The objective of this study was to investigate the potential for extracting lithium and aluminum from coal fly ash in depth. The activation-sintering method was used to study how factors like activators, sintering agents, sintering time, leaching concentration, and temperature affect the leaching of lithium and aluminum. A 1:2 Na<sub>2</sub>CO<sub>3</sub> activator was proportioned with coal fly ash for primary incineration at a temperature of 1000 °C for 30 min, after which a 3:1 Na<sub>2</sub>SO<sub>4</sub> sintering agent was added to be proportioned with coal fly ash for secondary incineration at a temperature of 1000 °C for 30 min. The temperature was then increased to 400 °C for 60 min, after which the lithium and aluminum were leached with a 1% H<sub>2</sub>SO<sub>4</sub> solution at 80 °C for 60 min. The leaching process was highly effective, with the lithium and aluminum leached out at rates of approximately 80%.
Directed energy deposition (DED) additive manufacturing (AM) enables the production of components at a high deposition rate. For certain alloys, interpass temperature requirements are imposed to control heat accumulation and microstructure transformation, as well as to minimize distortion under varying thermal conditions. A typical strategy to comply with interpass temperature constraints is to increase the interpass dwell time, which can lead to an increase in the total deposition time. This study aims to develop an optimized tool path that ensures interpass temperature compliance and reduces overall deposition time relative to the conventional sequential deposition path during the DED process. To evaluate this, a compact analytic thermal model is used to predict the thermal history during laser-based directed energy deposition (DED-LB/M) hot wire (lateral feeding) of ER100S-G, a welding wire equivalent to high yield steel. A greedy algorithm, integrated with the thermal model, identifies a tool path order that ensures compliance with the interpass requirement of the material while minimizing interpass dwell time and, thus, the total deposition time. The proposed path planning algorithm is validated experimentally with in situ temperature measurements comparing parts fabricated with the baseline (sequential) deposition path to the modified path (resulting from the greedy algorithm). The experimental results of this study demonstrate that the proposed path planning algorithm can reduce the deposition time by 9.2% for parts of dimensions 66 mm × 73 mm × 16.5 mm, comprising 15 layers and a total of 300 beads. Predictions based on the proposed path planning algorithm indicate that additional reductions in deposition time can be achieved for larger parts. Specifically, increasing the (experimentally validated) part dimension perpendicular to the deposition direction by five-times is expected to result in a 40% reduction in deposition time.
With the increasing number of mineral crystal excavations in our country, the analysis of resources, types and origins of these mineral crystal samples has important guiding significance for the mining and development of minerals in China. Combined with the mineral crystal samples found in Qiannan, Guizhou, the internal sequence and species composition of ore crystals were analyzed by measuring instruments such as microscopes and spectrum analyzers. Then, the types, related sequences and types of mineral crystals were traced, and their origins were identified. The research results show that the sample mineral crystals are mainly chalcopyrite and siderite, and there are major elements such as copper and iron in the crystals, as well as small amounts of elements such as antimony and arsenic. By tracing the origin and formation of these ore crystals, it is concluded that the environment in which the crystals are located is a place containing more hydrothermal fluids on the surface. These mineral crystals have undergone high-intensity crustal movement to form a relatively complex geological structure. The origin traceability comparison shows that it is in line with the origin of the samples.
Khouloud Oueslati, Gabriel Laberge, Maxime Lamothe
et al.
Defect reduction planning plays a vital role in enhancing software quality and minimizing software maintenance costs. By training a black box machine learning model and "explaining" its predictions, explainable AI for software engineering aims to identify the code characteristics that impact maintenance risks. However, post-hoc explanations do not always faithfully reflect what the original model computes. In this paper, we introduce CounterACT, a Counterfactual ACTion rule mining approach that can generate defect reduction plans without black-box models. By leveraging action rules, CounterACT provides a course of action that can be considered as a counterfactual explanation for the class (e.g., buggy or not buggy) assigned to a piece of code. We compare the effectiveness of CounterACT with the original action rule mining algorithm and six established defect reduction approaches on 9 software projects. Our evaluation is based on (a) overlap scores between proposed code changes and actual developer modifications; (b) improvement scores in future releases; and (c) the precision, recall, and F1-score of the plans. Our results show that, compared to competing approaches, CounterACT's explainable plans achieve higher overlap scores at the release level (median 95%) and commit level (median 85.97%), and they offer better trade-off between precision and recall (median F1-score 88.12%). Finally, we venture beyond planning and explore leveraging Large Language models (LLM) for generating code edits from our generated plans. Our results show that suggested LLM code edits supported by our plans are actionable and are more likely to pass relevant test cases than vanilla LLM code recommendations.
