Acoustic monitoring and control system to determine the properties of damping materials
- Authors: Stahlberg, Martin
- Date: 2012
- Subjects: Vibration (Aeronautics) -- Damping , Acoustic emission testing
- Language: English
- Type: Thesis , Masters , MEngineering (Mechatronics)
- Identifier: vital:9658
- Description: Experience shows that the noise and sound quality in vehicles are often a recurring criticism. The bodies of modern vehicles consist predominantly of thin sheets of metal. It is hard to prevent the excitation of bending vibrations and the subsequent emission of disturbing noise while driving. The noise spectrum in a car that can be heard by the driver is from ”latent roar” to ”chattering” noise of the body and engine. In automotive vehicles damped materials, especially plastics or materials made from sheet metal and surface damping treatments, are used. Those have high internal energy losses and damp sound oscillatory systems found in the body or interior of cars. A further advantage of such treated components is that they are applied to existing components working over wide temperature and frequency ranges. Many companies provide such ”sound-absorbing compounds”. The requirements for these damping materials are high temperature-resistance, water repellence, fuel and oil-resistance and good adhesion to the base material [17]. The acoustic properties, especially the damping of the plate vibrations through rubber are of interest. the question arises how can the damping coeficient of coated metal sheets can be measured and secondly, by how much the road noise is reduced when built-in sheets are coated with a known damped material. With the Oberst Bar Test Method (named after Dr. H. Oberst) the properties are determined of the internal damping materials that can be used to simulate mechanical constructions to determine damping of larger surfaces. This method describes a laboratory test procedure for measuring the mechanical properties of damped materials. A block diagram of the test system consisting of a damped material bonded to a vibrating cantilever steel bar is shown in figure 2.1. This method is useful for testing materials such as metals, enamels, ceramics, rubbers, plastics, reinforced epoxy matrices and wood. In addition to damping measurement, the test allows for the determination of the Young’s modulus E of the material. E is calculated from the resonance frequency of a given mode and from the physical constants of the bar. By associating the damping factor with the Young’s modulus, a complex quantity is defined which is called the Complex Modulus of Elasticity. Measurements of dynamic mechanical properties are also useful in the research on the molecular structure of materials.
- Full Text:
- Date Issued: 2012
- Authors: Stahlberg, Martin
- Date: 2012
- Subjects: Vibration (Aeronautics) -- Damping , Acoustic emission testing
- Language: English
- Type: Thesis , Masters , MEngineering (Mechatronics)
- Identifier: vital:9658
- Description: Experience shows that the noise and sound quality in vehicles are often a recurring criticism. The bodies of modern vehicles consist predominantly of thin sheets of metal. It is hard to prevent the excitation of bending vibrations and the subsequent emission of disturbing noise while driving. The noise spectrum in a car that can be heard by the driver is from ”latent roar” to ”chattering” noise of the body and engine. In automotive vehicles damped materials, especially plastics or materials made from sheet metal and surface damping treatments, are used. Those have high internal energy losses and damp sound oscillatory systems found in the body or interior of cars. A further advantage of such treated components is that they are applied to existing components working over wide temperature and frequency ranges. Many companies provide such ”sound-absorbing compounds”. The requirements for these damping materials are high temperature-resistance, water repellence, fuel and oil-resistance and good adhesion to the base material [17]. The acoustic properties, especially the damping of the plate vibrations through rubber are of interest. the question arises how can the damping coeficient of coated metal sheets can be measured and secondly, by how much the road noise is reduced when built-in sheets are coated with a known damped material. With the Oberst Bar Test Method (named after Dr. H. Oberst) the properties are determined of the internal damping materials that can be used to simulate mechanical constructions to determine damping of larger surfaces. This method describes a laboratory test procedure for measuring the mechanical properties of damped materials. A block diagram of the test system consisting of a damped material bonded to a vibrating cantilever steel bar is shown in figure 2.1. This method is useful for testing materials such as metals, enamels, ceramics, rubbers, plastics, reinforced epoxy matrices and wood. In addition to damping measurement, the test allows for the determination of the Young’s modulus E of the material. E is calculated from the resonance frequency of a given mode and from the physical constants of the bar. By associating the damping factor with the Young’s modulus, a complex quantity is defined which is called the Complex Modulus of Elasticity. Measurements of dynamic mechanical properties are also useful in the research on the molecular structure of materials.
- Full Text:
- Date Issued: 2012
Tool wear monitoring in end milling of mould steel using acoustic emission
- Authors: Olufayo, Oluwole Ayodeji
- Subjects: Acoustic emission testing , Tool-steel
- Language: English
- Type: Thesis , Masters , MEngineering
- Identifier: vital:9651 , http://hdl.handle.net/10948/d1014688
- Description: Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features.
- Full Text:
- Authors: Olufayo, Oluwole Ayodeji
- Subjects: Acoustic emission testing , Tool-steel
- Language: English
- Type: Thesis , Masters , MEngineering
- Identifier: vital:9651 , http://hdl.handle.net/10948/d1014688
- Description: Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features.
- Full Text:
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