Shaft keys and couplings quickly became the new standard in the field of shaft connections. This technology allows users to transmit high torque between two shafts. Using only hand tools, it can be installed and removed quickly and easily, and thermal work and hydraulic pressure are not required. This chapter aims to study the types of shaft keys and couplings in mechanical applications; it covers the full design of several types of keys and couplings, along with examples and solved/unsolved problems. The chapter analyzes forces and stresses on keys, including rectangular and square keys, gib-head keys, feather keys, and woodruff keys, and couplings such as clamp or compression couplings and flange couplings. This chapter can be used as teaching material for courses in machine design for mechanical and industrial engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
Over the last two decades, several experimental and numerical studies have been performed in order to investigate the acoustic behavior of different muffler materials. However, there is a problem in which it is necessary to perform large, important, time-consuming calculations particularly if the muffler was made from advanced materials such as composite materials. Therefore, this work focused on developing the concept of the indirect dual-chamber muffler made from a basalt fiber reinforced polymer (BFRP) laminated composite, which is a monitoring system that uses a deep learning algorithm to predict the acoustic behavior of the muffler material in order to save effort and time on muffler design optimization. Two types of deep neural networks (DNNs) architectures are developed in Python. The first DNN is called a recurrent neural network with long short-term memory blocks (RNN-LSTM), where the other is called a convolutional neural network (CNN). First, a dual-chamber laminated composite muffler (DCLCM) model is developed in MATLAB to provide the acoustic behavior datasets of mufflers such as acoustic transmission loss (TL) and the power transmission coefficient (PTC). The model training parameters are optimized by using Bayesian genetic algorithms (BGA) optimization. The acoustic results from the proposed method are compared with available experimental results in literature, thus validating the accuracy and reliability of the proposed technique. The results indicate that the present approach is efficient and significantly reduced the time and effort to select the muffler material and optimal design, where both models CNN and RNN-LSTM achieved accuracy above 90% on the test and validation dataset. This work will reinforce the mufflers' industrials, and its design may one day be equipped with deep learning based algorithms.
This chapter discusses the fundamental principles of bearing selection, lubrication, design computations, advanced bearing materials, arrangement, housing, and seals. Also, this chapter discusses fatigue failure mechanisms, fatigue lifetimes, and the reliability of rolling bearings and lubricating greases. Furthermore, readers are provided with hands-on essential formulas, along with examples and solved/unsolved problems for computational design of rolling bearings. This chapter can be used as teaching material for courses in machine design for mechanical, industrial, and materials engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
In various civil engineering applications (CEAs), most vibration responses vary with time and space and are characterized by nonlinearities and uncertainties that are not accounted for during data acquisition. Furthermore, these responses may be contaminated by various sources, which may affect the damage identification process. The main challenge is how to denoise these data in order to acquire a sensitive feature for damage identification that is insensitive to noise and environmental effects. Wavelet Transform (WT) has been proven to be useful for denoising in the field of structural health monitoring (SHM). However, its efficiency is affected by the selection of wavelet parameters. The questions related to the best approach for utilizing the most suitable parameters have not been adequately answered. This study attempts to address this issue by proposing a new denoising algorithm based on the Discrete Wavelet Transform (DWT) technique. The proposed technique provides a strategy to choose the right decomposition levels for denoising and selecting proper mother wavelets. The proposed algorithm uses separate noise thresholds for negative and positive coefficients at each level and applies denoising to detail and approximation components. Datasets from actual civil structures have been analyzed. According to the presented experimental results, the proposed technique exhibits promising results for signal denoising using "db3" compared with traditional techniques. In addition, "db3" and "sym3" are shown to be the best choices for the mother wavelet.
A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage. The proposed method takes into account data type, data and wavelet characteristics, similarity, sharing information, and data recovery accuracy. The novelty of this study lies in integrating multi-criteria which are associated directly with features that correlated well with change in structures due to damage, including common criteria such as energy, entropy, linear correlation index, and variance. Also, it introduces and considers new proposed measures, such as wavelet-based nonlinear correlation such as cosh spectral distance and mutual information, wavelet-based energy fluctuation, measures-based recovery accuracy, such as sensitive feature extraction, noise reduction, and others to evaluate various base wavelets’ function capabilities for appropriate decomposition and reconstruction of structural dynamic responses. The proposed method is verified by experimental and simulated data. The results revealed that the proposed method has a satisfactory performance for base wavelet selection and the small order of Daubechies and Symlet provide the best results, especially order 3. The idea behind our proposed framework can be applied to other structural applications.
