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In this study, we optimized the traditional composition of AISI 8630 steel and evaluated its corrosion resistance through a series of tests. We conducted corrosion tests in a 3.5% NaCl solution and performed a 720 h fixed-load tensile test in accordance with the NACE TM-0177-2016 standard to assess sulfide stress corrosion cracking (SSCC). To analyze the corrosion products and the structure of the corrosion film, we employed X-ray diffraction and transmission electron microscopy. The corrosion rate, characteristics of the corrosion products, structure of the corrosion film, and corrosion resistance mechanism of the material were investigated. The results indicate that the optimized AISI 8630 material demonstrates excellent corrosion resistance. After 720 h of exposure, the primary corrosion products were identified as chromium oxide, copper sulfide, iron oxide, and iron–nickel sulfide. The corrosion film exhibited a three-layer structure: the innermost layer with a thickness of 200–300 nm contained higher concentrations of alloying elements and formed a dense, cohesive rust layer that hindered the diffusion of oxygen and chloride ions, thus enhancing corrosion resistance. The middle layer was thicker and less rich in alloying elements, while the outer layer, approximately 300–400 nm thick, was relatively loose. [ABSTRACT FROM AUTHOR]

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy's components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm−3)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys. [ABSTRACT FROM AUTHOR]

As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions. [ABSTRACT FROM AUTHOR]

Scintillators in general and organic liquid scintillator specifically are widely used as a medium for the detection of charged particles for numerous applications in science, medicine, engineering, and other areas. The composition of the scintillator affects not only its direct performance characteristics, but also the overall cost. Optimization of this composition provides the ability to design particle detectors with an optimized light yield and emission spectra of the detection medium while optimizing the expenses at the same time. This article describes work on toluene-based liquid scintillator component optimization, where PPO is used as a fluor and POPOP as a shifter. The light yield vs. concentration and the changes in the output spectra will be presented. The empirical fit of the output spectrum using the measured contributions of the components is discussed. Further plans include the light attenuation measurements for different compositions. [ABSTRACT FROM AUTHOR]

Sinter composition optimization is an important process of iron and steel companies. To increase companies' profits, they often rely on innovative technology or the workers' operating experience to improve final productions. However, the former is costly because of patents, and the latter is error-prone. In addition, traditional linear programming optimization methods of sinter compositions are inefficient in the face of large-scale problems and complex nonlinear problems. In this paper, we are the first to propose a regressive convolutional neural network (RCNN) approach for the sinter composition optimization (SCORN). Our SCORN is a single input and multiple outputs regression model. Sinter plant production is used as the input of the SCORN model, and the outputs are the optimized sintering compositions. The SCORN model can predict the optimal sintering compositions to reduce the input of raw materials consumption to save costs and increase profits. By constructing a new neural network structure, the RCNN model is trained to increase its feature extraction capability for sintering production. The SCORN model has a better performance compared with several regressive approaches. The practical application of this predictive model can not only formulate corresponding production plans without feeding materials but also give better input parameters of sintered raw materials during the sintering process. [ABSTRACT FROM AUTHOR]

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum-lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy's components, a more generalized modulus prediction model for Al-Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al-Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm -3 )), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al-Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al-Li alloys.

Composition optimization of the asphalt mixture of pavement is one effective measures to reduce the harm of traffic noise. To improve the noise reduction effect of porous asphalt mixture (PAM) and promote the recycling of crumb rubber in highway engineering, the preparation parameters of high-viscosity modified asphalt for PAM were optimized in this study, and the mixture gradation was optimized based on the unbalance force and contact force of mixed aggregate. The effects of crumb rubber content and particle size on the damping performance and dynamic shear modulus of the mixture were studied. The effects of different preparation parameters on the performance of the PAM were comprehensively evaluated based on the orthogonal test, and preparation parameters of PAM were recommended. The results show that with the increase of crumb rubber content, the damping ratio of the mixed aggregate increases gradually. The addition of crumb rubber is conducive to improving the damping performance and toughness of the PAM, but it has an adverse impact on the bearing capacity. Under the condition of low strain, the damping ratio of the mixed aggregate containing 2–5 mm crumb rubber is 1.2–5 times that of the mixed aggregate containing 0.6–1 mm crumb rubber. The recommended optimum content of crumb rubber in PAM is 4%, and the optimum particle size of alternative aggregate is 2.36–4.75 mm. The significant factors affecting Marshall stability are rubber particle content, asphalt aggregate ratio, mixing temperature, compaction times, and forming temperature. The rational utilization of crumb rubber in PAM is of positive significance to promoting the green development of highway construction and the harmless treatment of waste resources. [ABSTRACT FROM AUTHOR]

The aim of this work is to optimize the composition of a two-component silicone enamel consisting of an aluminum pigment and a polyphenylsiloxane polymer to obtain the maximum dispersion of the pigment in the coating. The following products were used as surfactants: AS-1, PEPA, and Telaz. To assess the effect of surfactants on the dispersion of the pigment, computer-optical microscopy was used. The results of the studies showed that all the studied surfactants cause an improvement in the dispersion of the pigment. According to the degree of influence on the dispersion of the pigment, surfactants can be arranged in a row: PEPA > Telaz > AS-1. When the PEPA content in the enamel is 0.25 g/dm3, a decrease in the diameter of the pigment particles by 46% (from 26 to 14 microns) is recorded, with an increase in their specific amount by 2 times (from 258 to 550 pcs). Optimal enamel compositions allow a reduction in the corrosion rate by 11 times (from 0.6 to 0.053 mm/year) and improvement to the decorative properties of coatings (roughness, gloss, etc.). The effectiveness of the AS-1 product (obtained from oil refining waste) as a dispersant additive in silicone enamel has been proven. [ABSTRACT FROM AUTHOR]