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Neural Network Models of Simple Mechanical Systems Illustrating the Feasibility of Accelerated Life TestingA complete evaluation of the tribological characteristics of a given material/mechanical system is a time-consuming operation since the friction and wear process is extremely systems sensitive. As a result, experimental designs (i.e., Latin Square, Taguchi) have been implemented in an attempt to not only reduce the total number of experimental combinations needed to fully characterize a material/mechanical system, but also to acquire life data for a system without having to perform an actual life test. Unfortunately, these experimental designs still require a great deal of experimental testing and the output does not always produce meaningful information. In order to further reduce the amount of experimental testing required, this study employs a computer neural network model to investigate different material/mechanical systems. The work focuses on the modeling of the wear behavior, while showing the feasibility of using neural networks to predict life data. The model is capable of defining which input variables will influence the tribological behavior of the particular material/mechanical system being studied based on the specifications of the overall system.
Document ID
Document Type
Conference Paper
Fusaro, Robert L.
(NASA Lewis Research Center Cleveland,OH United States)
Jones, Steven P.
(Ohio Aerospace Inst. Cleveland, OH United States)
Jansen, Ralph
(Ohio Aerospace Inst. Cleveland, OH United States)
Date Acquired
September 6, 2013
Publication Date
May 1, 1996
Subject Category
Mechanical Engineering
Report/Patent Number
NAS 1.15:107108
Meeting Information
Annual Meeting(Cincinnati, OH)
Accession Number
Funding Number(s)
PROJECT: RTOP 323-16-04
Distribution Limits
Public Use Permitted.
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