
A service matching method for the cloud manufacturing of paper gravure printing machine doctor blades based on improved K-means clustering is proposed. This approach is aimed at the problem of poor accuracy of both service clustering and supply and demand matching in cloud-based doctor blade manufacturing for paper gravure printing machines. First, based on the improved K-means clustering algorithm, doctor blade cloud manufacturing services are clustered to form a set of services with high similarity within groups and low similarity between groups. Second, the extension theory is used to establish a correlation function to select the doctor blade cloud manufacturing service set with the highest correlation degree with processing demand to form a candidate service set. Finally, the analytic hierarchy process and grey relational analysis are used to select the best cloud manufacturing service based on the subjective demand preference of users to achieve the matching purpose. The experimental results demonstrate that the accuracy of this method in solving the manufacturing service problem of gravure printing machine doctor blades can exceed 90% in approximately 30 min.

Reflectance Transformation Imaging (RTI) is a technique that provides an enhanced visualization experience. The current acquisition methods for Reflectance Transformation Imaging (RTI) are time consuming and computationally expensive. This work investigates the idea of getting best light positions for RTI acquisition using surface topography. We propose automating the RTI acquisition by estimating the surface topography using deep learning method followed by estimating light positions using unsupervised clustering method. This is one shot method which only needs one image. We also created RTI Synthetic dataset in order to carry out experiments. We found that surface topography alone is not sufficient to estimate best light positions for RTI without putting constraints.