Analysis and Monitoring of Design Challenges in the Cyber Physical Systems in Manufacturing Sectors Using Cloud Computing

Abstract

The integration of additive manufacturing (AM) technology, commonly known as 3D printing, into industrial processes has led to the adoption of Industry 4.0 principles. This includes the integration of dynamic changes in the user experience, data-driven smart systems, and simpler production processes. In this research, a consumer-driven cyber-physical systems (CPS) framework is proposed, utilizing customer demand data to automate the entire process. The use of cloud computing, specifically Microsoft Azure, is utilized to optimize the process parameters and improve the efficiency of the 3D printing process using fused deposition modeling (FDM) technology. Additionally, machine learning techniques, specifically the multi-layer perceptron (MLP) neural network model, are implemented to further optimize the process parameters. A raspberry pi is used to connect to the Azure IoT hub and machine learning studio, where the algorithm is automatically reviewed, and the best value is selected.