Engineering a sustainable world requires to consider various systems that interact with each other. These systems include ecological systems, economical systems, social systems and tech-nical systems. They are loosely coupled, geographically distributed, evolve permanently and generate emergent behavior. As these are characteristics of systems of systems (SoS), we discuss the engi-neering of a sustainable world from a SoS engineering perspective. We studied SoS engineering in context of a research project, which aims at political recommendations and a research roadmap for engineering dynamic SoS. The project included an exhaustive literature review, interviews and work-shops with representatives from industry and academia from different application domains. Based on these results and observations, we will discuss how suitable the current state-of-the-art in SoS engi-neering is in order to engineer sustainability. Sustainability was a major driver for SoS engineering in all domains, but we argue that the current scope of SoS engineering is too limited in order to engineer sustainability. Further, we argue that mastering dynamics in this larger scope is essential to engineer sustainability and that this is accompanied by dynamic adaptation of technological SoS.
Process mining has grown popular today given their ability to provide managers with insights into the actual business process as executed by employees. Process mining depends on event logs found in process aware information systems to model business processes. This has raised the need to develop event log standards given that event logs are the entry point to any process mining project. One of the main challenges of event logs and process mining in general as was mentioned by the IEEE task force on process mining deals with the finding, merging and cleaning event data.This resulted in having multiple event log standards with different features. This paper attempts to propose a new unified standard for event logs that enriches the results of process mining without the need to tailor event logs for each process mining project.
Due to the rapid development of science and technology, the importance of imprecise, noisy, and uncertain data is increasing at an exponential rate. Thus, mining patterns in uncertain databases have drawn the attention of researchers. Moreover, frequent sequences of items from these databases need to be discovered for meaningful knowledge with great impact. In many real cases, weights of items and patterns are introduced to find interesting sequences as a measure of importance. Hence, a constraint of weight needs to be handled while mining sequential patterns. Besides, due to the dynamic nature of databases, mining important information has become more challenging. Instead of mining patterns from scratch after each increment, incremental mining algorithms utilize previously mined information to update the result immediately. Several algorithms exist to mine frequent patterns and weighted sequences from incremental databases. However, these algorithms are confined to mine the precise ones. Therefore, we have developed an algorithm to mine frequent sequences in an uncertain database in this work. Furthermore, we have proposed two new techniques for mining when the database is incremental. Extensive experiments have been conducted for performance evaluation. The analysis showed the efficiency of our proposed framework.
To improve the microstructure of compound casting Al/Mg bimetallic interface and optimize the bonding performance, the vibration assisted treatment and the Ni interlayer coating treatment were combined. After the composite treatment, the thickness of the Al/Mg bimetallic interface decreased significantly, only 8.13% of the original interface thickness. The original large amount of brittle and hard Al–Mg intermetallic compounds (IMCs) no longer existed, and were replaced by the (Mg–Ni) layer dominated by Mg3Ni2Al, the Al3Ni layer and the Ni solid solution layer. The shear crushing effect and the convective stirring effect of the vibration assisted treatment provided more stable and good metallurgical bonding for the Al/Mg bimetallic interface after introducing Ni interlayer. On this basis, combined with the second phase strengthening effect of the newly precipitating Mg3Ni2Al, the bonding strength of the Al/Mg bimetallic interface significantly improved, from 35.47 MPa to 56.12 MPa, with an increase of 58.22%.
In this study, the microstructure and mechanical properties of the commercial magnesium (Mg) ZK60 alloy were conducted to promote the application of Mg alloys. Scanning electron microscopy, tensile and compression tests, electron back scattering diffraction, transmission electron microscope and visco-plasticity self-consistent methods were employed to analyze the microstructure evolution and the strengthening and toughening mechanisms. By direct extrusion in the as-cast state, a bimodal microstructure was obtained, and the mechanical properties were enhanced. The ZK60 alloy, with a bimodal grain structure, exhibited a good combination of strength and ductility. The tensile yield strength (TYS), ultimate tensile strength (UTS), compressive yield strength (CYS) and fracture elongation (FE) reached 272 MPa, 347 MPa, 289 MPa, and 36.2%, respectively. Compared with the traditional extrusion ZK60 alloy, the increment in TYS, UTS, CYS, and FE was 15 MPa, 31 MPa, 25 MPa and 8.6%, respectively. The formation of the bimodal structure in the ZK60-CE sample is attributed to the combination of the PSN and Zener pinning effects. The improvement in strength is mainly attributed to the residual dislocations in the un-DRXed grains and the good ductility is ascribed to the activation of the non-basal slip. This study provides a low-cost and efficient strategy for the preparation of the bimodal structure by regulating the pre-extrusion microstructure to improve mechanical properties.
