Analysis of the Coherence of Published Data on Aluminum Matrix Composites

Abstract

Abstract Data on aluminum matrix composites were previously collected from scientific articles and analyzed. The purpose was to make it possible to select composite materials. During data analysis, incomplete representation of the information was discovered in many cases. Since this situation is not uncommon for advanced materials, it was decided to characterize the information that was missing. Important information has been divided into three main groups: mandatory, essential, and recommended meta data, where the meta data is the background information to actual data. Missing mandatory meta data make it impossible to verify the results by repeating the testing, e.g., when the composition of the matrix, the type and amount of reinforcement or the heat treatment is missing. The percentage of the 93 studied articles with such data missing was about 20%. If there is essential meta data missing, the precision in the interpretation of property data is significantly limited. Examples of essential information are processing steps, reinforcement length, and size. In about 60% of the articles some essential meta data were missing. Recommended meta data are, for example, description of the microstructure or other background information such as reinforcement distribution. The fraction of articles missing some recommended meta data is almost 100%. The lack of data leads to difficulties interpreting and modeling experimental data which will ultimately slow large-scale introduction of aluminum matrix composites.