A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing
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-physica...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Automation |
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| Online Access: | https://www.mdpi.com/2673-4052/3/3/21 |
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| author | M. Azizur Rahman Md Shihab Shakur Md. Sharjil Ahamed Shazid Hasan Asif Adnan Rashid Md Ariful Islam Md. Sabit Shahriar Haque Afzaal Ahmed |
| author_facet | M. Azizur Rahman Md Shihab Shakur Md. Sharjil Ahamed Shazid Hasan Asif Adnan Rashid Md Ariful Islam Md. Sabit Shahriar Haque Afzaal Ahmed |
| author_sort | M. Azizur Rahman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:44:40Z |
| format | Article |
| id | doaj.art-f49dbde234264fa0951c706783f7d2e3 |
| institution | Directory Open Access Journal |
| issn | 2673-4052 |
| language | English |
| last_indexed | 2024-03-10T00:44:40Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Automation |
| spelling | doaj.art-f49dbde234264fa0951c706783f7d2e32023-11-23T15:01:40ZengMDPI AGAutomation2673-40522022-08-013340042510.3390/automation3030021A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive ManufacturingM. Azizur Rahman0Md Shihab Shakur1Md. Sharjil Ahamed2Shazid Hasan3Asif Adnan Rashid4Md Ariful Islam5Md. Sabit Shahriar Haque6Afzaal Ahmed7Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Industrial and Production Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, BangladeshDepartment of Mechanical and Production Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Mechanical and Production Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Mechanical and Production Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Mechanical and Production Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Mechanical and Production Engineering, Ahsanullah University of Science and Technology (AUST), Dhaka 1208, BangladeshDepartment of Mechanical Engineering, IIT Palakkad, Palakkad 678557, Kerala, IndiaWith 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.https://www.mdpi.com/2673-4052/3/3/213D printingmachine learningfused deposition modeling (FDM)digital manufacturingweb-based system |
| spellingShingle | M. Azizur Rahman Md Shihab Shakur Md. Sharjil Ahamed Shazid Hasan Asif Adnan Rashid Md Ariful Islam Md. Sabit Shahriar Haque Afzaal Ahmed A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing Automation 3D printing machine learning fused deposition modeling (FDM) digital manufacturing web-based system |
| title | A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing |
| title_full | A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing |
| title_fullStr | A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing |
| title_full_unstemmed | A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing |
| title_short | A Cloud-Based Cyber-Physical System with Industry 4.0: Remote and Digitized Additive Manufacturing |
| title_sort | cloud based cyber physical system with industry 4 0 remote and digitized additive manufacturing |
| topic | 3D printing machine learning fused deposition modeling (FDM) digital manufacturing web-based system |
| url | https://www.mdpi.com/2673-4052/3/3/21 |
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