Microencapsulated phase change materials have better thermal stability, and are widely used in the field of energy storage. In this paper, the core and wall material characteristics of microencapsulated phase change materials are reviewed. Several preparation methods, such as in-situ polymerisation, interfacial polymerisation, and suspension-like polymerisation, are analysed and the applications of microencapsulated phase change materials in fibre fabric, building, military camouflage and heat conduction are summarised.
Previous broadband energy harvester techniques met many challenges like output power with a sharp peak, small enhancement in bandwidth, and large dimensions and weights. This paper introduces the Automatic Resonance Tuning (ART) technique of two piezoelectric beams to manage these challenges. The energy harvester of two clamped beams automatically adapts their natural frequencies corresponding to the ambient vibration using (sliding masses over the beams). The optimization using COMSOL was conducted to determine the frequency ranges of the low-frequency beam and high-frequency beam and maximize the output power. The bandwidth of the optimized ART harvester is broadened from 27 to 137 H z , ultra-broad bandwidth ( 110 H z ). Our Finite Element Method (FEM) results were validated with experimental results that exhibited excellent convergence. Usually, the dataset of voltage and power is collected by the FEM. Voltages and power evaluated using FEM for some positions are used as the convolutional neural network (CNN) input. CNN predicts the most of masses' positions over the harvester due to the complexity of repetition implementation FEM in several positions. Then, the CNNs are trained for new wide masses position prediction. The mean square error (MSE) of the training dataset is 2.5601 × 10 - 7 μ w and the performance of the CNN training is 97.62 % accuracy ( P % ), 95.38 % regression rate ( R % ), and 93.78 % F-score ( F % ), at epoch 1000 , which shows the effectiveness of the proposed approach.
The gear is one of the most common forms of mechanical transmission. Because of its smooth transmission and large transmission load, it is one of the transmission methods preferred by many engineers, although they may not know how to design gears from scratch, as most of them imitate existing gears and change the corresponding parameters. In this situation, either the strength is often insufficient, or it is over-designed, resulting in waste. This chapter provides gear design examples and shares the gear design process for spur, helical, bevel, and worm gear design, with the goal of providing greater understanding of gears. Examples and solved/unsolved problems for computational design of gears are provided. This chapter can be used as teaching material for courses in machine design for mechanical, industrial, and materials engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
In this paper, basic concepts of resilience, ecology and sustainability are introduced first. Then, associated performance metrics and interdependency of critical infrastructure systems are presented and discussed. Moreover, the importance of big data (BD) and data mining (DM), as emerging themes in this field, is discussed. Other relevant issues such as how to foster decision making and accountability to plan for any expansion in resilience services, resources, and the associated performance metrics and interdependency of critical infrastructure systems are presented. It is the recommendation of this study that due to the difficulty and complexity of resilience, and its definitional ambiguity, the ability to assess such a concept helps to bridge the gap between theory and application, and between the academic and the policy circles. A framework for creating resilient, ecological and sustainable infrastructure systems is also proposed, as a recommendation, in a more holistic and comprehensive way.
In this research, the long-term tensile creep (LTTC) failure in basalt fiber reinforced polymer (BFRP) composites under ambient conditions was detected and predicted via an expert system, in order to monitor the LTTC of BFRP laminate composites. This was accomplished by using the electrical potential change (EPC) technique that employs an electrical capacitance sensor (ECS) in conjunction with an artificial neural network (ANN). A finite element (FE) simulation model for tensile creep detection is generated by ANSYS and MATLAB. Therefore, FE analyses are employed to obtain groups of data for the training of the ANNs. The proposed method is applied to minimize the number of FE analysis for keeping the cost down and save the time of the creep behavior monitoring to a minimum. The paper first presents a study on the creep monitoring for different levels of tensile creep (%σc) as a percentage of ultimate tensile strength (UTS) equal to (25%, 50% and 75%) using EPC technique. Subsequently, the trained ANN is utilized to predict the creep behavior for the level of %σc not included in the FE data. Four different values are selected for the level of %σc; (15, 35%, 60% and 85%).
