Evaluation of low-velocity impact response of honeycomb sandwich structures using factorial-based design of experiments

Citation data:

Journal of Sandwich Structures and Materials, ISSN: 1099-6362, Vol: 14, Issue: 3, Page: 339-361

Publication Year:
2012
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Repository URL:
http://scholarsmine.mst.edu/mec_aereng_facwork/1046
DOI:
10.1177/1099636212442667
Author(s):
Butukuri, Ramanjaneya R.; Bheemreddy, Venkata; Chandrashekhara, K.; Samaranayake, V. A.
Publisher(s):
SAGE Publications
Tags:
Materials Science; Engineering; Prepreq; Low-Velocity Impact; Lay-Up; Response Surface Analysis; Out-Of-Autoclave; ANOVA; Regression Analysis; Prepreq; Low-Velocity Impact; Lay-Up; Response Surface Analysis; Out-Of-Autoclave; ANOVA; Regression Analysis; Mathematics; Mechanical Engineering; Statistics and Probability
article description
A response surface analysis of data from factorial experiments is used to determine the effect of different design factors on the low-velocity impact response of the honeycomb sandwich structures. A low-cost, out-of-autoclave manufacturing process is utilized to manufacture aerospace quality honeycomb sandwich panels. CYCOM® 5320 out-of-autoclave prepreg and FM® 300-2M are used as facesheet and adhesive, respectively. The out-of-autoclave process uses the vacuum bag pressure, thus, avoiding costly tooling and making the process more economical. A completely randomized design is used while manufacturing and testing the samples. Three design factors: angle difference between successive prepreg layers, number of prepreg layers, and number of adhesive layers, are selected as experimental variables. Each variable is considered at three levels, yielding a 3factorial design. To investigate the effect of the experimental variables on the three response variables: energy absorbed (X); peak contact force (Y) and maximum deflection (Z), a low-velocity impact test is conducted. Analysis of variance and response surface techniques are used to analyze the data. The significant design factors for each response variable are identified using analysis of variance. Response surface analysis is carried out and the resulting regression models are employed to quantify the behavior of each of the response variables in response to changes in the design factors. The regression models are verified with the confirmation test results and both are in close agreement. © The Author(s) 2012.