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Dynamic analyses of vehicles

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Title: Dynamic analyses of vehicles
Author(s): Ingram, Richard George
Doctoral Committee Chair(s): Larson, Carl
Department / Program: Mechanical Science and Engineering
Discipline: Mechanical Science and Engineering
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: Ph.D.
Genre: Dissertation
Subject(s): Engineering, Mechanical Artificial Intelligence
Abstract: The overall objective of this thesis is to improve the method engineers use to predict the performance of earthmoving vehicle systems. The motivation for this work is shown by illustrating how these predictions are used in the design process to impact the financial well-being of the corporations that use them. The vehicle, the operator who controls the vehicle, and the soil which the vehicle digs, carries, and dumps are the three parts of earthmoving systems whose behavior must be predicted. Practicing engineers have well known techniques for predicting the vehicle's behavior. They do need advances that will allow them to make the predictions more quickly. In this thesis, a method for predicting vehicle behavior known as Kane's method is evaluated to determine if it can address this problem. These engineers currently have difficulty modeling the soil. In this thesis, a new method for modeling soil using neural networks is evaluated and predicted results are compared to test results.Since the improvements to vehicle simulation developed in this thesis must ultimately improve the ability of corporations that produce earthmoving equipment to compete, it is necessary to show how this work benefits the business process. This thesis includes a description of one type of marketing model that utilizes vehicle performance predictions and shows how that model can be applied to an earthmoving vehicle.
Issue Date: 1994
Type: Text
Language: English
URI: http://hdl.handle.net/2142/22781
Rights Information: Copyright 1994 Ingram, Richard George
Date Available in IDEALS: 2011-05-07
Identifier in Online Catalog: AAI9512411
OCLC Identifier: (UMI)AAI9512411
 

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