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2025年巴黎高科 - CSC合作公派读博项目 - 课题No.37,38
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2025年巴黎高科 - CSC合作公派读博项目 - 课题No.37,38 2025 巴黎高科 - CSC公派博士项目 （12月8日截止） 网申通道：https://paristech.kosmopolead.com/phd 申请攻略：https://paristech.fr/fr/paristech-csc-phd-program-how-apply 课题37,38详情: TITLE: PHYSICAL MODELING OF LOW-PRESSURE CASTING OF LIGHT ALLOYS IN 3D PRINTED MOLDS AND INDUSTRIAL SCALE EXPERIMENTAL VALIDATION Topic number : 2024_037 Field : Energy, Processes - Material science, Mechanics and Fluids Subfield: ParisTech School: Arts et Métiers Research team : Research team website: Research lab: MSMP - Laboratoire Mécanique, Surface, Matériaux et Procédés Lab location: Aix-en-Provence Lab website: msmp.eu Contact point for this topic: nan KANG, nan.kang@ensam.eu Advisor 1: Mohamed El Mansori - mohamed.elmansori@ensam.eu Advisor 2: Advisor 3: Advisor 4: Short description of possible research topics for a PhD: Low Pressure Casting (LPC) is widely used due to its high automation and precise control of casting parameters, producing high-quality parts. While typically used with metallic molds, LPC’s application with sand molds enhances the quality of engine blocks. To ensure defect-free castings, controlling the dynamics of filling and solidification is crucial. Poor filling can cause oxide formation or misrun, and incorrect solidification can lead to shrinkage porosity. In LPC, the flow is managed through pressure control, and factors such as casting temperature, alloy properties, part geometry, and pressure settings directly influence flow quality. During solidification, applying overpressure allows liquid metal to feed the part, preventing porosity. 3D printing of molds enables optimized designs for filling systems and risers, although careful consideration is needed due to material anisotropy. Combining simulations with industrial-scale experiments can improve process optimization, ensuring better casting quality. The MSMP lab is seeking a PhD candidate to model and experimentally validate LPC’s filling and solidification dynamics, aiming to develop optimized design rules for filling systems and risers. The work will involve new case studies, real-time and post-mortem characterization, and numerical modeling using commercial software. Required background of the student: A list of (5 max.) representative publications of the group: (Related to the research topic) 1. Bonollo F, Urban J, Bonatto B, Botter M. Gravity and low pressure die casting of aluminium alloys: a technical and economical benchmark. La Metallurgia Italiana [Internet]. 3 juin 2005 [cité 16 mai 2024]; Disponible sur: https://www.fracturae.com/index.php/aim/article/view/610 2. dler FJ, Lagrené G, Siepe R. Thin-walled Mg Structural Parts by a Low- pressure Sand Casting Process. In: Magnesium Alloys and their Applications [Internet]. John Wiley & Sons, Ltd; 2000 [cité 16 mai 2024]. p. 553-7. Disponible sur: https://onlinelibrary.wiley.com/doi/abs/10.1002/3527607552.ch87 3. Bedel M, Sanitas A, El Mansori M. Geometrical effects on filling dynamics in low pressure casting of light alloys. Journal of Manufacturing Processes. 1 sept 2019;45:194-207. 4. Sanitas A, Coniglio N, bedel M, El Mansori M. Investigating surface roughness of ZE41 magnesium alloy cast by low-pressure sand casting process. International Journal of Advanced Manufacturing Technology. 2017;92(5-8):1883-91. 5. Bedel M, Fabre A, Coniglio N. Defining the printing direction impact of additively manufactured sand molds on casting roughness. Journal of Manufacturing Processes. 30 avr 2024;116:329-40. TITLE: GENERATIVE AI FOR COMPLEX MECHANICAL SYSTEMS DESIGN – TECHNICAL AND EDUCATIONAL PERSPECTIVES Topic number : 2024_038 Field : Design, Industrialization - Mathematics and their applications - Information and Communication Science and Technology Subfield: ParisTech School: Arts et Métiers Research team : Research team website: Research lab: LCFC - Laboratoire de conception, fabrication, commande Lab location: Metz Lab website: https://lcfc.ensam.eu/ Contact point for this topic: HOMRI - Lazhar - lazhar.homri@ensam.eu Advisor 1: Lazhar HOMRI - lazhar.homri@ensam.eu Advisor 2: Advisor 3: Advisor 4: Short description of possible research topics for a PhD: The effectiveness of artificial intelligence (AI) technology heavily depends on the availability of high-quality and extensive training data effective design of complex systems. To Overcome this issue, generative AI has greatly enhanced the production of digital content and has recently had a significant impact on the design activities. For designers, this means access to a tool that can produce innovative and optimized designs with speed and complexity that is unachievable by human efforts or experiences. Various tools and researches started working on the benefits of generative AI, mainly models based on generative adversarial networks (GANs) and various deep learning networks for achieving automated generative design and considering engineering performances. Input prompts can help to generate design concepts automatically and to meet with design constraints, without spending considerable time and effort. Generative AI is considered to be more user-friendly for designers without programming training. The objective of this thesis project is to develop a practical methodology for integrating the generative AI into the design process of complex systems, with a focus on predicting their behavior considering engineering and education perspectives. By taking advantage of AI-derived knowledge, this methodology aims to streamline decision-making processes, optimize system performance and promote the creation of more innovative, efficient and sustainable design solutions in complex engineering environments. Required background of the student: Mechanical/ industrial Engineering (product design) Mathematics, Computer science, interest for eductional framwork A list of (5 max.) representative publications of the group: (Related to the research topic) 1. Li, Y., Li, Y., Yan, W., Yang, F., and Ding, X. (2024) Advancing Design With Generative AI: A Case of Automotive Design Process Transformation, in Gray, C., Ciliotta Chehade, E., Hekkert, P., Forlano, L., Ciuccarelli, P., Lloyd, P. (eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.1260. 2. Lu, P., Hsiao, S. W., Tang, J., & Wu, F. (2024). A generative-AI-based design methodology for car frontal forms design. Advanced Engineering Informatics, 62, 102835. 3. Zouhri, W., Homri, L., & Dantan, J. Y. (2022). Identification of the key manufacturing parameters impacting the prediction accuracy of support vector machine (SVM) model for quality assessment. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(1), 177-196. 4. Bartlett, K. A., & Camba, J. D. (2024). Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics. 5. Gupta, P., Ding, B., Guan, C., & Ding, D. (2024). Generative AI: A systematic review using topic modelling techniques. Data and Information Management, 100066. |
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