| Affiliation: | 1. Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China;2. School of Software, Shandong University, Jinan 250101, China;3. Qingdao Air Traffic Management Station of Civil Aviation of China, Qingdao 266100, China;4. School of Mechanical Engineering, Shandong University, Jinan 250061, China;1. Department of Construction Management, Louisiana State University, Baton Rouge 70803, USA;2. Department of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge 70803, USA;1. College of Engineering, China Agricultural University, No.17 Tsinghua East Road, Haidian District, Beijing 100083, China;2. Key Laboratory of Optimal Design of Modern Agricultural Equipment, College of Engineering, China Agricultural University, No.17 Tsinghua East Road, Haidian District, Beijing 100083, China;1. College of Engineering and Technology, Southwest University, Chongqing 400715, China;2. International R & D Center for New Technologies of Smart Grid and Equipment, Southwest University, Chongqing 400715, China;1. School of Mechanical Engineering, Shandong University, Jinan 250061, China;2. Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;3. Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China;4. School of Software, Shandong University, Jinan 250101, China |
| Abstract: | Smart manufacturing has great potential in the development of network collaboration, mass personalised customisation, sustainability and flexibility. Customised production can better meet the dynamic user needs, and network collaboration can significantly improve production efficiency. Industrial internet of things (IIoT) and artificial intelligence (AI) have penetrated the manufacturing environment, improving production efficiency and facilitating customised and collaborative production. However, these technologies are isolated and dispersed in the applications of machine design and manufacturing processes. It is a challenge to integrate AI and IIoT technologies based on the platform, to develop autonomous connect manufacturing machines (ACMMs), matching with smart manufacturing and to facilitate the smart manufacturing services (SMSs) from the overall product life cycle. This paper firstly proposes a three-terminal collaborative platform (TTCP) consisting of cloud servers, embedded controllers and mobile terminals to integrate AI and IIoT technologies for the ACMM design. Then, based on the ACMMs, a framework for SMS to generate more IIoT-driven and AI-enabled services is presented. Finally, as an illustrative case, a more autonomous engraving machine and a smart manufacturing scenario are designed through the above-mentioned method. This case implements basic engraving functions along with AI-enabled automatic detection of broken tool service for collaborative production, remote human-machine interface service for customised production and network collaboration, and energy consumption analysis service for production optimisation. The systematic method proposed can provide some inspirations for the manufacturing industry to generate SMSs and facilitate the optimisation production and customised and collaborative production. |
