Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-10-13 DOI:10.48084/etasr.6185

Sivarao Subramonian, Kumaran Kadirgama, Abdulkareem Sh. Mahdi Al-Obaidi, Mohd Shukor Mohd Salleh, Umesh Kumar Vatesh, Satish Pujari, Dharsyanth Rao, Devarajan Ramasamy

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引用次数: 0

Abstract

This research article presents a comprehensive study on the performance modeling of 3D printed parts using Artificial Neural Networks (ANNs). The aim of this study is to optimize the mechanical properties of 3D printed components through accurate prediction and analysis. The study focuses on the widely employed Fused Deposition Modeling (FDM) technique. The ANN model is trained and validated using experimental data, incorporating input parameters such as temperature, speed, infill direction, and layer thickness to predict mechanical properties including yield stress, Young's modulus, ultimate tensile strength, flexural strength, and elongation at fracture. The results demonstrate the effectiveness of the ANN model with an average error below 10%. The study also reveals the significant impact of process parameters on the mechanical properties of 3D printed parts and highlights the potential for optimizing these parameters to enhance the performance of printed components. The findings of this research contribute to the field of additive manufacturing by providing valuable insights into the optimization of 3D printing processes and facilitating the development of high-performance 3D printed components.

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增材制造3D打印零件的人工神经网络性能建模与评价

本文对基于人工神经网络(ann)的3D打印部件性能建模进行了全面研究。本研究的目的是通过准确的预测和分析来优化3D打印部件的力学性能。研究重点是广泛应用的熔融沉积建模(FDM)技术。人工神经网络模型使用实验数据进行训练和验证，包括温度、速度、填充方向和层厚等输入参数，以预测机械性能，包括屈服应力、杨氏模量、极限抗拉强度、弯曲强度和断裂伸长率。实验结果证明了该模型的有效性，平均误差在10%以下。该研究还揭示了工艺参数对3D打印部件机械性能的重大影响，并强调了优化这些参数以提高打印部件性能的潜力。这项研究的结果通过为3D打印工艺的优化和促进高性能3D打印部件的开发提供有价值的见解，为增材制造领域做出了贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.