# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 3 Progress of Information Fusion for Multi-Source Material Data
### Data Collection and Processing of Material Data  
According to the general process of material data fusions shown in Figure 1, we first need to define the various data items for materials. Take the aluminum matrix composite material as an example; when designing the material, it is necessary to design the particle shape, size, orientation and the distribution  
Figure 1: General process of multi-source material data fusion.  
statistics of the composite material. The material preparation needs to consider the diameter, height and weight of the material. It needs to be accurate to describe the interfacial tensile strength of aluminum matrix composites when describing the material characterization. The material processing stage needs to consider the maximum diameter, thickness and maximum rolling thickness of the forging. The LIMS (Laboratory Information Management System) [11; 12] technology can be used for collecting material data automatically, analyzing data and material data management in many fields.  
Next, we need to run simulation software to get simulation data. At present, there are some simulation frameworks [13] for material properties, which can ensure the normal operations of the workflow. Material data are produced in a variety of ways, such as mechanical properties--tensile tests measure elastic modulus, yield strength, tensile strength, elongation after fracture, breaking strength, maximum force non-proportional elongation, maximum force total elongation rate and the total elongation of the fracture. Considering the multi-source heterogeneity of material data, data fusion techniques based on majority voting strategies [14; 15] are widely used, and source weight can be computed to make the fusion result closer to the data from more reliable sources [16].  
In addition, machine-learning models including SVM (Support Vector Machines), nearest neighbors and other machine learning methods, can also be used to discover the relationship between material data. The application of thermal barrier coating materials in aerospace applications is very important. The factors affecting the performance of thermal barrier coatings are currently unclear, and the ways to optimize the process of improving performance still remains unrevealed. Thus, it is necessary to quantify the intrinsic relationship between all factors and performance, and to use machine learning to obtain the optimal solution [17], so that the bonding strength of the thermal barrier coating on the turbine guide vanes of domestic aviation engines can be increased. When analyzing the high-precision image data of ceramic complex microstructures, an image is segmented based on complex network and deep learning. The main steps are to establish the relationship between the surface image layer structure, feature extraction, texture analysis, judgment of microstructure and performance. With the ability to simultaneously describe multivariate microstructure factors and complex process parameters (such as the melting factor model), we can indirectly establish the quantitative relationship between process and microstructure, and provide scientific guidance for coating performance improvement. At the same time, first-principles as well as finite element calculations [18] are used to optimize the composition and structure of the material. Some examples are: Establishing multiple regression equations, performing a significance test, removing insignificant variables and quantifying the magnitude of each influencing factor. Figure 2 shows how material data are collected and stored into databases.  
Figure 2: Material data collection and storage process.