Hasil untuk "Mining engineering. Metallurgy"

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

Vishal Soni, Roberto Liam Menchaca, Devin Davis et al.

A process-specific alloy design strategy was employed to achieve exceptional as-deposited strength-ductility synergy in Al–Ce alloys, eliminating the need for post-aging treatments. Al–Ce alloys, known for their excellent creep resistance and thermal stability due to the Al11Ce3 phase, were optimized using CALPHAD simulations to incorporate solid solution and precipitation strengthening in addition to composite strengthening from the Al11Ce3 phase. Additive friction stir deposition (AFSD) was used to fabricate 3D builds from cast material, refining the grain size and microstructure by fragmenting Al11Ce3 lamellae into smaller, uniformly dispersed particles and inducing Al3Sc nanoprecipitates during deposition. Microstructural and mechanical analyses revealed remarkable improvements: while the cast alloy fractured at 230 MPa with no ductility, the AFSD alloy achieved a yield strength of 385 MPa, UTS of 530 MPa, and 12.6% plastic strain. These as-deposited properties surpassed those of cast, extruded, HIPPed, or LPBF/DED Al–Ce alloys, including the previous results obtained after aging. This strategy leverages AFSD-specific process dynamics, such as intense deformation and localized high temperatures, to enable in-situ precipitation and microstructural refinement with exceptional mechanical properties, offering a transformative pathway for designing energy-efficient, high-performance materials.

Shengquan He, Rentao Gou, Biao Cao et al.

Abstract Constructing underground structures in coastal regions poses significant challenges, particularly due to seawater intrusion, which can cause corrosion and threaten the safety and stability of the caverns and surrounding facilities. A crucial aspect of preventing seawater intrusion lies in accurate mapping of the geological structure of the reservoir area and its proximity to the coastline. This study uses reflection seismic data, borehole ultrasonic imaging, and core samples to identify geological features that influence subsurface stability. The seismic profile revealed a V-shaped or concave-down structure associated with faults, suggesting a down-dropped block within the subsurface. Seismic facies analysis identified chaotic, high-amplitude reflections within basement rocks, indicating highly fractured and faulted zones, possibly including mylonitic rocks. A novel approach is proposed that combines borehole ultrasonic imaging with fractal theory, integrating core photos, seismic attributes, and geophysical analysis. A functional relationship was established between the joint surface density and the joint information dimension within the borehole. Additionally, a relationship was established between fault information dimension and borehole joint surface density. Results showed that the joint information dimensions within the identified fault zones consisttently exceeded 1.775. By applying a threshold of joint information dimension greater than 1.775, 15 small-scale structural prediction zones were identified. Subsequent analysis of core photos from the predicted regions confirmed the presence of relatively long fractured zones, demonstrating the high accuracy of the proposed method in identifying small-scale structures. This study presents a comprehensive method for mapping geological structures in coastal areas, providing an essential reference for the identification and management of small-scale features in underground engineering projects.

Shubhangini Yadav, Varun A. Baheti

The Mn–Zn system, technologically crucial for galvanized Mn–containing steels and potential ZnMn–based biomaterials, has been studied using the conventional diffusion couple technique. The first experimental evidence has been presented to indicate the presence of 3 separate phases, as ε, ε1 and ε2, together in the interdiffusion zone. By taking advantage of local equilibrium present at interphase interfaces in a diffusion couple, the controversial ε–phase region has been resolved after almost more than 6 decades. Incorporating new experimental results of 3 separate phases could be beneficial in refining the present thermodynamic optimization of the Mn–Zn system. Furthermore, MnZn9 and MnZn13 have also grown in the Mn/Zn diffusion couple, such that there are 5 distinct phase layers, including ε, ε1 and ε2. Diffusion parameters such as integrated diffusion coefficients and the ratio of tracer diffusivities, which are currently unavailable, are also determined considering ideal molar volumes. It helps to understand the atomic mechanism of diffusion and the probable defects present in phase(s).

Agnieszka Lawrynowicz, Luis Galarraga, Mehwish Alam et al.

