As a candidate cladding material for fourth-generation nuclear energy systems, the microstructure and properties of oxide dispersion strengthened 316L (ODS-316L) steel are significantly affected by heat treatment (HT) processes. This paper highlights the influence mechanism of HT temperature on the microstructural evolution and corrosion resistance of ODS-316L alloys prepared by laser powder bed fusion (LPBF) technology. The results show that HT can significantly regulate the microstructure of ODS-316L alloys. After heat treatment, the ODS-316L alloy consists of a single austenitic phase, indicating no phase transformation occurs before and after heat treatment. With the increase of HT temperature, the fish-scale molten pool morphology characteristic of the as-fabricated ODS-316L alloy disappears, and recrystallized grains coarsen. When the HT temperature reaches 1150 °C, the grain size reaches a maximum of 43.1 μm, the proportion of low-angle grain boundaries (LAGBs) is the highest at 76.7 %, and the Kernel Average Misorientation (KAM) value drops to a minimum of 0.28°. At the HT temperature of 1150 °C, the alloy exhibits excellent corrosion resistance: the self-corrosion potential (Ecorr) reaches a maximum of −0.255 V, the self-corrosion current density (Icorr) decreases to a minimum of 7.797 × 10−7 A/cm2, the pitting potential (Ep) reaches a maximum of 0.632 V, and the passivation interval reaches a maximum of 0.659 V. Moreover, two types of nano-oxide particles, Y–Si–O and Si–O, are uniformly distributed in the 316L matrix heat-treated at 1150 °C, which helps to reduce the self-corrosion current density, thereby improving its corrosion resistance. Furthermore, the correlation between nano-oxides and corrosion resistance is elaborate. This study provides a theoretical basis and reference value for further optimizing the microstructure and corrosion resistance of nuclear-grade ODS-316L alloy components.
Blast-induced ground vibration (BIGV) is one of the detrimental environmental consequences of blasting operations in mining and civil engineering. Hence, accurate prediction of BIGV is highly imperative. Therefore, different novel artificial intelligence (AI) methods such as Bayesian regularized neural network (BRNN), Bayesian regularized causality-weighted neural network (BRCWNN) and Z-number-based Bayesian regularized causality-weighted neural network (Z-BRCWNN) are proposed in this study for the reliable prediction of BIGV in a dolomitic marble quarry using the obtained field data. The outcome of the proposed models is subjected to rigorous statistical analyses. The outcome of analyses revealed that the Z-BRCWNN model outperformed the other models with 70%, 82% and 82% threshold statistic values evaluated at the 5%, 10% and 15% confidence levels for the testing phase and 63%, 91% and 91% threshold values for the validation phase evaluated at the same levels as above. The sensitivity analysis conducted revealed that the distance from the measuring point to the blasting point (DI) has the highest influence on BIGV.
Mahan Ghosh, Nandika Anne D'Souza, Yunwei Xu
et al.
Lightweighting has been a key goal for engineers and designers, with lattice structures widely explored as the building blocks for structural components. Cellular structure-inspired lattice truss frame designs made of struts, have been extensively studied. All plate-based lattice designs have superior mechanical performance. Limits in scalability occur from formation of closed pockets limiting uv curing in processes like stereolithography (SLA). We examine hybrid frame and plate body-centered cubic-simply cubic (BCC-SC) lattices under compression. Unit cells and scaled up lattice exhibit an increase in yield stress and modulus with the addition of plates. The loading direction on the hybrid frame and plate unit cells affected the magnitude of improvement. Simulations and measurements indicated that the optimal lightweight lattice was determined to be when two plates were placed opposite each other with plates buttressing the struts, inhibiting buckling of the struts aligned with the loading direction. This lattice resulted in 63% improvement in specific modulus, a 137% improvement in specific yield point, and a 360% improvement in specific energy absorption (SEA) and the scaled up 4 × 4 × 4 scaled-up structures, showed a 107%, 148%, and 297% in specific modulus, specific yield point, and SEA, respectively A combined stretching-bending behavior was identified in optimal orientations reflecting the delayed buckling mechanism paired to a rising stress-strain curve past the elastic yield indicating bending resistance. The mass moment of inertia was found to be a key parameter correlating optimum orientation for the same number of plates added to the BCC-SC frame.
This study aims to accurately perceive the position and posture information of hydraulic supports in a disturbed environment. To address this, a precise perception method for the position and posture of hydraulic supports based on multi-sensor fusion was proposed. Firstly, nine-axis attitude sensors were deployed on four components of the hydraulic support, including top beam, shield beam, rear linkage, and base, to measure roll, pitch, and yaw angles using gyroscopes, accelerometers, and magnetometers. Then, the position and posture data was filtered using the Unscented Kalman Filter (UKF) algorithm and Improved Gradient Descent (IGD) algorithm (IGD-UKF algorithm), reducing interference from disturbance factors. Finally, an adaptive weighted fusion algorithm was employed to merge the filtered yaw and roll angle data of the top beam and base of hydraulic supports, eliminating data deviations caused by external vibrations, noise, and other factors. Perception experiments were conducted on the position and posture of top beam, shield beam, rear linkage, and base under various working conditions. The disturbances included the lowering and raising of top beam and base, as well as left-leaning, right-leaning, left-deviating and right-deviating of hydraulic supports. The study found that the data curves processed by the IGD-UKF algorithm exhibited smoother fluctuations, significantly suppressing oscillations and reducing amplitude. The yaw angle error of hydraulic supports ranged from 0.001 8° to 0.025 1°, with an average absolute error of 0.004 8°. The roll angle error ranged from 0.001 4° to 0.028 1°, with an average absolute error of 0.004 7°. The results indicate that the precise perception of the position and posture of hydraulic supports in a disturbed environment is achieved.
