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Survey of the Dynamic Modeling Methods of Light Vehicles
723-727Views:272Vehicle dynamics models can be classified into two groups based on the model simplification. There are simplified models based on neglections, these models do not contain all body directions: longitudinal, lateral and vertical directions. There are several reasons for the simplification: control, estimation and analysis methods can be used only with simplified models, or another reason is the computational cost. Apart from simplified models, there are detailed/truth vehicle dynamics models which aim is to provide a virtual plant of the real vehicle for virtual prototype-based development. In this paper, some simplified vehicle models are presented, after a short introduction.
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Survey of the Application Fields and Modeling Methods of Automotive Vehicle Dynamics Models
196-209Views:249In this paper, a review is presented on automotive vehicle dynamics modeling. Applied vehicle dynamics models from various application fields are analyzed and classified in the first section. Vehicle dynamics models may be simplified because of different reasons: several control/estimation/analysis methods are suitable only for simplified models (e.g. using control-oriented models), or because of the computational cost. Detailed/truth models of vehicle dynamics represent another field of vehicle dynamics modeling, these models play an important role in the virtual prototyping of vehicles. In the second section, the main modeling considerations of vehicle dynamics are presented in longitudinal, lateral and vertical directions. Various physical effects must be considered in the case of vehicle dynamics modeling, a lot of these effects are significant only in a specific direction of the vehicle body, which is the main potential of model simplification. The section presents vehicle modeling considerations in all of the three translational directions of the vehicle body.
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Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering
72-89Views:249The optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.
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Maintenance Strategies and Life Cycle Costs of Renewable Energy Systems
106-116Views:164Life cycle costs are important factors in decisions on renewable energy investments. Since maintenance costs generally constitute a high portion of the life cycle costs, the maintenance strategy applied in a project can affect the bottom line significantly. The effective maintenance tools used in the production industry (e.g., diagnostics, condition monitoring, data management, integrated information systems, machine learning, and automated decision making) can be involved in planning and maintenance of renewable energy systems to gain the benefits of these approaches. In this paper the effects of maintenance strategies on life cycle costs are investigated and the benefits of up-to-date condition monitoring techniques are presented through case studies.
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Defect Analysis of Bearings with Vibration Monitoring and Optical Methods
1-12Views:206Diagnosis of bearings with advanced methods gained remarkable roles in the previous years. This article focuses on the manufacturing defects and methods to reveal and classify them. During manufacturing several faults could emerge because of the grinding operation, tool wear and chatter vibration. Inproper handling of the bearing parts because of the collosion to each other and the storing box that causes deformation. To reveal these problems several methods are applied in industry. For deeper surface analysis nitric acid can be used to initate the finished surface of the roller then natrium-carbonate that nautralize the elements. Vibration analysis in its standard Fourier form is not a new achivement but other mathematical tools could be applied to condition monitoring such as wavelet transform. It is an efficient tool for analyzing the vibration signal of the bearings because it can detect the sudden changes and transient impulses in the signal caused by faults on the bearing elements. In this article five different wavelets, Daubechies, Gaussian, Coiflet, Mexican hat, Meyer are compared according to the Energy to Shannon Entropy ratio criteria to reveal their efficiency for fault detection.
