Product Lifecycle Management Michael Grieves Pdf Download |TOP|

Product end-of-lifeDigital twins can even help manufacturers decide what to do with products that reach the end of their product lifecycle and need to receive final processing, through recycling or other measures. By using digital twins, they can determine which product materials can be harvested.

The data encoded in each flavor of the digital twin allows the engineer to perform various analyses corresponding to each digital twin representation. As described earlier, Level 1 and Level 2 (CAD/CAE/CAM) are already being used by engineering and design teams at major OEMs. In the Level 3 representation, the data model and sensor data are employed to perform regression, anomaly detection, and other data driven studies. In Level 3, the studies are primarily statistical in nature with seldom any input or insight provided from the design of the asset. The Level 4 representation provides the engineer with an ability to perform a variety of insightful analyses, primarily because this representation is augmented with physics-based models and with knowledge gained from an expert system. This knowledge could be provided by service engineers who have learned certain asset behaviors not necessarily encoded in statistical or physics-based models, and may consist of region-specific or context-specific insights. This expert system is extremely useful in failure mode analyses or decision trees to track down issues with assets. For instance, a pump service engineer might have noticed over time that tightening a few bolts or leveling the pump addresses overheating issues. This human knowledge can be very valuable to encode in a digital twin of the system that consists of the pump. Using this representation, one can perform system-level performance simulations, study what-if scenarios and perform system level optimization. Level 5 is the ideal target state of a complete digital twin. In addition to the Level 4 analyses, Level 5 digital twins can be used to understand the impact of asset behavior and performance on supply chain and procurement, i.e. product lifecycle, and manufacturing lifecycle, through integration with systems that focus on enterprise resource planning, manufacturing execution, Supervisory Control and Data Acquisition (SCADA), customer relationship management, and so on.Visualization techniques

Dr. Parle has 20 years of experience in different industries. His core engineering experience is around CAD/CAM, CAE, SLM & PLM whereas his emerging technologies experience is around Industry 4.0, 3D Printing, Virtual Reality and Augmented Reality. He has published 40+ papers and delivered several technical talks across national and international forums. He is on board and committee member of several industrial and academic bodies. Dr. Parle has worked across the product lifecycle for industries like aerospace, automotive, oil & gas, biomedical and nuclear engineering. 2b1af7f3a8