Dimitri P. Papazoglou, Amy T. Neidhard-Doll, Margaret F. Pinnell
et al.
In an effort to contribute to the ongoing development of ASTM standards for additively manufactured metal lattice specimens, particularly within the field of medicine, the compressive and tensile mechanical properties of biomimetic lattice structures produced by laser powder bed fusion (L-PBF) using Ti-6Al-4V feedstock powder were investigated in this research. The geometries and porosities of the lattice structures were designed to facilitate internal bone growth and prevent stress shielding. A thin strut thickness of 200 µm is utilized for these lattices to mimic human cancellous bone. In addition to a thin strut size, two different strut geometries were utilized (cubic and body-centered cubic), along with four different pore sizes (400, 500, 600, and 900 µm, representing 40–90% porosity in a 10 mm cube). A 10 mm<sup>3</sup> cube was used for compression testing and an experimental pin-loaded design was implemented for tensile testing. The failure mode for each specimen was examined using scanning electron microscopy (SEM). Lattice structures were compared to the mechanical properties of human cancellous bone. It was found that the elastic modulus of human cancellous bone (10–900 MPa) could be matched for both the tensile (92.7–129.6 MPa) and compressive (185.2–996.1 MPa) elastic modulus of cubic and body-centered cubic lattices. Body-centered cubic lattices exhibited higher compressive properties over cubic, whereas cubic lattices exhibited superior tensile properties. The experimental tensile specimen showed reacquiring failures close to the grips, indicating that a different tensile design may be required for consistent data acquisition in the future.
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.
Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach
et al.
Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.
Educational Process Mining (EPM) is a data analysis technique that is used to improve educational processes. It is based on Process Mining (PM), which involves gathering records (logs) of events to discover process models and analyze the data from a process-centric perspective. One specific application of EPM is curriculum mining, which focuses on understanding the learning program students follow to achieve educational goals. This is important for institutional curriculum decision-making and quality improvement. Therefore, academic institutions can benefit from organizing the existing techniques, capabilities, and limitations. We conducted a systematic literature review to identify works on applying PM to curricular analysis and provide insights for further research. We reviewed 27 primary studies published across seven major databases. Our analysis classified these studies into five main research objectives: discovery of educational trajectories, identification of deviations in student behavior, bottleneck analysis, dropout / stopout analysis, and generation of recommendations. Key findings highlight challenges such as standardization to facilitate cross-university analysis, better integration of process and data mining techniques, and improved tools for educational stakeholders. This review provides a comprehensive overview of the current landscape in curricular process mining and outlines specific research opportunities to support more robust and actionable curricular analyses in educational settings.
Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen
et al.
The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.
The paper discusses the issues of implementation of the Mineral Resources Strategy of the Russian Federation till 2035. The main objective is to develop a coordinated regional strategy that takes into account the planned structural changes in industry for the implementation of priority programs of engineering, metallurgy, industry of building materials and other industries, based on Russian and the world trends of minerals consumption in conditions of deterioration of quality and availability of raw materials. The paper formulates the main regional organizational and technological conditions to form the mineral resources programs with regard to the main trends of development and predicted technological changes in the mining industry.
Daniel Restrepo-Echeverri, Jovani Alberto Jiménez-Builes, John Willian Branch-Bedoya
En este artículo se presenta un modelo para la implementación de los celulares como un componente funcional de la robótica educativa. Con esto se puede lograr una masificación de las prácticas educativas de la robótica, y un planteamiento de soluciones innovadoras con el uso de componentes cotidianos. Lo anterior se constituye en un modelo de fácil adaptación para desarrollar habilidades específicas de los estudiantes en las áreas STEM con una baja inversión. Se evidenció como resultado, que la robótica facilita la posibilidad de introducir la tecnología en los procesos de enseñanza y aprendizaje por medio de estos kits que poseen sensores, mecanismos, piezas y características que pueden acoplarse e integrase con un celular para armar un robot funcional. A través de un cuestionario en línea sobre la integración de robótica educativa y celulares, se constató el interés que tienen los estudiantes de ingeniería y docentes, para que sus instituciones de educación superior incluyan la robótica en sus procesos formativos, contribuyendo de esta forma a la preparación para afrontar los retos de la línea de Educación 4.0 dentro del contexto de la Industria 4.0.
A reasonable flow field in the continuous casting mold is beneficial to produce high quality billets, and the design of the nozzle parameters of the mold is key to regulating the flow behavior of molten steel. Through combining the numerical simulation and physical experiments and taking SEN immersion depth and inner diameter as indicators, the flow behavior of molten steel in the mold during high-speed casting of a 160 mm × 160 mm billet was investigated in detail, and the nozzle parameters were optimized. The results demonstrate that, compared with the inner diameter of the nozzle, the immersion depth has a significant influence on the impact depth of molten steel. On the premise of ensuring that the velocity distribution of molten steel on the surface of the mold is uniform and the impact range inside is appropriate, the inlet immersion depth after optimization is 100–120 mm and the inner diameter is 40 mm. The corresponding impact depth is 605–665 mm, and the maximum velocity of molten steel on the mold surface is between 0.04 and 0.045 m/s. Additionally, the results of the physical experiment and numerical simulation reveal that the optimized nozzle parameters can adapt well to the continuous casting process with a high casting speed.
Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities comprising the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.