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The Internet of Things (IoT) has a significant effect on the development of manufacturing technology. Therefore, according to the analysis of the challenges and opportunities faced by manufacturing industry, this study uses the assembly process of mechanical products as the research object and analyzes the characteristics of IoT-based manufacturing systems. To improve the interconnection, perception, efficiency, and intelligence of the assembly system, this study proposes the concept of IoT-enabled intelligent assembly system for mechanical products (IIASMP). The IIASMP framework, which is based on advanced techniques such as information and communication technology, sensor network, and radio-frequency identification, is then presented. Key technologies under this framework, including assembly resources identification, information interaction technology, multi-source data perception and fusion, intelligent assembly agent, and value-added data and dynamic self-adaptive optimization, are described. Finally, the current results of IIASMP are described in the case study. The proposed framework and methods aims to have an important reference value for applying the key technologies and be used widely in the intelligent manufacturing field.

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Tab.1 A selected summary of existing energy performance certification studies in various energy-intensive industries from literature Type Industry Research method Specific research object Sources Direct correlation with energy performance certification Building industry Strategic energy review Framework of building energy certification Pe rez-Lombard [64] Comparative analysis Energy certification of buildings Andaloro [65] Embodied energy calculations and live cycle analysis Building energy regulation and certification in Europe Casals [66] Means of Artificial Neural Networks Tool for checking energy performance and certification Buratti [67] Comparative analysis Building energy efficiency certification system Park [68] Geostatistical approach and data-mining technique Energy performance certificates for existing buildings Koo [58] Indirect correlation with energy performance certification Bio-chemical industry Analysis of criteria and indicators Certification on sustainable biomass trade Lewandowski [69] Petrochemical industry Strategic energy review Energy benchmarking of petrochemical application Rikhtegar [70] Mathematical modelling Performance rating system Rahdari [71] Analysis and review oil shale energy rating Koitmets [72] Steel and cement industry Analysis Integrated benchmarking and energy savings tool Worrell [73] -Energy benchmarking of cement grinding Zeng [74] Coal mine industry Experimental analysis Classification and labelling Skeaff [75] Mathematical modeling Energy efficiency benchmarking system Wang [76] Paper industry Analysis Benchmarking energy use on process unit level Laurijssen [77] Environmental protection industry Life cycle assessment Energy and environmental rating of advanced glazing Papaefthimiou [78] Statistical analysis Energy benchmarking of WWTPs Krampe [79] Agricultural and food industry Statistical analysis Certification of food products Ortega [80] Analysis and field survey Energy utilization of main crop straw resource Ming [81] Manufacturing industry Analysis and review Energy training and certification Glatt [82] Analysis and review Energy labeling for electric fans Mahlia [83] Analysis and modeling Energy benchmarking rules in machining systems Cai [84] Modeling and Statistical analysis Dynamic energy benchmark for mass production Cai [85] Others System modelling Industrial energy benchmarking Ke [86] Comparison of methods and approach Energy rating of PV modules Kenny [87] Statistical analysis China energy label. Zhou [88]

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