In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining methodologies, categorized into three groups: inductive logic programming, path sampling and generalization, and linear programming. Following this, we delve into neurosymbolic methods, covering topics such as the integration of deep learning with rules, the use of embeddings for rule learning, and the application of large language models in rule learning.

Shasha Huang, Haijun Fu, Yaoxu Xiong et al.

The advancement of high entropy alloys (HEAs) has stimulated the development of multicomponent L12 γ′ intermetallics, which exhibit both excellent strength and ductility. These intermetallics with long-range order are typically employed as coherent precipitates to enhance the high-temperature performance of HEAs. This study investigates the influence of multicomponent γ′ intermetallics on the irradiation response of the strengthened HEA γ phase, focusing on the role of the multicomponent γ/γ′ interface in defect production and evolution. Our results indicate that the chemical disorder within the multicomponent γ′ phase leads to a broad defect energy spectrum near the interface zone, diminishing its effectiveness as a defect sink. Based on these observations, we propose that, distinct from traditional superalloys, the γ/γ′ interface plays a minor role in the irradiation resilience of multicomponent γ′ intermetallics-strengthened HEAs. Instead, the inherent chemical disorder within the multicomponent γ′ intermetallics emerges as the key factor.

Ning TIAN, Xiaoyun SONG, Wenjun YE et al.

The hot compression test of SP700 titanium alloy was performed using a Gleeble3800 thermal simulation test machine, and the thermal deformation behavior and microstructure evolution were examined in the temperature range of 800–880 °C, strain rate range of 1–10 s−1, and compression deformation of 30%–50%. The findings reveal that the peak flow stress of the SP700 titanium alloy decreases with increasing deformation temperature but increases with increasing strain rate. At a deformation temperature of 800 ℃, the flow stress curves demonstrate evident dynamic softening features with a rapid decrease in flow stress after the peak stress. By metallographic and scanning electron microstructure observations of the deformed microstructure, the α lamellar is gradually broken and spheroidized, and dynamic recrystallization occurs. With increasing deformation temperature, the induced phase transformation occurs, which leads to the dissolution of the α phase and an increase in the volume fraction of the β phase. The degree of recrystallization of the β phase increases with several β recrystallization grains at the grain boundaries, whereas the degree of globularization of the α lamellae decreases with increasing temperature. As the deformation temperature increases to 880 ℃, the flow stress curves exhibit steady flow. Recrystallization behavior preferentially occurs in the β grains, while the α lamellar remains flat without globularization behavior. That is, recrystallization of the β phase occurs under the test deformation conditions. For the α lamellae, when the deformation temperature is constant, the degree of spheroidization of the α lamellae increases with strain rate and compression deformation. During the hot deformation process, the α lamellae parallel to the compression axis kink, and the cumulative misorientation is discontinuous inside the α lamellae. At the discontinuous points, the new α/α interface boundary is produced, which causes the formation of unstable dihedral angles. To lower the surface tension energy, the β phase wedges into the α lamellae, which eventually results in the break of the α lamellae. For the α lamellae perpendicular to the compression axis, the interface fluctuates, resulting in continuous cumulative misorientation inside the α lamellae. When the rotation axis of the lamellae changes, a new α/α interface boundary is produced. At the interface fluctuation or the new α/α interface, the β phase easily wedges into the α lamellae by element diffusion, which finally causes fragmentation and spheroidization. Moreover, some of the α lamellae experience a shear deformation, leading to fragmentation under compression.

Soroush Mojiri, Alireza Shafiei, Amin Nourollahi

Currently, due to the high costs of production and expensive raw materials, approaches, including, making models smaller and lighter, are especially considered in the design of structures. In order to better describe the capabilities, efficiency, and limitations of an innovative field called topology optimization, various practical problems under different loadings and boundary conditions were evaluated in this study. Optimization algorithms were used in ANSYS software for the optimization of a cantilever beam under static loading, double-girder beam and a dome-shaped geometry under static and thermal loading, a hot fluid transfer tee and an engine exhaust manifold under static loading and convection heat transfer. The results showed that the reduced volume in the final models were equal to 66.29%, 52.88%, 50.05%, 51.85%, and 35.02%, respectively. Consequently, this reduced volume causes some increase in the tension, and displacement of the final model, which can adjust them according to the limitations governing the problem. Furthermore, the amount of increase in the average value of the stress in the cantilever beam, double-girder beam, and dome-shaped geometry were 88, 800, and 6 MPa, and the average amount of displacement in these samples increased by 10.2%, 200%, and 3.3%, respectively. Challenges, and manufacturability of optimized problems were investigated by 3D printing of a dome-shaped model using the FDM method, which illustrated that the output product has a suitable level of accuracy and smoothness. Subsequently, by using supporting structures, three-dimensional holes were created with proper precision in the 3D-printed sample, which satisfied the manufacturability of relatively complex models.

