Multi-parameter analysis of curing cycle for GNPs/glass fabric/ epoxy laminated nanocomposites

In this study, a multi-parameter analysis, using Taguchi method for design of experiments, has been conducted to investigate the optimum curing conditions for GNPs/E-glass fabric/epoxy laminated nanocomposites. The independent variables in the L25 Taguchi orthogonal array were heating rate, curing temperature and curing time, addressing five levels each. Tensile and 3-point bending tests were performed for each experiment number (run number) of the Taguchi L25. The analysis shown that the most significant para­meter for tensile strength is the time and for flexural strength is the tem­pe­ra­ture. Also, it shown that the optimum performance was obtained for tem­pera­ture values greater than the glass transition temperature Tg

Model-based tool condition prognosis using power consumption and scarce surface roughness measurements

In machining processes, underusing and overusing cutting tools directly affect part quality, entailing economic and environmental impacts. In this paper, we propose and compare different strategies for tool replacement before processed parts exceed surface roughness specifications without underusing the tool. The proposed strategies are based on an online part quality monitoring system and apply a model-based algorithm that updates their parameters using adaptive recursive least squares (ARLS) over polynomial models whose generalization capabilities have been validated after generating a dataset using theoretical models from the bibliography. These strategies assume that there is a continuous measurement of power consumption and a periodic measurement of surface roughness from the quality department (scarce measurements). The proposed strategies are compared with other straightforward tool replacement strategies in terms of required previous experimentation, algorithm simplicity and self-adaptability to disturbances (such as changes in machining conditions). Furthermore, the cost of each strategy is analyzed for a given benchmark and with a given batch size in terms of needed tools, consumed energy and parts out of specifications (i.e., rejected). Among the analyzed strategies, the proposed model-based algorithm that detects in real-time the optimal instant for tool change presents the best results.Funding for open access charge: CRUE-Universitat Jaume

Prediction, classification and diagnosis of spur gear conditions using artificial neural network and acoustic emission

The gear system is a critical component in the machinery and predicting the performance of a gear system is an important function. Unpredictable failures of a gear system can cause serious threats to human life, and have large scale economic effects. It is necessary to inspect gear teeth periodically to identify crack propagation and, other damages at the earliest. This study has two main objectives. Firstly, the research predicted and classified specific film thickness (λ) of spur gear by Artificial Neural Network (ANN) and Regression models. Parameters such as acoustic emission (AE), temperature and specific film thickness (λ) data were extracted from works of other researchers. The acoustic emission signals and temperature were used as input to ANN and Regression models, while (λ) was the output of the models. Second objective is to use the third generation ANN (Spiking Neural Network) for fault diagnosis and classification of spur gear based on AE signal. For this purpose, a test rig was built with several gear faults. The AE signal was processed through preprocessing, features extraction and selection methods before the developed ANN diagnosis and classification model were built. These processes were meant to improve the accuracy of diagnosis system based on information or features fed into the model. This research investigated the possibility of improving accuracy of spur gear condition monitoring and fault diagnoses by using Feed-Forward Back- Propagation Neural Networks (FFBP), Elman Network (EN), Regression Model and Spiking Neural Network (SNN). The findings showed that use of specific film thickness has resulted in the FFBP network being able to provide 99.9% classification accuracy, while regression and multiple regression models attained 73.3 % and 81.2% classification accuracy respectively. For gear fault diagnosis, the SNN achieved nearly 97% accuracy in its diagnosis. Finally, the methods use in the study have proven to have high accuracy and can be used as tools for prediction, classification and fault diagnosis in spur gear

Cutting tool operational reliability prediction based on acoustic emission and logistic regression model

Working status of cutting tools (CTs) is crucial to the products’ precision. If broken down, it may lead to waste product. Condition monitoring and life prediction are beneficial to the manufacturing process. In this research, Logistic regression models (LRMs) and acoustic emission (AE) signal are used to evaluate reliability. Based on different conditions estimation, CTs are investigated to determine the best maintenance time. Based on experimental data analysis, AE and cutting force signals have better linear relationship with CT wearing process. They can be used to demonstrate CT degradation process. Frequency band energy is determined as characteristic vector for AE signal using wavelet packet decomposition. Two reliability estimation models are constructed based on cutting force and AE signals. One uses both signals, while the other uses only AE signal. The reliability degree can be estimated using the two models, independently. AE feature extraction and LRM can effectively estimate CT conditions. As it is difficult to monitor cutting force in a practical working condition, it is an effective method for CT reliability analysis by the combination of AE and LRM method. Experimental investigation is used to verify the effectiveness of this method

