Machine Learning Can Predict the Timing and Size of Analog Earthquakes

Despite the growing spatiotemporal density of geophysical observations at subduction zones, predicting the timing and size of future earthquakes remains a challenge. Here we simulate multiple seismic cycles in a laboratory‐scale subduction zone. The model creates both partial and full margin ruptures, simulating magnitude M_w 6.2–8.3 earthquakes with a coefficient of variation in recurrence intervals of 0.5, similar to real subduction zones. We show that the common procedure of estimating the next earthquake size from slip‐deficit is unreliable. On the contrary, machine learning predicts well the timing and size of laboratory earthquakes by reconstructing and properly interpreting the spatiotemporally complex loading history of the system. These results promise substantial progress in real earthquake forecasting, as they suggest that the complex motion recorded by geodesists at subduction zones might be diagnostic of earthquake imminence

Graph Analytics For Smart Manufacturing

Emergence in the high-resolution sensing and imaging technologies have allowed us to track the variability in manufacturing processes occurring at every conceivable resolution of interest. However, representation of the underlying manufacturing processes using streaming sensor data remains a challenge. Efficient representations are critical for enabling real-time monitoring and quality assurance in smart manufacturing. Towards this, we present graph-based methods for efficient representation of the image data gathered from advanced manufacturing processes. In this dissertation, we first focus on experimental studies involving the finishing of complex additively manufactured components and discuss the important phenomenological details of the polishing process. Our experimental studies point to a material redistribution theory of polishing where material flows in the form of thin fluid like layers, eventually bridging up the neighboring asperities. Subsequently, we use the physics of the process gathered from this study to develop a random planar graph approach to represent the evolution of the surface morphology as gathered from electron microscopic images during mechanical polishing. In the next half of the dissertation, we focus on unsupervised image segmentation using graph cuts by iteratively estimating the image labels by solving the max-flow problem while optimally estimating the tuning parameters using maximum a posteriori estimation. We also establish the consistency of the posterior estimates. Applications of the method in benchmark and manufacturing case studies show more than 90% improvement in the segmentation performance as compared to state-of-the-art unsupervised methods. While the characterization of the advanced manufacturing processes using image and sensor data is increasingly sought after, it is equally important to perform characterization rapidly. The last chapter of this dissertation is set to focus on the rapid characterization of the salient microstructural phases present on a metallic workpiece surface via a nanoindentation-based lithography process. A summary of the contributions and directions of future work are also presented

Deep learning approach to the texture optimization problem for friction control in lubricated contacts

All the data used in this work is available free of charge from https://doi.org/10.34622/datarepositorium/MUVOJDThe possibility to control friction through surface microtexturing can offer invaluable advantages in many fields, from wear and pollution reduction in the transportation industry to improved adhesion and grip. Unfortunately, the texture optimization problem is very hard to solve using traditional experimental and numerical methods, due to the complexity of the texture configuration space. Here, we apply machine learning techniques to perform the texture optimization, by training a deep neural network to predict, with extremely high accuracy and speed, the Stribeck curve of a textured surface in lubricated contact. The deep neural network is used to completely resolve the mapping between textures and Stribeck curves, enabling a simple method to solve the texture optimization problem. This work demonstrates the potential of machine learning techniques in texture optimization for friction control in lubricated contacts.This work was supported by the Portuguese Foundation for Science and Technology (FCT, I.P.) in the framework of the Strategic Funding UIDB/04650/2020, projects PTDC/EME-SIS/30446/2017, project POCI-01–0247-FEDER-045940, and Advanced Computing Project CPCA/A2/4513/2020 for accessing MACC-BOB HPC resources

Triboinformatic Approaches for Surface Characterization: Tribological and Wetting Properties

Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates along with a tendency to seize under dry sliding or poor lubricating conditions. Graphite and graphene particle-reinforced Al metal matrix composites (MMCs) exhibit self-lubricating properties and they can be potential alternatives for Al alloys in dry or starved lubrication conditions. In this dissertation, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and hybrid ensemble algorithm-based ML models have been developed to correlate the dry friction and wear of aluminum alloys, Al-graphite, and Al-graphene MMCs with material properties, the composition of alloys and MMCs, and tribological parameters. ML analysis reveals that the hardness, sliding distance, and tensile strength of the alloys influences the COF most significantly. On the other hand, the normal load, sliding speed, and hardness were the most influential parameters in predicting wear rate. The graphite content is the most significant parameter for friction and wear prediction in Al-graphite MMCs. For Al-graphene MMCs, the normal load, graphene content, and hardness are identified as the most influential parameters for COF prediction, while the graphene content, load, and hardness have the greatest influence on the wear rate. The ANN, KNN, SVM, RF, and GBM, as well as hybrid regression models (RF-GBM), with the principal component analysis (PCA) descriptors for COF and wear rate were also developed for Al-graphite MMCs in liquid-lubricated conditions. The hybrid RF-GBM models have exhibited the best predictive performance for COF and wear rate. Lubrication condition, lubricant viscosity, and applied load are identified as the most important variables for predicting wear rate and COF, and the transition from dry to lubricated friction and wear is studied. The micro- and nanoscale roughness of zinc (Zn) oxide-coated stainless steel and sonochemically treated brass (Cu Zn alloy) samples are studied using the atomic force microscopy (AFM) images to obtain the roughness parameters (standard deviation of the profile height, correlation length, the extreme point location, persistence diagrams, and barcodes). A new method of the calculation of roughness parameters involving correlation lengths, extremum point distribution, persistence diagrams, and barcodes are developed for studying the roughness patterns and anisotropic distributions inherent in coated surfaces. The analysis of the 3×3, 4×4, and 5×5 sub-matrices or patches has revealed the anisotropic nature of the roughness profile at the nanoscale. The scale dependency of the roughness features is explained by the persistence diagrams and barcodes. Solid surfaces with water-repellent, antimicrobial, and anticorrosive properties are desired for many practical applications. TiO2/ZnO phosphate and Polymethyl Hydrogen Siloxane (PMHS) based 2-layer antimicrobial and anticorrosive coatings are synthesized and applied to steel, ceramic, and concrete substrates. Surfaces with these coatings possess complex topographies and roughness patterns, which cannot be characterized completely by the traditional analysis. Correlations between surface roughness, coefficient of friction (COF), and water contact angle for these surfaces are obtained. The hydrophobic modification in anticorrosive coatings does not make the coated surfaces slippery and retained adequate friction for transportation application. The dissertation demonstrates that Triboinformatic approaches can be successfully implemented in surface science, and tribology and they can generate novel insights into structure-property relationships in various classes of materials

