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.
To improve the assembly accuracy of remanufactured parts with multiple heterogeneity and properties, and to guarantee the quality of remanufacturing product no worse than the original one, this paper takes the air tightness of remanufactured engines' cylinder block and head as the research target and proposes a quality control method based on the Jacobian-torsor model for remanufacturing assembly. First of all, three different Jacobian-torsor models for assembly tolerance are structured, and according to that, the defined torsor difference (ΔFR) of remanufacturing products is calculated by considering the geometric error of remanufactured related parts assembly influenced by the temperature field and force field. Then, the optimal tightening torque of cylinder heads' main bolts corresponding to ΔFR is achieved by applying the bivariate Lagrange interpolation method. Finally, taking the assembly of remanufactured engines' cylinder block and head as a case study to verify the proposed methods is feasible and effective.
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