Abstract:

Educational Implications of Industry 4.0: Organizations are making transformation toward Industry 4.0 based on innovation by Cyber-Physical System. Transformative technologies influencing this are, Machine-to-machine learning, Big-Data analysis, Cloud computing, Mobile internet, Autonomous vehicles, Advanced materials. A major shift is underway, and the key question for engineering educators is: are your students being properly prepared. This paper includes four recent case studies. Case Studies in Manufacturing Engineering: Predictive maintenance takes advantage of data-driven insights on the measurement of operating conditions. In an example with automobile, this could be measurement of oil viscosity or engine speed. These analytics could be bolstered with data from external factors such as outside air temperature or geo-location. Statistical modeling and forecasting tools calculate when repairs are required. Organizations, who adopt predictive maintenance strategies are able to better manage parts and labor costs. This example examines the predictive maintenance in typical elevator and escalator industry which calculates Elevator/Escalator Condition Index (ECI) based on original equipment reliability, projected average life cycle of key wear components. Case Study in Civil Engineering: Industry 4.0 in an Adaptively Controlled Rainwater Harvesting System. Recent advances in information technology are now, however, providing cost-effective opportunities to achieve better performance of conventional storm water infrastructure through a Continuous Monitoring and Adaptive Control (CMAC) approach. The primary objective of this study is to demonstrate that a CMAC approach can be applied to a conventional rainwater harvesting system in New York City to improve performance by minimizing discharge to the combined sewer during rainfall events, reducing water use for irrigation of local vegetation, and optimizing vegetation health. To achieve this objective, a hydrologic and hydraulic model was developed for a planned and designed rainwater harvesting system. Case Study in Biomedical Engineering: Growing heath care needs have seen the introduction of artificial intelligence and data science integrated to tele-sensing of vital signs. Sensory perception and model based decision making has helped the study of human gait to assist in balance. This case study involves impaired populations involving, stroke survivors, fall-prone elderly, vestibular loss sufferers, amputees using ambulatory suspension system. The case study demonstrates the use of sensory perception, data collection and the use of body support system such as Ambulatory Suspension System to decision making on balance. Case Study in Mechanical Engineering; This case study how multiphysics simulations and applications are being used to build essential skills in preparation for entry into an Industry 4.0 workforce. In a highly networked and collaborative human/machine cyberspace, some important competencies for engineering graduates include the ability to: (1) explore design options and results easily between suites of software, (2) predict and visualize performance of complex problems in the beginning phase of the design process. The paper describes how integrated project- and inquiry-based learning in the context of a simulation environment and across the curriculum is improving student readiness and transition into industry.

Keywords:

Industry 4.0, Engineering Education, Case Studies, Mechanical Manufacturing Biomedical, Civil Engineering.