查看更多>>摘要：Online monitoring of the curing temperature field is essential to improving the quality and efficiency of the manufacturing process of composite parts.Traditional embedded sensor-based technologies have difficulty moni-toring the full temperature field or have to introduce het-erogeneous items that could have an undesired impact on the part.In this paper,a non-contact,full-field monitoring method based on deep learning that predicts the internal temperature field of composite parts in real time using sur-face temperature measurements of auxiliary materials is pro-posed.Using the proposed method,an average temperature monitoring accuracy of 97％is achieved in various heating patterns.In addition,this method also demonstrates satisfy-ing feasibility when a stronger thermal barrier covers the part.This method was experimentally validated during the self-resistance electric heating process,in which the moni-toring accuracy reached 93.1％.This method can potentially be applied to automated manufacturing and process control in the composites industry.
查看更多>>摘要：In this study,a new method was developed to realize two-dimensional(2D)figure correction of grazing-incidence X-ray mirrors using a one-dimensional(1D)ion-beam figuring system.A mask of holes was specifically designed to generate removal functions at different widths and extend the figuring capability over a wide area.Accord-ingly,a long mirror could be manufactured.Using this method,the surface height root-mean-square(RMS)error of the center area of 484 mm ×16 mm was reduced from 11.49 nm to 2.01 nm,and the 1D meridional RMS error reached 1.0 nm.The proposed method exhibits high precision and cost effectiveness for production of long X-ray mirrors.
查看更多>>摘要：Production logistics(PL)is considered as a criti-cal factor that affects the efficiency and cost of production operations in discrete manufacturing systems.To effectively utilize manufacturing big data to improve PL efficiency and promote job shop floor economic benefits,this study proposes a PL trajectory analysis and optimization deci-sion making method driven by a manufacturing task data chain(MTDC).First,the manufacturing task chain(MTC)is defined to characterize the discrete production process of a product.To handle manufacturing big data,the MTC data paradigm is designed,and the MTDC is established.Then,the logistics trajectory model is presented,where the various types of logistics trajectories are extracted using the MTC as the search engine for the MTDC.Based on this,a logistics efficiency evaluation indicator system is proposed to sup-port the optimization decision making for the PL.Finally,a case study is applied to verify the proposed method,and the method determines the PL optimization decisions for PL efficiency without changing the layout and workshop equipment,which can assist managers in implementing the optimization decisions.
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