A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing

Abstract

With the advancement of additive manufacturing (AM), or 3D printing technology, manufacturing industries are driving towards Industry 4.0 for dynamic changed in customer experience, data-driven smart systems, and optimized production processes. This has pushed substantial innovation in cyber-physical systems (CPS) through the integration of sensors, Internet-of-things (IoT), cloud computing, and data analytics leading to the process of digitization. However, computer-aided design (CAD) is used to generate G codes for different process parameters to input to the 3D printer. To automate the whole process, in this study, a customer-driven CPS framework is developed to utilize customer requirement data directly from the website. A cloud platform, Microsoft Azure, is used to send that data to the fused diffusion modelling (FDM)-based 3D printer for the automatic printing process. A machine learning algorithm, the multi-layer perceptron (MLP) neural network model, has been utilized for optimizing the process parameters in the cloud. For cloud-to-machine interaction, a Raspberry Pi is used to get access from the Azure IoT hub and machine learning studio, where the generated algorithm is automatically evaluated and determines the most suitable value. Moreover, the CPS system is used to improve product quality through the synchronization of CAD model inputs from the cloud platform. Therefore, the customer’s desired product will be available with minimum waste, less human monitoring, and less human interaction. The system contributes to the insight of developing a cloud-based digitized, automatic, remote system merging Industry 4.0 technologies to bring flexibility, agility, and automation to AM processes.

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

“…Therefore, information connectivity has evolved from operators as the information carriers to manufacturing equipment connected to computers and networked equipment and computers, as illustrated in Figure 29 a [ 241 ]. Therefore, the digital manufacturing has progressed significantly due to the advancement of the machine-to-machine (M2M) communication [ 242 ] and machine-to-human (M2H) interaction over the cloud platform, making the process autonomous and data driven over the internet [ 243 ]. Moreover, I4.0 technologies have transformed both the AM and SM processes in terms of real-time operation management, data collection and analysis [ 244 ].…”

Time Series Prediction in Industry 4.0: A Comprehensive Review and Prospects for Future Advancements

“…Cloud Computing: Cloud-based services offer scalable, on-demand access to computing resources and storage. Manufacturing companies leverage cloud platforms to store and process vast amounts of data generated from IoT devices, allowing them to analyze data [17] more efficiently and implement collaborative processes with stakeholders across different locations [1,6,18].…”

“…Additionally, the COVID-19 pandemic has accelerated the adoption of Industry 4.0 technologies in pandemic management scenarios. This chapter presents a comprehensive review of scientific papers that highlight the applications of artificial intelligence, machine learning, IoT, cloud computing, time series analysis [18,[31][32][33][34], and predictive maintenance [35] in the context of Industry 4.0. The papers explore innovative methodologies, algorithms, and solutions that contribute to enhancing efficiency, optimizing processes, and addressing challenges in modern manufacturing.…”