## 4 Key Technologies of Multidiscipline Integrated Platform  
From the requirements of the multidisciplinary integrated platform, more issues should be widely recognized relating to the following key technologies:  
1. Monitoring technology from the industrial processes. Monitoring processes is the key to improving processes previously optimized by modeling. Monitoring would help to fine-tune the window parameters in high-added value production. However, if the real recorded values  
Figure 4: Architecture of a multidisciplinary integrated platform for manufacturing engineering processes. are not related to the exact point of the process in which damage, risk or a non-conformity may happen, useful information would be missed. ANN (artificial neural networks) can correlate any variable, therefore it is of paramount importance to define the experimental data and to use a sound method. A good instance is a duo of studies, the first providing the physical results [29], and the second providing the tool and procedure [30], as it is applied in [31]. As can be seen, almost 14 years passed from the gathering of experimental values to the current application of AI (artificial intelligence) tools. 2. Integration and encapsulation for probabilistic analysis models. The proposed platform includes related data and models for reliable manufacturing. Models of different disciplines should be integrated and encapsulated to Web services via XML (extensible markup language). Considering security, Web services could be shared with remote users with authorization, as shown in Figure 5.  
In order to assess the reliability and the risk for engineering materials and manufacturing engineering, probabilistic analysis models (such as a generative model, a discriminative model, a hidden Markov model or a maximum entropy model), should be established in the actual system. A user-defined model can also be integrated into probabilistic analysis models via a standard interface. If the cloud computing pattern is used in the platform, information sharing will become more convenient [32]. Remote users are able to use the required services without concern about the services' providers. Moreover, public cloud services can increasingly be obtained at a low price, or even for free, at present. (3) Information fusion approaches [33] for the multisource data. In the platform, significant quantities of data, including materials data and manufacturing data, are integrated. From the view of mathematical modeling, data are homogeneous or heterogeneous. Information fusion for homogeneous data, such as vibration data, acoustic emission data and temperature data, can be achieved using the Laplacian spectra analysis approach. Information fusion for heterogeneous data, such as vibration data and images, can be a perspective transformation. Finally, we can use the D-S (Dempster-Shafer) evidence theory to make decisions for reliable manufacturing. We should also consider analysis of the data and the problem using the compatible information approach. Figure 6 shows a multisource information fusion diagram for homogeneous signals and heterogeneous data. With the trend of big data analysis, more spatiotemporal models using data mining approaches can be introduced into the proposed platform. Figure 5: Schematic diagram of model encapsulation and sharing. 4. Technical description of platform implementation. In the proposed platform in this paper, it is necessary to integrate basic data, experimental data and industrial data. In order to ensure system compatibility and scalability, the system should adopt a language supported by cross-platform technology. The B/S (browser/service) mode is used for the end user, and is mainly used to support the dynamic expansion of the model. The main development languages of the platform are recommended as in Table 1.  
In addition, the scalability of the proposed platform is very important. The scalability of this platform includes the scalability of functional modules, algorithms and data.