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With the development of advanced sensing and network technology such as wireless data transmission and data storage and analytics under cloud platforms, the manufacturing plant is going through a new revolution, by which different production units/components can communicate with each other, leading to inter-connected manufacturing. The interconnection enables the close coordination of process control actions among machines to improve product quality. Traditional quality prediction methods that focus on the data from one single source are not sufficient to deal with the variation modeling, and quality prediction problems involved the inter-connected manufacturing. Instead, new quality prediction methods that can integrate the data from multiple sources are necessary. This research addresses the fundamental challenges in improving quality prediction by data fusion for inter-connected manufacturing including knowledge sharing and transfer among different machines and collaboration error monitoring. The methodology is demonstrated through surface machining and additive manufacturing processes. The first study is on the surface quality prediction for one machining process by fusing multi-resolution spatial data measured from multiple surfaces or different surface machining processes. The surface variation is decomposed into a global trend part that characterizes the spatially varying relationship of selected process variables and surface height and a zero-mean spatial Gaussian process part. Three models including two varying coefficient-based spatial models and an inference rule-based spatial model are proposed and compared. Also, transfer learning technique is used to help train the model via transferring useful information from a data-rich surface to a data-lacking surface, which demonstrates the advantage of inter-connected manufacturing. The second study deals with the surface mating errors caused by the surface variations from two inter-connected surface machining processes. A model aggregating data from two surfaces is proposed to predict the leak areas for surface assembly. By using the measurements of leak areas and the profiles of surfaces mated as training data along with Hagen–Poiseuille law, this study develops a novel diagnostic method to predict potential leak areas (leakage paths). The effectiveness and robustness of the proposed method are verified by an experiment and a simulation study. The approach provides practical guidance for the subsequent assembly process as well as troubleshooting in manufacturing processes. The last study focuses on the learning of quality prediction model in inter-connected additive manufacturing systems, by which different 3D printing processes involved are driven by similar printing mechanisms and can exchange quality data via a network. A quality prediction model that estimates the printing widths along the printing paths for material-extrusion-based additive manufacturing (a.k.a., fused filament fabrication or fused deposition modeling) is established by leveraging the between-printer quality data. The established mathematical model quantifies the printing line-width along the printing paths based on the kinematic parameters, e.g., printing speed and acceleration while considering data from multiple printers that contain between-machines similarity. The method can allow for the between-printer knowledge sharing to improve the quality prediction so that a printing process with limited historical data can quickly learn an effective quality model without intensive re-training, thus improving the system responsiveness to product variety. In the long run, the outcome of this research can help contribute to the development of high-efficient Internet-of-Things manufacturing services for personalized products.