Optimization of highly automated production line: An advanced engineering educational approach

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As industry attempts to integrate the new technologies of the digitalized era in its current approaches, the re-structuring of the production is imperative. Taking into consideration the plethora of alternatives that are revealed through the Industry 4.0 technologies, it is perceivable that the decision-making procedure has become highly complex. In order to support the decision-making process, software platforms that can analyze the data to provide insights via the investigation of different configurations in a manufacturing system are used. Low cost, low risk and quick data analysis are some of the benefits that render simulation an appealing choice to examine various scenarios for the production line. The industry is well aware of the limitations and challenges of the current production practice. However, the lack of expertise and experience on the emerging technologies and applications makes them hesitant to adopt them in the current practice. In contrast, academia has increased knowledge on the technical aspects including the testing of a wider variety of tools and methodologies, but usually in laboratory environment. Nowadays, considering the rapid technological evolution, the gap between academic and industrial practice broadens over time. In this research work, collaboration under an advanced engineering educational approach is proposed, where the technical knowledge of the university is transferred to a highly automated production line for the manufacturing of thermosiphon systems. This collaboration is beneficial for both parties, providing the industry with the capability to solve modern problems by integrating digital technologies while providing the academia with valuable experience on a real industrial problem. The proposed approach includes remote communication between the two parties, data and knowledge exchange, aiming to re-structure the production line including an additional robot. To validate the proposed design, the productivity of the proposed scenarios is compared with the current throughput.




Mourtzis, D., Tsakalos, D., Xanthi, F., & Zogopoulos, V. (2019). Optimization of highly automated production line: An advanced engineering educational approach. In Procedia Manufacturing (Vol. 31, pp. 45–51). Elsevier B.V. https://doi.org/10.1016/j.promfg.2019.03.008

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