| 1. |
- Li, Ziyu, et al.
(författare)
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Enhancing Rheocasting Process Control with AI-based Systems
- 2023
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Konferensbidrag (refereegranskat)abstract
- Semisolid casting has emerged as an attractivealternative to conventional casting methods due to its potentialto yield superior mechanical properties, reduce environmentalpollution, and decrease production costs. However, optimizingprocess parameters and controlling the casting process remainschallenging. Process control largely relies on human expertise,associated with significant time and cost expenditures. Inresponse, this study presents a third-circle research project toinvestigate the correlation between the casting process and thesolidification process. The study proposes leveraging AI technologyto digitize the entire process control, thereby increasing thereliability and stability of cast products’ quality. The researchwill focus on understanding the key factors influencing thecasting process and developing an AI-based decision supportsystem to aid in process parameter selection and optimization.The outcomes of this study are expected to contribute to thedevelopment of more reliable and efficient semisolid castingprocesses.
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| 2. |
- Li, Ziyu, et al.
(författare)
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On the Possibility of Replacing Scheil-Gulliver Modeling with Machine Learning and Neural Network Models
- 2023
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Ingår i: Solid State Phenomena. - : Trans Tech Publications. ; , s. 157-163
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Bokkapitel (refereegranskat)abstract
- Resource-efficient manufacturing is a foundation for sustainable and circular manufacturing. Semi-solid processing typically reduces material loss and improves productivity but generally requires a better understanding and control of the solidification of the cast material. Thermal analysis is commonly used in high-pressure die casting (HPDC) processes to determine casting process parameters, such as liquidus and solidus temperatures. However, this method is inadequate for semi-solid casting processes because the eutectic temperature is also a crucial parameter for successful semi-solid casting. This study explores the feasibility of using machine learning and artificial neural networks to predict fundamental values in Al-Si alloy casting. The Thermo-Calc 2022 software Scheil-Gulliver calculation function was used to generate the training and the test datasets, which included features such as melting temperature, alpha aluminium solidification temperature, eutectic temperature, and the solid fraction amounts at eutectic temperature. The results show that both models have a symmetric mean absolute percentage error (SMAPE) of less than 2 % with temperature prediction, with the machine learning model achieving a better accuracy of less than 1 %. A case study comparing practical measurements with prediction results is also discussed, demonstrating the potential of AI methods for predicting semi-solid casting processes.
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| 3. |
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| 4. |
- Li, Ziyu, et al.
(författare)
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Smart-Cast : An AI-Based System for Semisolid Casting Process Control
- 2024
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 232, s. 2440-2447
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Tidskriftsartikel (refereegranskat)abstract
- To satisfy the rising demand for higher product quality and giga-casting requirements, the casting process is undergoing significant changes. However, current control methods rely significantly on human expertise and experience, making process availability and stability difficult to ensure. The semisolid casting process is more complicated than conventional liquid casting due to the additional casting parameters incorporated during the slurry preparation, which can have an effect on the quality of the final product. Therefore, an efficient tool is required to simplify the complete process of semisolid casting. The introduction of an AI system to aid in the supervision of the casting manufacturing procedure is one potential solution. This paper introduces a new casting system named”Smart-Cast” developed for this specific purpose. The paper describes the functions of the system and its current development process. Using an AI system as an assistant can help to achieve the goal of enhancing the efficacy of casting process control, and it can also help foundries step into the Industry 4.0 era.
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| 5. |
- Sbarra, AN, et al.
(författare)
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Mapping routine measles vaccination in low- and middle-income countries
- 2021
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 589:7842, s. 415-
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Tidskriftsartikel (refereegranskat)abstract
- The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1–4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5–8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.
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| 6. |
- Silventoinen, K., et al.
(författare)
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The CODATwins Project : The current status and recent findings of COllaborative Project of Development of Anthropometrical Measures in Twins
- 2019
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Ingår i: Twin Research and Human Genetics. - : Cambridge University Press. - 1832-4274 .- 1839-2628. ; 22:6, s. 800-808
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Tidskriftsartikel (refereegranskat)abstract
- The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m2) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural-geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status.
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