预测熔融长丝制造部件拉伸性能的反向传播和一般回归神经网络对比分析

IF 1.9 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Steffany N. Cerda-Avila, H. I. Medellín-Castillo, J. Cuevas-Tello
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引用次数: 0

摘要

熔融金属丝制造(FFF)工艺包含大量工艺参数,这些参数会影响零件的最终机械性能,从而在增材制造设计(DfAM)实践中产生不确定性。一些研究使用了基于分类机器学习技术(如反向传播神经网络(BPNN))的人工神经网络(ANN)来评估 FFF 零件的尺寸精度、表面粗糙度、抗压、抗弯和抗拉强度。作为一种替代方法,本文提出了一种基于回归机器学习技术的新通用回归神经网络(GRNN)方法,用于估算使用可变工艺参数的聚乳酸(PLA)-FFF 零件的拉伸结构特性。新提出的 GRNN 的性能与 BPNN 的性能进行了比较。比较和评估基于它们准确预测 FFF 零件实验极限拉伸应力(UTS)和弹性模量(E)的能力。评估结果表明,尽管 GRNN 和 BPNN 都能高精度地估计 FFF 零件的结构行为,但拟议 GRNN 的性能(0.001 平均平方误差,MSE)优于 BPNN(0.0031 MSE)。此外,新提出的 GRNN 以 0.62% 的最小平均误差拟合了实验测试值。因此,所提出的 GRNN 可用于设计使用 FFF 制造的部件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparative analysis between backpropagation and general regression neural networks to predict the tensile properties of fused filament fabricated parts
The Fused Filament Fabrication (FFF) process comprises a large number of process parameters that affect the resultant mechanical properties of the parts, generating uncertainties during the Design for Additive Manufacturing (DfAM) practice. Several studies have used Artificial Neural Networks (ANN) based on classification machine learning techniques such as Backpropagation Neural Network (BPNN), to evaluate the dimensional accuracy, surface roughness, compressive, flexural and tensile strength of FFF parts. As an alternative, in this paper a new General Regression Neural Network (GRNN) approach, based on a regression machine learning technique, is proposed to estimate the tensile structural properties of polylactic acid (PLA)-FFF parts using variable process parameters. The performance of the new proposed GRNN is compared with the performance of a BPNN. The comparison and evaluation are based on their capability to accurately predict the experimental Ultimate Tensile Stress (UTS) and the Elastic Modulus ( E) of FFF parts. The outcomes of this evaluation have shown that although the GRNN and the BPNN are able to estimate with high accuracy the structural behaviour of FFF parts, the performance of the proposed GRNN is superior (0.001 Mean Square Error, MSE) than the BPNN (0.0031 MSE). Moreover, the new proposed GRNN fits the experimental test values with a minimal average error of 0.62%. Thus, the proposed GRNN can be used during the design of components intended to be manufacture by FFF.
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来源期刊
CiteScore
5.10
自引率
30.80%
发文量
167
审稿时长
5.1 months
期刊介绍: Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed. Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing. Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.
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