The influence of low-frequency electromagnetic casting (LFEC) and Sc addition as variables on the microstructure, properties, and deformation behavior of 7A36 aluminum alloy has been systematically examined. The results have predicted that the LFEC could make the grains of the as-cast alloy finer by electromagnetic stirring and forced convection. By the addition of Sc, the average diameters of the equiaxed grains have been reduced and the intensity of the fiber texture has become weakened. The fine and continuous η′ precipitates on the grain boundaries (GBs) instead of the coarse large-sized discontinuous η′ precipitates, which results in a more uniform distribution of plastic deformation and which is beneficial for improving plasticity. By adding Sc and applying LFEC, the large size η′ internal grain precipitated phase decreased slightly. The fine nano-sized Al3(Sc, Zr) phase particles were dispersed in the matrix. As the dislocation movement had to travel less distance, that's why this process consumed less energy. These features also reduced the strain difference within the grain and near the grain boundary, which result in the increase of EL. The excellent mechanical properties of 7A36 aluminum as extruded plates were obtained when it was aged at 120 °C for 8 h by Sc micro-alloying and applying LFEC. The yield strength (YS) and ultimate tensile strength (UTS) remained at 738.3 MPa and 767.0 MPa, respectively, while the elongation (EL) to failure 6.5% is increased by 160% than the value of direct chill casting.
Ondřej Červinek, Heinz Pettermann, Melanie Todt
et al.
The development of additive manufacturing technologies enables the production of a new type of porous materials for the absorption of mechanical energy. These are, for example, metallic lattice structures produced by laser powder bed fusion. The structures can be made from a wide range of alloys, achieve high specific energy absorption, and can be manufactured as hybrid parts with conventional bulk components. To effectively develop lattice structures, it is necessary to complement experimental tests with simulations using the finite element method (FEM) performed under conditions of increased loading velocities. Therefore, this study focuses on the development of the FEM modelling strategy that reflects the effect of strain rate sensitivity of the base material (SS316L) and the most significant geometrical imperfections of the manufacturing process. The strain rate is reflected by the Cowper-Symonds constitutive law, which parameters are determined by the dynamic tensile test on Hopkinson split bars. The imperfections are captured by optical digitalization. The significance of the Cowper-Symonds parameters and geometric imperfections are studied independently, whereas agreement with the experiment is observed. Tests are performed for several lattice structures with different strut orientations and velocities to evaluate the versatility of the proposed approaches. A good correlation between computational and experimental results in terms of energy absorption is found for structures with an experimentally determined strut diameter and the proposed Cowper-Symonds input parameters.
Simon N. Lekakh, Asebi Bofah, Larry A. Godlewski
et al.
High silicon and molybdenum (<i>SiMo</i>) ductile iron is commonly used for car exhaust systems, and its micro-structural dispersity depends on intrinsic parameters, which include alloy composition and inoculation efficiency, as well as extrinsic factors, such as casting wall thickness and molding material, which define cooling rate during solidification. Micro-structural dispersity is referred to as the degree of heterogeneity of sizes of structural constituencies within the microstructure. A variation in the micro-structural dispersity could impact the high temperature performance of <i>SiMo</i> ductile iron during static oxidation and transient thermo-mechanical loading conditions. In this study, static high temperature tests were performed on <i>SiMo</i> ductile iron solidified in a casting with varying wall thicknesses from 5 mm to 100 mm. The faster solidified specimens (taken from near chilled casting surfaces) had extremely high micro-structural dispersity as compared to the thicker section samples. After thermal exposure, each of the samples were characterized using 2D sections and 3D µCT images, and the results indicated an order of magnitude difference in graphite phase dispersity. The surface degradation was quantified after static oxidation experiments were implemented at temperature intervals between 650 °C and 800 °C. Non-destructive µCT 3D analysis and SEM/EDS were performed on cross sections and used to quantify the scale topology and structure. Carbon analysis was used to decouple the scale formation and decarburization phenomena that occurred within the samples. These methods enabled the quantification of the oxidation of the <i>SiMo</i> cast iron with different micro-structural dispersity levels after being exposed to high temperature static oxidation. Additionally, the complex material behavior during oxidation-assisted transient thermo-mechanical loading will be presented in a separate article.
