Finite Element Analysis and Machine Learning Guided Design of Carbon Fiber Organosheet-based Battery Enclosures for Crashworthiness

Carbon fiber composite can be a potential candidate for replacing metal-based battery enclosures of current electric vehicles (E.V.s) owing to its better strength-to-weight ratio and corrosion resistance. However, the strength of carbon fiber-based structures depends on several parameters that should be carefully chosen. In this work, we implemented high throughput finite element analysis (FEA) based thermoforming simulation to virtually manufacture the battery enclosure using different design and processing parameters. Subsequently, we performed virtual crash simulations to mimic a side pole crash to evaluate the crashworthiness of the battery enclosures. This high throughput crash simulation dataset was utilized to build predictive models to understand the crashworthiness of an unknown set. Our machine learning (ML) models showed excellent performance (R2 > 0.97) in predicting the crashworthiness metrics, i.e., crush load efficiency, absorbed energy, intrusion, and maximum deceleration during a crash. We believe that this FEA-ML work framework will be helpful in down select process parameters for carbon fiber-based component design and can be transferrable to other manufacturing technologies

A conceptual framework for determining acceptance of Internet of Things (IoT) in higher education institutions of Pakistan

The IoT is the latest innovation and increasingly growing area to be implemented in all areas of life especially in higher education. This has created new excitement and challenges for academicians, this study will focus on IoT adoption and acceptance in higher educational institutes of Pakistan. The way IoT in learning environments supports educators can influence how we collaborate, communicate and operate. In this research, we focus on two aspects to investigate here. Firstly, how students are taught and; secondly, how educational institutes can bring in IoT to improve learning. This study explores the important aspect which influence the IoT acceptance and usage in an academic setting with in higher education institutes of Pakistan. Current study lays foundation for a comprehensive framework using trusted models of technology and scoial psychology like Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). This research proposes the network analysis approach to observe the users' behaviors towards different IoT applications usage in higher education. The considerations of key deliverable that have significant effect on the acceptance of IoT in higher education institutions of Pakistan and the in-depth analysis showed that few applications are being used heavily compared to all other applications.This study provides basis for developing countries to increase wider acceptance and use of IoT technologies in higher education to provide benefits for both students' and faculty members