Data-Driven Approaches for Bead Geometry Prediction via Melt Pool Monitoring

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING Journal of Manufacturing Science and Engineering-transactions of The Asme Pub Date : 2023-06-21 DOI:10.1115/1.4062800
Zoe Alexander, T. Feldhausen, K. Saleeby, Thomas Kurfess, Katherine Fu, Christopher Saldaña
{"title":"Data-Driven Approaches for Bead Geometry Prediction via Melt Pool Monitoring","authors":"Zoe Alexander, T. Feldhausen, K. Saleeby, Thomas Kurfess, Katherine Fu, Christopher Saldaña","doi":"10.1115/1.4062800","DOIUrl":null,"url":null,"abstract":"\n In additive manufacturing, choosing process parameters to prevent over and under deposition is a time and resource intensive trial-and-error process. Due to the uniqueness of each part geometry, further development of real-time process monitoring and control is needed for reliable part dimensional accuracy. This research shows that support vector regression (SVR) and convolutional neural network (CNN) models offer a promising solution for real-time process control due to the models' abilities to recognize complex, nonlinear patterns with high accuracy. A novel experiment was designed to compare the performance of SVR and CNN models to indirectly detect bead height from a coaxial image of a melt pool from a single layer, single bead build. The study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively.","PeriodicalId":16299,"journal":{"name":"Journal of Manufacturing Science and Engineering-transactions of The Asme","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Science and Engineering-transactions of The Asme","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4062800","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0

Abstract

In additive manufacturing, choosing process parameters to prevent over and under deposition is a time and resource intensive trial-and-error process. Due to the uniqueness of each part geometry, further development of real-time process monitoring and control is needed for reliable part dimensional accuracy. This research shows that support vector regression (SVR) and convolutional neural network (CNN) models offer a promising solution for real-time process control due to the models' abilities to recognize complex, nonlinear patterns with high accuracy. A novel experiment was designed to compare the performance of SVR and CNN models to indirectly detect bead height from a coaxial image of a melt pool from a single layer, single bead build. The study showed that both SVR and CNN models trained on melt pool data collected from a coaxial optical camera can accurately predict the bead height with a mean absolute percentage error of 3.67% and 3.68%, respectively.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于熔池监测的熔头几何形状预测数据驱动方法
在增材制造中,选择工艺参数以防止过度沉积和沉积不足是一个耗时和资源密集的试错过程。由于每个零件几何形状的独特性,需要进一步发展实时过程监测和控制,以获得可靠的零件尺寸精度。该研究表明,由于支持向量回归(SVR)和卷积神经网络(CNN)模型能够以高精度识别复杂的非线性模式,因此为实时过程控制提供了一个很有前途的解决方案。设计了一个新的实验,比较了SVR模型和CNN模型的性能,从单层单熔头构建的熔池同轴图像中间接检测熔头高度。研究表明,基于同轴光学相机采集的熔池数据训练的SVR和CNN模型均能准确预测熔池高度,平均绝对百分比误差分别为3.67%和3.68%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
期刊最新文献
CONTINUOUS STEREOLITHOGRAPHY 3D PRINTING OF MULTI-NETWORK HYDROGELS IN TRIPLY PERIODIC MINIMAL STRUCTURES (TPMS) WITH TUNABLE MECHANICAL STRENGTH FOR ENERGY ABSORPTION A Review of Prospects and Opportunities in Disassembly with Human-Robot Collaboration The Effect of Microstructure on the Machinability of Natural Fiber Reinforced Plastic Composites: A Novel Explainable Machine Learning (XML) Approach A Digital Twin-based environment-adaptive assignment method for human-robot collaboration Combining Flexible and Sustainable Design Principles for Evaluating Designs: Textile Recycling Application
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1