Data-driven cyber-physical system framework for connected resistance spot welding weldability certification. (February 2021) - Ahmed, Fahim; Jannat, Noor-E; Schmidt, Daniel; Kim, Kyoung-Yun
Highlights: Realized data-driven cyber-physical system for RSW certification with integrated analytics and optimization capabilities. Integrating data from analytics lifecycle phases - data collection operation to predictive analytics to design visualization. The framework is based on conceptualization of layers of cyber-physical system and incorporates design and machine changes. Closed-loop machine parameter optimization implemented considering the target product design. Case study based on a real industrial weldability certification to illustrate the application of the envisioned framework. Abstract: A cyber-physical system is one of the integral parts of the development endeavor of the smart manufacturing domain and the Industry 4.0 wave. With the advances in data analytics, smart manufacturing is gradually transforming the global manufacturing landscape. In the Resistance Spot Welding (RSW) domain, the focus has been more on the physical systems, compared to the virtual systems. The cyber-physical system facilitates the integrated analysis of the design and manufacturing processes by converging the physical and virtual stages to improve product quality in real-time. However, a cyber-physical system integrated RSW weldability certification is still an unmet need. This research is to realize a real-time data-driven cyber-physical system framework with integrated analytics and parameter optimization capabilities for connected RSW weldability certification. The framework is based on the conceptualization of the layers of the cyber-physical system and can incorporate the design and machine changes. It integrates data from the analytics lifecycle phases, starting from the data collection operation, to the predictive analytics operation, and to the visualization of the design. This integrated framework aims to support decision-makers to understand product design and its manufacturing implications. In addition to data analytics, the proposed framework implements a closed-loop machine parameter optimization considering the target product design. The framework visualizes the target product assembly with predicted response parameters along with displaying the process parameters and material design parameters simultaneously. This layer should help the designers in their decision-making process and the engineers to gain knowledge about the manufacturing processes. A case study on the basis of a real industrial case and data is presented in detail to illustrate the application of the envisioned cyber-physical systems framework.