The effect of Cu doping on the microstructure and functional properties of polycrystalline Co–Ni-Ga-Cu shape memory alloy is investigated with the Cu composition ranging from 0.3 to 4.5 at.%. For the miniaturization applications in actuators and sensors, the alloy with 3 at.% Cu is selected to fabricate one-dimensional microwires. The results reveal that Cu promotes the growth of the γ phase precipitates, accompanied by the decrease of the martensitic transformation temperature in the bulk Co–Ni-Ga-Cu samples. At low Cu doping, a reversible strain of 4% is obtained. By increasing Cu to 4.5%, a compressive reversible strain can reach at least 2.5% at room temperature. During the compression process, the adiabatic elastocaloric effect of a 3.3 K temperature change is also achieved. Furthermore, the polycrystalline microwire displays a great tensile superelasticity with a larger reversible strain of 5% in a temperature range varying from 223 to 373 K, which is the best among Co–Ni-Ga polycrystalline alloys. The shape memory effect of the microwire with a 3% strain output ranging from 123 K to 353 K is achieved as well. The good multifunctional properties in the Co–Ni-Ga-Cu shape memory alloy bulks and microwires lay a foundation for their applications in sensing, actuating and solid-state refrigeration fields.
Twinning is a critical deformation mode in Mg alloys. Understanding deformation twinning (DT) is essential to improving mechanical properties of Mg alloys. To address the experimentally observed conspicuous hardening effects in Mg-1.8Zn-0.2Ca alloys, interactions between the {10–12} twin boundaries (TBs) and solute clusters in Mg-Zn-Ca alloys were examined via molecular dynamics (MD) simulations. We find that the Zn/Ca-containing clusters show different hindering effects on TBs and an increment in the applied shear stress of 100 MPa is required to accomplish the interaction between the boundary and the cluster with Ca content > 50 at%. The cluster hardening effects on twinning are positively correlated to the Ca content and the size of the clusters in Mg-Zn-Ca alloys.
Graph association rule mining is a data mining technique used for discovering regularities in graph data. In this study, we propose a novel concept, {\it path association rule mining}, to discover the correlations of path patterns that frequently appear in a given graph. Reachability path patterns (i.e., existence of paths from a vertex to another vertex) are applied in our concept to discover diverse regularities. We show that the problem is NP-hard, and we develop an efficient algorithm in which the anti-monotonic property is used on path patterns. Subsequently, we develop approximation and parallelization techniques to efficiently and scalably discover rules. We use real-life graphs to experimentally verify the effective
A uniaxial compression test and scanning/transmission electron microscopy observations were performed to investigate the differences in mechanical behavior and deformed microstructure between focused ion beam-manufactured [1 1 1]- and [0 0 1]-oriented austenite micro-pillars with 5 μm diameter from duplex stainless steel. After yielding, the strain hardening of two orientation micro-pillars increased sharply as a result of the formation of a microband, namely microband-induced plasticity, MBIP. The same phenomenon could be observed in a [0 0 1]-oriented pillar due to the activation of the secondary slip system, while slight strain hardening behavior was observed in the [1 1 1] orientation because of the refinement of the microband. Furthermore, the trend of the calculated strain hardening rates of both [1 1 1]- and [0 0 1]-oriented micro-pillars were in good agreement with the experimental data. This study proved that MBIP can be helpful for the mechanical property enhancement of steels.
Networks are used as highly expressive tools in different disciplines. In recent years, the analysis and mining of temporal networks have attracted substantial attention. Frequent pattern mining is considered an essential task in the network science literature. In addition to the numerous applications, the investigation of frequent pattern mining in networks directly impacts other analytical approaches, such as clustering, quasi-clique and clique mining, and link prediction. In nearly all the algorithms proposed for frequent pattern mining in temporal networks, the networks are represented as sequences of static networks. Then, the inter- or intra-network patterns are mined. This type of representation imposes a computation-expressiveness trade-off to the mining problem. In this paper, we propose a novel representation that can preserve the temporal aspects of the network losslessly. Then, we introduce the concept of constrained interval graphs (CIGs). Next, we develop a series of algorithms for mining the complete set of frequent temporal patterns in a temporal network data set. We also consider four different definitions of isomorphism to allow noise tolerance in temporal data collection. Implementing the algorithm for three real-world data sets proves the practicality of the proposed algorithm and its capability to discover unknown patterns in various settings.