Ziqi Zhang, Tomas Jasaitis, Richard Freeman et al.

While text mining and NLP research has been established for decades, there remain gaps in the literature that reports the use of these techniques in building real-world applications. For example, they typically look at single and sometimes simplified tasks, and do not discuss in-depth data heterogeneity and inconsistency that is common in real-world problems or their implication on the development of their methods. Also, few prior work has focused on the healthcare domain. In this work, we describe an industry project that developed text mining and NLP solutions to mine millions of heterogeneous, multilingual procurement documents in the healthcare sector. We extract structured procurement contract data that is used to power a platform for dynamically assessing supplier risks. Our work makes unique contributions in a number of ways. First, we deal with highly heterogeneous, multilingual data and we document our approach to tackle these challenges. This is mainly based on a method that effectively uses domain knowledge and generalises to multiple text mining and NLP tasks and languages. Second, applying this method to mine millions of procurement documents, we develop the first structured procurement contract database that will help facilitate the tendering process. Second, Finally, we discuss lessons learned for practical text mining/NLP development, and make recommendations for future research and practice.

Jonathan Gal, Maytal B Szabo, Ori Rottenstreich

In the last few years several papers investigated selfish mine attacks, most of which assumed that every miner that is not part of the selfish mine pool will continue to mine honestly. However, in reality, remaining honest is not always incentivized, particularly when another pool is employing selfish mining or other deviant strategies. In this work we explore the scenario in which a large enough pool capitalises on another selfish pool to gain 100\% of the profit and commit double spending attacks. We show that this counterstrategy can effectively counter any deviant strategy, and that even the possibility of it discourages other pools from implementing deviant strategies.

Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza et al.

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.

Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real et al.

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.

Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi et al.

Machine learning models need to be continually updated or corrected to ensure that the prediction accuracy remains consistently high. In this study, we consider scenarios where developers should be careful to change the prediction results by the model correction, such as when the model is part of a complex system or software. In such scenarios, the developers want to control the specification of the corrections. To achieve this, the developers need to understand which subpopulations of the inputs get inaccurate predictions by the model. Therefore, we propose correction rule mining to acquire a comprehensive list of rules that describe inaccurate subpopulations and how to correct them. We also develop an efficient correction rule mining algorithm that is a combination of frequent itemset mining and a unique pruning technique for correction rules. We observed that the proposed algorithm found various rules which help to collect data insufficiently learned, directly correct model outputs, and analyze concept drift.

Jingyan LIN, Mingxiao SUN, Hongdou LI et al.

With the wide application of rare earth metals in high-tech fields such as medical treatment and new materials, its strategic position has been increasing. As a major country in rare earth, China supplies rare earth products of different varieties and grades to all countries in the world, making great contributions to the development of emerging industries in the world .In order to explore the enrichment degree, occurrence state and sedimentary environment of rare earth elements in late Permian coal in western Henan, 20 stratified coal samples from No.21 coal in Huixiang mining area in western Henan were taken as the main research object. The rare earth elements and major elements in stratified coal samples were measured by ICP-MS and XRF, and the content characteristics and enrichment degree of rare earth elements in coal samples were discussed. The occurrence state and sedimentary environment of rare earth elements in samples were discussed by correlation analysis and characteristic parameters .The results show that the mass concentration of REY is 35.29-133.61 μg/g, and the average concentration is 79.14 μg/g, which is slightly higher than the average concentration of REY in the world coal, but obviously lower than the average concentration of REY in China coal. The REY content is low, and LREY is mainly enriched. There is a significant positive correlation between REY and ash content (Ad), SiO2, Al2O3 and other major oxides in the No.21 coal of Huixiang mining area, indicating that REY mainly occurs in clay minerals .The negative anomalies of Ce and Eu elements and slight positive anomalies of (Gd/Gd)N* in the samples in the study area indicate that the study area is mainly affected by terrigenous sources and the coal forming environment is a weakly acidic reducing environment.

