Acoustic Emission Signal Denoising of Bridge Structures using SOM Neural Network Machine Learning

Identification Noise signal is one of the challenging problems in the health monitoring of bridge structure using acoustic emission monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common method in identifying of the signals from the defect of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focus on the AE noise signal from a bridge in operation state and other specific loading state, which is diagnosed in the hardware filtering technology, spatial identification and SOM neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50 %, and can barely filter strong noise signal. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90 % and 100 % respectively. The trained network is used to test183 sample signals, the defect signal detection accuracy reaches 76 % and 78.8 %, therefore, the noise signal filtering effect is significantly improved

Analysis of drilling of composite laminates

This dissertation deals with the characterization, modeling, and monitoring of drilling process of composite materials through various experimental and analytical investigations. Analytical models were developed which predicts critical thrust force and feed rate above which the delamination crack begins to propagate in the drilling of multi-directional laminated composites. The delamination zone was modeled as a circular plate, with clamped edge and subjected to different load profiles. Based on fracture mechanics, classical laminate theory and orthogonal cutting mechanics, expressions were obtained for critical thrusts and feed rates at different ply locations. The proposed models have been verified by experiments and compared with the existing models. It was found that the new developed models provide more accurate and rigorous results than the formers. Quality of holes and drilling-induced damage when drilling fiber reinforced composite laminates were experimentally studied. Several quality responses were measured as indices of drilling performance, including thrust force, delamination size, residual compression strength, and flexural strength. Effects of key drilling parameters on these responses were statistically analyzed, and optimal drilling conditions for high performance and free-damage drilling were identified. Experimental results revealed that the choice of drilling conditions are critical to hole performance especially when these materials are subjected to structural loads. An experimental study of acoustic emission as a tool for in-process monitoring and nondestructive evaluation of drilling of composites was conducted. Acoustic emission was used to examine the relationship between signal response and drilling induced damages. A procedure for discrimination and identification of different damage mechanisms was presented utilizing different signal analysis tools. Based on the results, frequency distribution and energy percentage of most important damage mechanisms occurring during drilling were determined. It was concluded that acoustic emission has a great potential for the application of online monitoring and damage characterization in the drilling of composite structures

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production