Online monitoring of Rheology using Passive Acoustic Emission sensing with machine learning

Advancement in technological practices in the manufacturing industry continuously provides opportunities for producing more efficient and higher quality products. For industries such as personal care, home care, pharmaceutical and cosmetics that rely heavily on rheology for the manufacture of liquid products, these opportunities present in the implementation of real-time measurement and monitoring of rheology using Industry 4.0 principles. An experimental setup was developed using a passive acoustic emission sensor placed online on a continuous, closed loop flow system to collect acoustic data in real-time from the fluid flowing through the closed loop. The acoustic data was then processed using mathematical models and machine learning algorithm to develop rheological fingerprints specific to a distinct fluid under distinct process conditions. Solutions of Glycerol, Carboxymethyl Cellulose (CMC) and Carbopol® were chosen to represent Newtonian, Non-Newtonian and Non-Newtonian with yield stress respectively. Results of the acoustic data processing showed that visual monitoring of rheology can be achieved using the tools developed and a predictive model using Artificial Neural Network (ANN) can estimate rheological values for unknown fluid samples

Bayesian Filtering Methods For Dynamic System Monitoring and Control

Real-time system monitoring and control represent two of the most important issues that characterize modern industries in critical areas of civilian and military interest, including the power grid, energy, healthcare, aerospace, and infrastructure. During the past decade, there has been a rapid development of robust dynamic system monitoring and control methods for fault diagnosis and failure prognosis. Among various monitoring and control policies, condition-based maintenance (CBM) has been studied by many researchers due to its ability to enable a large amount of monitoring data for real-time diagnostics and prognostics. A considerable amount of literature has been published on the subject, providing a large volume of dynamic system control methods. Previously published studies are limited by assumptions that can generally be distinguished into three main categories: i) predefined system failure thresholds, ii) simplified latent dynamics, and iii) unrealistic parametric forms that describe the evolution of system dynamics through time. This thesis provides an array of solution approaches that overcome the aforementioned assumptions in a smart and effective way by introducing novel quantitative frameworks for real-time monitoring, control, and decision-making for dynamic systems. The proposed frameworks are categorized into two main phases of a comprehensive framework. The first phase contains two original Bayesian filtering methods for condition monitoring and control of systems with either linear or non-linear degradation dynamics. The former is designed only for systems with linear latent and observable dynamics and utilizes Kalman filtering for state-parameter inference. It considers a failure process that is purely stochastic and is based on logistic regression. This process is directly affected by the latent system dynamics, therefore avoiding the need for a priori failure thresholds. The latter takes into consideration multiple levels of system dynamics that evolve either linearly or non-linearly. A hybrid particle filter is developed for state-parameter inference, while an Extreme Learning Machine artificial neural network is utilized to relate sensor observations to latent system dynamics. Both frameworks are tested and validated on synthetic and real-world time-series datasets. The second phase of this thesis introduces an original method for optimal control and decision-making that employs Bayesian filtering-based deep reinforcement learning with fully stochastic environments. Sets of deep reinforcement learning agents were trained to develop control policies. Bayesian filtering methods from the first phase were utilized to provide environment states that use the estimates from latent system dynamics. This method is used in two different applications for maintenance cost minimization and estimating the remaining useful life of a system under condition monitoring. Results obtained from applying the framework on simulated and real-world time-series data suggest that the proposed Bayesian filtering-based deep reinforcement learning algorithm can be trained even with limited data, which can be useful for real-time control and decision making for many dynamic systems

Potenziale der schwachen künstlichen Intelligenz für die betriebliche Ressourceneffizienz

POTENZIALE DER SCHWACHEN KÜNSTLICHEN INTELLIGENZ FÜR DIE BETRIEBLICHE RESSOURCENEFFIZIENZ Potenziale der schwachen künstlichen Intelligenz für die betriebliche Ressourceneffizienz / Friedrich, Robert (Rights reserved) ( -