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Direct Optimization of an Automotive Sheet Metal Part Using ANSYS
134-142Views:630Optimization of automotive parts nowadays is mainly used to design lightweight and cost-effective vehicle parts in order to improve the cost and efficiency. In this research, a sheet metal part was taken into consideration and optimized using direct optimization module in ANSYS to evaluate the process. An initial Finite Element Analysis (FEA) was done on the sheet metal part by adding forces and constraints in order to initiate direct optimization. The purpose of the optimization is to minimize the mass of the sheet metal part and maintaining a certain Factor of Safety (FOS) by automatically modifying the sheet thickness and the dimension of the side holes. As a result, the best candidate point with 23% mass reduction was found which complied with FOS value was selected for optimal geometry.
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Innovative Strategies and Student Academic Performance: Machine Learning Insights on International Students in Chinese Universities
37-60Views:211The higher education sector in China has faced unprecedented challenges recently due to the global COVID-19 pandemic. The influx of international students, a vital component of the nation's academic landscape, presented distinct challenges, including maintaining academic achievements through various online platforms, which necessitated innovative strategies to ensure that their educational pursuits remained rewarding despite these challenges. This study aims to explore the innovative strategies adopted by Chinese higher education institutions in response to the COVID-19 pandemic and examine their impact on the academic achievements of international students. This study employs a comprehensive approach that incorporates questionnaire surveys and dominant Machine Learning Algorithms, such as Multiple Linear Regression (MLR), Decision Tree Model (DTM), Support Vector Regression Model (SVRM), and K-nearest neighbors (KNN). By employing rigorous data-gathering approaches, our study aimed to address a set of particular questions: How did these innovative strategies improve students' academic performance in the face of environmental emergencies? To what extent did international students benefit from these adaptations? Through investigation of these concerns, our research provides insight into the effectiveness of these strategies and their possible significance for future educational methodologies. Innovative strategies positively correlated with student academic performance during the COVID-19 pandemic in Chinese higher Education. This research highlights how overcoming these challenges can have broader implications for shaping resilient global education systems in future crises. The study accurately predicted academic performance, highlighting the importance of innovative teaching approaches in the context of the COVID-19 pandemic. This study might influence educational policies and practices. Educational institutions can make informed decisions about emergency preparedness and development by assessing results using a creative approach. Our findings bring depth to the global conversation on higher Education under challenging circumstances, showing how Innovation might alleviate the adverse impacts on international students' learning experiences.