Zhong Zhang, Chaowen Huang, Zilu Xu et al.

Notch high cycle fatigue (HCF) properties and microcrack initiation behavior of Ti-55531 alloy with a multilevel lamellar microstructure under various notch radii were systematically investigated. Results indicate that the reduction of notch root radius significantly promotes fatigue microcrack initiation, and then dramatically reduces the HCF life and strength of the alloy. Cyclic deformation of the alloy is mainly controlled by the slipping and deformation twinning in α plates. The primary fatigue crack initiation micro-mechanism is α/β interface cracking induced by slipping and twinning at all notch HCF specimens. Moreover, the volume fraction of twinning would increase with decreasing of the notch root radius. Interestingly, when the notch root radius is the smallest (R = 0.14 mm) and the stress concentration factor is the largest (Kt = 4), in addition to slipping and twinning, basal stacking faults promoting the cracking of α/β interface could be another crucial HCF microcrack initiation mechanism of the alloy. Furthermore, with decreasing of notch root radius and increasing of stress concentration factor, the size of cycle plastic deformation zone at notch root gradually reduces to the size of α colony and even the α plate. Therefore, the order of influential degree of three different levels microstructure on the crack initiation mechanism of notched HCF can be arranged as α plate > α colony > β GB.

Iztok Fister, Dušan Fister, Iztok Fister et al.

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method's potential and opened the door for further extension.

MA Liang, YANG Jing, WANG Ji-ping et al.

The annular Al2O3–B4C thin wall tube with the wall thickness of near 0.7 mm was successfully sintered by powder injection molding combined with the solvent degreasing and thermal degreasing, using the multi-component wax-based binder. The results show that, when the paraffin mass fraction is 45%, the feedstock has the lower viscosity and the better bending strength. When the solid phase volume fraction is 58%, the feedstock has the good performance in the premise of low viscosity. When the sintering temperature is in the range of 1550 ℃ to 1650 ℃, the relative density and the bending strength of the pellets increase with the increase of temperature. When the sintering temperature reaches 1650 ℃, the density and strength of the pellets begin to decrease slightly, the density increases with the increase of the B4C particle size, and the bending strength increases first and then decreases with the increase of particle size.

Zarina Kurbatova, Yaroslav Golubev, Vladimir Kovalenko et al.

In software engineering, a great number of new approaches are being actively researched, and a lot of tools are being developed based on them. These tools require a framework for their creation and an opportunity to be used by potential developers. Modern IDEs provide both. In this paper, we describe the main capabilities of the IntelliJ Platform that could be useful for researchers that are developing code analysis tools. To illustrate the benefits of using the platform, we describe several use cases that researchers might be interested in: mining software data, running machine learning models on code, recommending refactorings, and visualizing data in the IDE. We provide several examples of existing plugins that implement these cases. Finally, to make it easier to start working with the platform, we develop and provide simple plugins for each use case that could serve as a template for a new project.

Pushp, Satish Chand

Knowledge discovery in databases aims at finding useful information, which can be deployed for decision making. The problem of high utility itemset mining has specifically garnered huge research focus in the past decade, as it aims to find the patterns from the databases that conform to an objective utility function. Several algorithms exist in literature to mine the high utility items from the databases; however, most of them require large execution time and have high memory consumption. In this paper, we propose a new algorithm, R-Miner, based on a novel data structure, called the residue maps, that stores the utility information of an item directly and is used for the mining process. Several experiments are undertaken to assess the efficacy of the proposed algorithm against the benchmark algorithms. The experimental results indicate that the R-Miner algorithm outperforms the state-of-the-art mining algorithms.

Mohammad Kasra Habib, Stefan Wagner, Daniel Graziotin

Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natural language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved, which results in building a product different from the expected one. Previous work proposed to enhance the quality of the software requirements detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is solved by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.