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

Explainable machine learning for labquake prediction using catalog-driven features

Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) prediction based on ML using acoustic emission (AE) records from three laboratory stick-slip experiments performed on Westerly granite samples with naturally fractured rough faults, more similar to the heterogeneous fault structures in the nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new features based on focal mechanism. A regression voting ensemble of Long-Short Term Memory (LSTM) networks predicts TTF with a coefficient of determination (R2) of 70% on the test dataset. Feature importance analysis revealed that AE rate, correlation integral, event proximity, and focal mechanism-based features are the most important features for TTF prediction. Results reveal that the network uses all information among the features for prediction, including general trends in high correlated features as well as fine details about local variations and fault evolution involved in low correlated features. Therefore, some highly correlated and physically meaningful features may be considered less important for TTF prediction due to their correlation with other important features. Our study provides a ground for applying catalog-driven to constrain TTF of complex heterogeneous rough faults, which is capable to be developed for real application

Material removal modeling and life expectancy of electroplated CBN grinding wheel and paired polishing

Paired polishing process (PPP) is a variant of the chemical mechanical polishing process which facilitates defect mitigation via minimization of maximum force as well as effective planarization via profile driven determination of force gradient. The present embodiment of PPP machine employs two polishing wheels, radially spanning the wafer surface on a counter-gimbaled base. The PPP machine is deployed to experimentally investigate the role of the process parameters on the surface roughness evolution, and the effective material removal rate. Two sets of copper and aluminum blanket layers were polished under a range of applied down force, polishing wheel speed and transverse feed rate to examine the scalability of the process parameters for different material constants. The experimental measurements along with the topological details of the polishing pad have been utilized to develop a mechanistic model of the process. The model employs the soft wheel-workpiece macroscopic contact, the polishing wheel roughness and its amplification to the local contact pressure, the kinematics of abrasive grits at the local scale, and the collective contribution of these individual micro-events to induce an effective material removal rate at the macroscale. The model shows the dependence of the material removal on the ratio of wheel rotational to feed speed for the PPP process, in a form of an asymptote that is scaled by the surface hardness of each material. The PPP machine exploits this insight and utilizes an oblique grinding technique that obviates the traditional trade-off between MRR and planarization efficiency. High speed grinding with cubic boron nitride (CBN) wheels are industrially attractive options for hard-to-machine metallic alloys, due to their low cost, reliability, reduced thermal damage and superior workpiece surface finish. However, thermal issues and transient behavior of the grinding wheel wear directly affect the workpiece surface integrity and tolerances. This thesis investigates the topological evolution of an electroplated CBN grinding wheel, characterization of its wear and life expectancy, when utilized in nickel-based alloy grinding. Depth profiling, digital microscopy and scanning electron microscopy are utilized to investigate topological evolution and mechanisms of grit failure. The results are used to elucidate the statistical evolution of the grinding wheel surface. It is found that when a grit is pulled out, load redistribution commences in its neighboring domain, with localized rapid grit wear. The unique experimental findings are used to develop a novel phenomenological model for the progressive wheel wear, including the combination of grit pullout (Stage I) and grit wear (Stage II). For Stage-I, the model employs the grinding kinematics, thermal shock to the grit-wheel interface, Paris law type fatigue approach. For Stage-II, Preston type wear approach is employed. The molding framework is utilized to infer electroplated CBN grinding wheels’ life expectancy for the high speed grinding and high efficiency deep grinding (HEDG) processes. The model provides the process design domain for different grinding process parameters, while maintaining a targeted wheel life, and averting potential damage of the workpiece

Unsupervised clustering of catalogue-driven features for characterizing temporal evolution of labquake stress

Earthquake forecasting poses significant challenges, especially due to the elusive nature of stress states in fault systems. To tackle this problem, we use features derived from seismic catalogues obtained from acoustic emission (AE) signals recorded during triaxial stick-slip experiments on natural fractures in three Westerly granite samples. We extracted 47 physically explainable features from AE data that described spatio-temporal evolution of stress and damage in the vicinity of the fault surface. These features are then subjected to unsupervised clustering using the K-means method, revealing three distinct stages with a proper agreement with the temporal evolution of stress. The recovered stages correspond to the mechanical behaviour of the rock, characterized as initial stable (elastic) deformation, followed by a transitional stage leading to an unstable deformation prior to failure. Notably, AE rate, clustering-localization features, fractal dimension, b-value, interevent time distribution, and correlation integral are identified as significant features for the unsupervised clustering. The systematically evolving stages can provide valuable insights for characterizing preparatory processes preceding earthquake events associated with geothermal activities and waste-water injections. In order to address the upscaling issue, we propose to use the most important features and, in case of normalization challenge, removing non-universal features, such as AE rate. Our findings hold promise for advancing earthquake prediction methodologies based on laboratory experiments and catalogue-driven features

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time