been undertaken by the authors, who designed a specific test part for their new hybrid manufacturing equipment (HME), in order to test the new methodology of optimizing the process parameters, to validate the capability of the new HME. At the same time, this new methodology could be replicated by other researchers and is useful as a guideline on how to optimize the process parameters for newly developed equipment. The innovative approach holds potential for widespread equipment functionality enhancement among other users.
本文提出了一种新的工艺参数优化方法，适用于聚乳酸（PLA）部件的3D打印。作者开发了一种新型混合制造设备（HME），适用于生产由可生物降解热塑性聚合物制成的复杂部件，以促进环境可持续性。我们的新型HME设备通过增材和减材技术生产PLA部件，目的是获得具有良好表面质量的精确PLA部件。为了优化目的，应用了实验设计。分析了以下制造参数：主轴转速、切削深度、进给速度、层厚、喷嘴速度和表面粗糙度。开发了线性回归模型和神经网络模型来改进和预测所制造部件的表面粗糙度。设计并制造了一个由PLA制成的新测试部件，以验证新的数学模型，该模型现在可用于生产由聚合物材料制成的复杂部件。与传统线性回归模型（LNM）相比，神经网络建模（NNM）使我们在预测最终表面粗糙度（Ra）方面获得了更高的精度。基于这些建模方法，作者开发了一种实用方法来优化工艺参数，以提高3D打印部件的表面质量并预测实际粗糙度值。使用PLA等聚合物材料进行混合制造所提出结果的主要优点是3D打印和铣削的工艺参数均得到了优化。作者进行了一个案例研究，他们为其新型混合制造设备（HME）设计了一个特定的测试部件，以测试优化工艺参数的新方法，验证新型HME的能力。同时，其他研究人员可以复制这种新方法，它可作为如何为新开发设备优化工艺参数的指南。这种创新方法在增强其他用户设备功能方面具有广泛的潜力。
