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
收藏
页码：74 / 78
页数：5
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