Evaluation and prediction of wear response of pine wood dust filled epoxy composites using neural computation

被引:65
作者
Kranthi, Ganguluri [1 ]
Satapathy, Alok [1 ]
机构
[1] Natl Inst Technol, Rourkela 769008, India
关键词
Neural computation; Pine wood dust; Epoxy; Composites; Sliding wear; ANN; MECHANICAL-PROPERTIES; FRACTURE-TOUGHNESS; THERMAL-PROPERTIES; FAILURE; EROSION; BEHAVIOR;
D O I
10.1016/j.commatsci.2010.06.001
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Inspired by the biological nervous system, an artificial neural network (ANN) approach is a fascinating computational tool, which can be used to simulate a wide variety of complex engineering problems such as tribo-performance of polymer composites. This paper, in this context, reports the implementation of ANN in analyzing the wear performance of a new class of epoxy based composites filled with pine wood dust. Composites of three different compositions (with 0, 5 and 10 wt.% of pine wood dust reinforced in epoxy resin) are prepared. Dry sliding wear trials are conducted following a well planned experimental schedule based on design of experiments (DOE). Significant control factors predominantly influencing the wear rate are identified. An ANN approach taking into account training and test procedure is implemented to predict the dependence of wear behavior on various control factors. This work shows that pine wood dust possesses good filler characteristics as it improves the sliding wear resistance of the polymeric resin and that factors like filler content, sliding velocity and normal load, in this sequence, are the significant factors affecting the specific wear rate. It is further seen that the use of a neural network model to simulate experiments with parametric design strategy is quite effective for prediction of wear response of materials within and beyond the experimental domain. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:609 / 614
页数:6
相关论文
共 32 条
[1]  
[Anonymous], 1998, HDB COMPOSITES
[2]  
[Anonymous], J ADHES
[3]  
Barta S, 1997, J APPL POLYM SCI, V64, P1525
[4]  
CANTWELL WJ, 1994, FRACTOGRAPHY FAILURE, P233
[5]  
Haykin S., 1999, Neural Networks: A Comprehensive Foundation, DOI DOI 10.1017/S0269888998214044
[6]   Fracture toughness of spherical silica-filled epoxy adhesives [J].
Imanaka, M ;
Takeuchi, Y ;
Nakamura, Y ;
Nishimura, A ;
Iida, T .
INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES, 2001, 21 (05) :389-396
[7]   Multicomponent compounding of polypropylene [J].
Jarvela, PA ;
Jarvela, PK .
JOURNAL OF MATERIALS SCIENCE, 1996, 31 (14) :3853-3860
[8]  
Katz H., 1987, Handbook of Fillers for Plastics
[9]   Positive temperature coefficient behavior of polymer composites having a high melting temperature [J].
Kim, JI ;
Kang, PH ;
Nho, YC .
JOURNAL OF APPLIED POLYMER SCIENCE, 2004, 92 (01) :394-401
[10]  
KINOCH AJ, 1985, J MATER SCI, V20, P4169