Machine Learning Approach for Optimization of Automated Fiber Placement Processes

被引：38

作者：

Bruening, J. ^{[1
]}

Denkena, B. ^{[1
]}

Dittrich, M. -A. ^{[1
]}

Hocke, T. ^{[2
]}

机构：

[1] Leibniz Univ Hannover, Inst Prod Engn & Machine Tools, Univ 2, D-30823 Hannover, Germany

[2] Leibniz Univ Hannover, Inst Prod Engn & Machine Tools, Ottenbecker Damm 12, D-21684 Stade, Germany

来源：

1ST CIRP CONFERENCE ON COMPOSITE MATERIALS PARTS MANUFACTURING (CIRP CCMPM 2017) | 2017年 / 66卷

关键词：

machine learning; assisted process planning; process data visualization;

D O I：

10.1016/j.procir.2017.03.295

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved. (C) 2017 Published by Elsevier B.V.

引用

页码：74 / 78

页数：5

共 50 条

[41] Automated Shmoo Data Analysis: A Machine Learning Approach
Wang, Wei
PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL SYMPOSIUM ON QUALITY ELECTRONIC DESIGN (ISQED 2014), 2015, : 212 - 218
[42] Machine Learning Approach to Automated Correction of LATEX Documents
Chuvilin, Kirill
2016 18TH CONFERENCE OF OPEN INNOVATIONS ASSOCIATION AND SEMINAR ON INFORMATION SECURITY AND PROTECTION OF INFORMATION TECHNOLOGY (FRUCT-ISPIT), 2016, : 33 - 40
[43] Automated Motor Tic Detection: A Machine Learning Approach
Bruegge, Nele Sophie
Sallandt, Gesine Marie
Schappert, Ronja
Li, Frederic
Siekmann, Alina
Grzegorzek, Marcin
Baeumer, Tobias
Frings, Christian
Beste, Christian
Stenger, Roland
Roessner, Veit
Fudickar, Sebastian
Handels, Heinz
Muenchau, Alexander
MOVEMENT DISORDERS, 2023, 38 (07) : 1327 - 1335
[44] Predictors of NFT Prices: An Automated Machine Learning Approach
Alon, Ilan
Bretas, Vanessa P. G.
Katrih, Villi
JOURNAL OF GLOBAL INFORMATION MANAGEMENT, 2023, 31 (01)
[45] A machine learning approach for automated ULF wave recognition
Balasis, Georgios
Aminalragia-Giamini, Sigiava
Papadimitriou, Constantinos
Daglis, Ioannis A.
Anastasiadis, Anastasios
Haagmans, Roger
JOURNAL OF SPACE WEATHER AND SPACE CLIMATE, 2019, 9
[46] An automated packaging planning approach using machine learning
Knoll, Dino
Neumeier, Daniel
Prueglmeier, Marco
Reinhart, Gunther
52ND CIRP CONFERENCE ON MANUFACTURING SYSTEMS (CMS), 2019, 81 : 576 - 581
[47] Hyperparameter Optimization of the Machine Learning Model for Distillation Processes
Oh, Kwang Cheol
Kwon, Hyukwon
Park, Sun Yong
Kim, Seok Jun
Kim, Junghwan
Kim, Daehyun
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2024, 2024
[48] Dynamic modeling and parameter identification for a gantry-type automated fiber placement machine
Cheng, Liang
Wu, Jianbo
Guo, Yingjie
Li, Jiangxiong
Ke, Yinglin
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 119 (11-12): : 7243 - 7258
[49] A Post-processing Algorithm for Gantry-Type Automated Fiber Placement Machine
Lu, Hua
Yang, Fan
Jin, Hongyu
Han, Zhenyu
ADVANCED MATERIALS RESEARCHES AND APPLICATION, 2013, 763 : 187 - 190
[50] Dynamic modeling and parameter identification for a gantry-type automated fiber placement machine
Liang Cheng
Jianbo Wu
Yingjie Guo
Jiangxiong Li
Yinglin Ke
The International Journal of Advanced Manufacturing Technology, 2022, 119 : 7243 - 7258

← 1 2 3 4 5 →